CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63/401,206, filed August 26, 2022, the contents of which application are hereby incorporated by reference herein in their entireties.

BACKGROUND

[0002] Humans and other mammals exhibit three major vital signs — temperature, respiration, and pulse — that can readily convey whether a subject is alive. These and other vital signs are associated with vital bodily functions, and can be used to assess a state of health of a subject beyond a straightforward determination that the subject is alive.

[0003] Monitoring of vital signs of a subject is thus a ubiquitous tool in healthcare, and is commonly accomplished by tethering the subject to monitoring equipment. As such, commonplace monitoring of vital signs can be complicated and rather invasive, especially in situations where the subject is an infant, a young child, or an elder. Even if tethering monitoring equipment to a subject could be readily accomplished, there are situations where the monitoring of vital signs is necessary over extended periods of times, as it would be the case for subjects undergoing non-ambulatory care at a hospital or another type of medial facility. In those situations, monitoring the vital signs of a subject may be disrupted due to movement of the subject or other factors, such as discomfort or pain, that result in the subject becoming untethered to the monitoring equipment. Not only can disruptions yield unreliable data, but in a subject under intensive care, for example, disruptions in the monitoring of vital signs can be detrimental, if not harmful, to the subject.

[0004] Therefore, several technical challenges remain in the field of monitoring of vital signs and the technologies for such monitoring. Improved technologies that address those challenges are desirable.

SUMMARY

[0005] The disclosure recognizes and addresses the issue of monitoring vital signs of a subject. In an aspect, the disclosure provides a computer-implemented method. The computer- implemented method includes generating, using accelerometer signals, physiological signals for a subject. The accelerometer signals correspond to respective accelerometer channels of an accelerometer device integrated into a flexible solid medium that is configured to support a subject in one of a laydown position or a sitting position and further configured to deform under load by the subject. The computer-implemented method also includes determining, using the physiological signals, first datasets indicative of peaks corresponding to respective accelerometer channels; determining, using the physiological signals, second datasets indicative of troughs corresponding to respective accelerometer channels; determining, using the first datasets, first estimates of a vital sign of the subject; determining, using the second datasets, second estimates of the vital sign; determining, using the first estimates and the second estimates, an observed estimate of the vital sign; and providing the observed estimate of the vital sign.

[0006] Other aspects include another computer-implemented method. That other computer- implemented method includes generating, using accelerometer signals, physiological signals for a subject. The accelerometer signals correspond to respective accelerometer channels of an accelerometer device mechanically coupled with a subject via a solid medium. The method also includes determining, using the physiological signals, one or more of (i) first datasets indicative of peaks corresponding to respective accelerometer channels or (ii) second datasets indicative of troughs corresponding to respective accelerometer channels; determining, using one or more of (a) the first datasets or (b) the second datasets, multiple estimates of a vital sign of the subject; determining, using the multiple estimates, an observed estimate of the vital sign; and providing the observed estimate of the vital sign.

[0007] In another aspect, the disclosure provides a computing device. The computing device includes at least one processor; and at least one memory device storing processor-executable instructions that, in response to being executed by one or a combination of the at least one processor, cause the computing device to perform or facilitate one or both of the above-noted method.

[0008] Yet another aspect includes a computer-program product. The computer-program product includes one or more non-transitory computer-readable media having instructions stored thereon, where the instructions are executable by at least one processor, individually or in combination, to perform or facilitate one or both of the above-noted method. [0009] An additional aspect includes a system. The system includes a low-noise accelerometer device configured to generate measurement signals representative of motion of subject, where the motion corresponds to a vital bodily function of the subject. The accelerometer device is integrated into a solid medium configured to support the subject in one of a laydown position or a sitting position and is also configured to deform under load by the subject. The system also includes a computing device functionally coupled with the low-noise accelerometer device. The computing device includes at least one processor, and at least one memory device storing processor-executable instructions that, in response to being executed by one or a combination of the at least one processor, cause the computing device to perform or facilitate one or both of the above-noted methods.

[0010] Other aspects also include another system, comprising a low-noise accelerometer device configured to generate accelerometer signals representative of motion of subject, the motion corresponding to a vital bodily function of the subject; and a computing device functionally coupled with the low-noise accelerometer device. The computing device includes at least one processor; and at least one memory device storing processor-executable instructions that, in response to being executed by one or a combination of the at least one processor, cause the computing device to perform or facilitate one or both of the above-noted methods.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings form part of the disclosure and are incorporated into the subject specification. The drawings illustrate example aspects of the disclosure and, in conjunction with the following detailed description, serve to explain at least in part various principles, features, or aspects of the disclosure. Some aspects of the disclosure are described more fully below with reference to the accompanying drawings. However, various aspects of the disclosure can be implemented in many different forms and should not be construed as limited to the implementations set forth herein. Like numbers refer to like elements throughout.

[0012] FIG. 1 is a block diagram of an example of a system for contactless monitoring of vital signs, in accordance with one or more aspects of this disclosure.

[0013] FIG. 2A presents plots of examples of observed accelerometer signals for three axes of an accelerometer device that can be part of the system shown in FIG. 1, in accordance with one or more aspects of this disclosure. [0014] FIG. 2B presents an example of raw accelerometer signals and detrended accelerometer signals for a particular channel of an accelerometer device that can be part of the system shown in FIG. 1, in accordance with one or more aspects of this disclosure.

[0015] FIG. 3 is a plot of conditioned acceleration signals in a particular channel, in accordance with aspects described herein. The conditioned acceleration signals include a period of invalid signals.

[0016] FIG. 4 is an example of a user interface (UI) where heart rate and respiratory rate are presented as a function of time, in accordance with one or more aspects of this disclosure. The UI can be presented during the contactless monitoring of vital signs described herein.

[0017] FIG. 5 is a block diagram of a contactless monitoring unit that contains an example of a system for contactless monitoring of vital signs, in accordance with one or more aspects of this disclosure.

[0018] FIG. 6 a diagram of an example of a system for contactless monitoring of vital signs, in accordance with one or more aspects of this disclosure.

[0019] FIG. 7 is a block diagram of an example of a computing device that can provide various functionality of contactless monitoring of vital signs in accordance with one or more aspects of this disclosure.

[0020] FIG. 8 is a flowchart of an example of a method for contactless monitoring of vital signs, in accordance with one or more aspects of this disclosure.

DETAILED DESCRIPTION

[0021] The disclosure recognizes and addresses the issue of monitoring vital signs of a subject. Commonplace monitoring of vital signs involves tethering a subject to dedicated monitoring equipment. For that reason, commonplace monitoring of vital signs can be complicated and rather invasive, especially in situations where the subject being monitored is an infant, a young child, or an elder. Even if tethering monitoring equipment to the subject could be readily accomplished, there are situations where the monitoring of vital signs is necessary over extended periods of times, without interruption. In those situations, monitoring the vital signs of the subject may be disrupted due to movement of the subject or other factors, such as discomfort or pain, that result in the subject becoming untethered to the monitoring equipment. Besides yielding unreliable data, in some circumstances, disruptions in the monitoring of vital signs can be detrimental, if not altogether harmful, to the subject.

[0022] The technologies described herein provide systems, devices, computer-implemented methods, and computer program products that, individually or in combination, permit contactless monitoring of vital signs of a subject. As is used herein, the term “contactless monitoring” refers to monitoring that avoids tethering the subject in any way to monitoring equipment. Embodiments of the technologies described herein can monitor vital signs in contactless fashion by using acceleration signals corresponding to respective accelerometer channels of an accelerometer device. The accelerometer device is mechanically coupled with a subject via a solid medium, where the subject is being monitored in contactless fashion in accordance with aspects described herein. The solid medium couples the accelerometer device and the subject by propagating movement of the subject towards the accelerometer device. In some cases, the accelerometer device is integrated into the solid medium, which medium is configured to support the subject in laydown position and/or an upright sitting position, for example. In addition, or in some cases, the solid medium deforms under load from a subject — that is, the solid medium can be deformed essentially elastically by a weight of the subject. By being deformable, the solid medium permits propagating motion from the subject to the accelerometer device. Thus, the solid medium mechanically couples the subject and accelerometer device with one another. In some cases, depending on the vital sign being monitored (e.g., heart rate), the solid medium can be rigid and can support the accelerometer device instead of the subject. For example, the solid medium can be a frame of furniture configured to receive, and hold, another solid medium that deforms under load by the subject. The accelerometer device is attached to such a frame.

[0023] The accelerometer device also is functionally coupled to a computing device having one or more processors and other computing resources. A communication architecture permits the transport of accelerometer data, including the acceleration signals, from the accelerometer device to the computing device.

[0024] The computing device can operate on the acceleration signals in order to generate conditioned accelerometer signals. The conditioned accelerometer signals can be physiological signals corresponding to vital bodily functions associated with vital signs being monitored in the contactless fashion as is described herein. Without intending to be bound by theory or modeling, the conditioned accelerometer signals represent such physiological signals because the vital bodily functions being monitored involve motion of the body of the subject, and the motion can be monitored in contactless fashion using the accelerometer device coupled to the computing device. Such a motion does not result in substantive displacement of the subject on a surface of the solid medium. That is, the vital bodily functions involve movement of body parts (e.g., chest wall) and/or body portions (e.g., torso) that essentially maintain the subject at rest on the surface of the solid medium.

[0025] The physiological signals correspond to both respiration and cardiac function, which are vital bodily functions that exhibit cyclical characteristics. Hence, the computing device can generate first datasets indicative of peaks present in the physiological signals, where the first datasets correspond to respective accelerometer channels. Additionally, the computing device also can generate second datasets indicative of troughs present in the physiological signals, where the second datasets corresponding to respective accelerometer channels. Further, the computing device can determine, using the first datasets, first estimates of a vital sign of a subject, and also can determined, using the second datasets, second estimates of the vital sign. The vital sign can be respiratory rate or heart rate, for example.

[0026] Each of the first estimates and second estimates corresponds to a channel of the accelerometer device. Accordingly, the computing device can aggregate the first and second estimates to generate an observed estimate of the vital sign. The manner of aggregating the first and second estimates depends on the vital sign being monitored.

[0027] The computing device can then provide the observed estimate of the vital sign. In some cases, the computing device can provide the observed estimate of the vital sign by causing a display device to present the observed estimate of the vital sign in a user interface accessible to the subject and/or a healthcare provider of the subject.

[0028] Because the accelerometer device that is mechanically coupled to the subject can generate acceleration signals over time, at a defined sampling rate, the computing device can determine additional observed estimates of the vital sign over time, as the acceleration signals become available to the computing device. Thus, in sharp contrast to existing technologies that monitor vital signs, the technologies described herein provide estimates of a vital sign contemporaneously with the vital bodily function associated with the vital sign. Accordingly, the vital sign is monitored in contactless fashion and in real-time or nearly real-time, without resorting to averaging estimates of the vital sign of arbitrarily defined time periods. As a result, the contactless monitoring of vital signs that is described in this disclosure drastically improves the technological field of vital sign monitoring. Indeed, the technologies described herein avoid the use of dedicated and rather costly equipment, and also maintain a timely assessment of the vital signs of the subject being monitored.

[0029] Various aspects in accordance with this disclosure are described in connection with a bed, such as residential bed or a hospital bed, as furniture where the contactless monitoring of vital signs is implemented. The disclosure is not limited in that respect. Indeed, the principles and practical applications of the contactless monitoring of vital signals in accordance with aspects of this disclosure can be implemented in other types of furniture, include recliner chairs, sofas, cribs, cots, mats, blankets, cloaks, and the like. Indeed, the contactless monitoring of vital signs that is described herein can be implemented in any solid medium that is configured to support a subject in laydown position, and/or an upright position, or is configured to be disposed so as to contact a torso of the subject by supporting the subject or covering the subject. In this disclosure, a “subject” is a human being or another type of animal. Non-human animals can include dogs, cats, horses, chimpanzees and other non-human primates, and livestock (such as cows, pigs, sheep, and the like).

[0030] FIG. 1 is a block diagram of an example of a system 100 for non-contact monitoring of vital signs of a subject, in accordance with one or more aspects of this disclosure. The example system 100 includes a bed 104 where a subject (not depicted) can he down or sit upright to be monitored. It is noted that in situations where the subject is sitting upright on the bed 104, a particular type of vital sign may not be monitored as long as the subject remains in such a position. The cyclic motion involved in breathing and circulation of blood within the body of the subject causes a time-dependent deformation of a solid medium (e.g., a mattress or mattress topper) that constitutes the bed 104. By monitoring the time-dependent deformation of the solid medium, respiratory rate (RR) and heart rate (HR) of the subject can be monitored in contactless fashion. Here, consistent with the foregoing characterization of “contactless monitoring,” monitoring implemented in “contactless fashion” refers to monitoring that avoids tethering the subject in any way to monitoring equipment.

[0031] To monitor RR and HR of the subject, the bed 104 is fitted with an accelerometer device 110 that is mechanically coupled with the subject via the solid medium that constitutes, at least partially, the bed 104. Without intending to be bound by theory, the solid medium propagates movement of the subject to the accelerometer device 110. The accelerometer device 110 can be assembled in numerous ways within or about the bed 104. For example, the accelerometer device 110 can be embedded within the solid medium (e.g., a mattress). As another example, in case the bed 104 includes a mattress topper, the accelerometer device 110 can be disposed on a surface at the interface between a mattress of the bed 104 and the mattress topper. In yet another example, the accelerometer device 110 can be embedded within a pillow that rests on a mattress of the bed 104. In still another example, the accelerometer device 110 can be affixed to the solid medium (e.g., a bed frame 105 of the bed 104).

[0032] The accelerometer device 110 is a low-noise accelerometer device that is configured to generate measurement signals representative of movement of the body of the subject as the subject breaths and has an active cardiovascular system while lying or sitting upright on the bed 104. The measurement signals can be referred to as accelerometer signals and form a time series of acceleration values. Each acceleration value can be referred to as a sample. The accelerometer device 110 generates the acceleration values at a defined sampling rate (or data output rate). Examples of the sampling rate are 250 Hz and 256 Hz.

[0033] The accelerometer device 110 is a low-noise accelerometer device and can be a three- axis accelerometer device, such as a three-axis microelectromechanical systems (MEMS) accelerometer. At a time of sampling acceleration, the three-axis accelerometer device generates three acceleration signals corresponding to three orthogonal axes, such as the axes x, y, and z of a Cartesian coordinate system. Each of those axes is referred to as a channel. Thus, over a time period, the accelerometer device 110 is configured to generate first accelerometer signals corresponding to a first channel a (x, y, or z), second accelerometer signals corresponding to a second channel 0, and third accelerometer signals corresponding to a third channel y (x, y, or z), where oc 0 y. The inertial measurements are collectively representative of (i) an orientation of the three-axis accelerometer device relative to gravity when the accelerometer device is at rest and (ii) a rectilinear acceleration vector. When the accelerometer device 110 moves in response to movement of the subject’s body, the accelerometer device 110 can measure the generated rectilinear acceleration vector. Without intending to be bound by modeling, during contactless monitoring of vital signs of a subject in accordance with this disclosure, the rectilinear acceleration vector has negligible magnitude. [0034] The accelerometer device 110 is functionally coupled to a computing device 120. The computing device 120 can include computing resources (not shown) including one or more processing units, each including at least one processor; one or more memory devices, one or more interfaces that can permit the exchange of data and signaling between a processing unit and a memory device and/or between the computing device and an external device; a power supply; a combination of the foregoing; and/or similar resources. Although the computing device is illustrated as being external to the bed 104, the disclosure is not limited in that respect and other configurations are contemplated.

[0035] A communication architecture 114 permits the functional coupling between the accelerometer device 110 and the computing device 120. The particular structure of the communication architecture 114 depends on the type of computing device 120 that is coupled to the accelerometer device 110. Regardless of its particular structure, the communication architecture 114 includes an uplink that can transport accelerator signals 116 from the accelerometer device 110 to the computing device 120, as the accelerator signals 116 are generated. Hence, over time, the accelerometer device 110 can send a first time series {a _x } of acceleration values corresponding to channel x, a second time series {a _y } of acceleration values corresponding to channel y, and a third time series {a _z } of acceleration values corresponding to channel z. In one configuration, channel x and channel y correspond to in-plane acceleration on a plane perpendicular to gravity, and channel z correspond to acceleration on an axis parallel to gravity. The acceleration values in each of the first, second, and third time series are separated in time domain by a time interval equal to the reciprocal of the defined sampling rate of the accelerometer device 110. Thus, at a measurement time t _n , the accelerometer device 110 can send a first signal indicative of an acceleration value a _x [n] for channel x, a second signal indicative of an acceleration value a _y [n], and a third signal indictive of an acceleration value a _z [n]. Simply as an illustration, FIG. 2A presents plots of observed {a _x ), {a _y ), and {a _z } during an example time period for a subject lying on the bed 104.

[0036] The computing device 120 can obtain accelerometer signals from the accelerometer device 110 via the communication architecture 114. To the end, as is illustrated in FIG. 1, the computing device 120 can include an intake module 122 that can receive the accelerometer signals. [0037] A subject being monitored can, over time, change positions in the bed 104. Thus, a baseline of accelerometer signals corresponding to a channel can change over time. In addition, a bodily function of the subject other than the bodily function being monitored for vital signals can introduce noise in the inertial signals being used to monitor the vital signal. For example, cardiac function may introduce noise that can distort the monitoring of pulmonary ventilation. In addition, or in some cases, equipment (a refrigerator, for example) operating in proximity of the bed 104 and coupled (mechanically, electrically, or otherwise) to the bed 140 also can introduce noise that can distort the monitoring of a particular vital sign. Accordingly, the computing device 120 can condition the accelerometer signals received by the computing device 120 before determining an estimate of a vital sign of the subject. Because in this disclosure the monitoring of vital signals of the subject is contemporaneous with the generation of the accelerometer signals, the computing device 120 can condition the accelerometer signals as the accelerometer signals are received at the computing device 120. Conditioning of the accelerometer signals can result in the removal of components of accelerometer signal that are not germane or otherwise representative of the vital sign being monitored. Accordingly, an accelerometer signal that has been conditioned in accordance with aspects described herein can be a physiological signal corresponding to vital bodily functions associated with vital signs being monitored in contactless fashion.

[0038] To condition an accelerometer signal, and thus generate a physiological signal, the computing device 120 can include a conditioning module 126. The conditioning module 126 can operate on the accelerometer signals to detrend the accelerometer signals, resulting in detrended accelerometer signals. Detrending an accelerometer signal includes subtracting a moving average of the accelerometer signal. The moving average can change over time due to changes in position of the monitored subject in the bed 104. Hence, the detrended inertial signals lack baseline drift. Detrending the accelerometer signals ultimately removes the acceleration due to gravity from the accelerometer signals. Simply as an illustration, FIG. 2B presents accelerometer signals as received (referred to as raw accelerometer signals) and detrended accelerometer signals for a particular channel of the accelerometer device 110, during an example time interval. The example time interval span approximately 8.5 hours, which can correspond to a desirable amount asleep time in a night.

[0039] As part of conditioning accelerometer signals, and thus generate physiological signals, the conditioning module 126 (FIG. 1) also can operate on detrended accelerometer signals to filter the detrended accelerometer signals, resulting in filtered accelerometer signals. In order to yield filtered accelerometer signals that are appropriate for determining estimates of respiratory rate, the conditioning module 126 can filter the detrended accelerometer signals by applying a low- pass filter configured to remove frequencies exceeding a cutoff frequency. The cutoff frequency is configured to a value that ensures that all likely frequencies of pulmonary ventilation in an adult healthy human are contained in the filtered accelerometer signal. Those likely frequencies can be within a breathing bandwidth that can range from about 0.08 Hz to about 0.42 Hz, for example. By filtering the detrended acceleration signals in such a fashion, frequency components associated with other bodily functions besides the particular bodily function being monitored (e.g., breathing, or cardiac function) can be removed from the detrended acceleration signals. In other words, filtering the detrended acceleration signals can retain respiratory information in the fundamental frequency, and eliminates higher frequencies associated with other vital bodily functions. In one example configuration, the cutoff frequency is 1.0 Hz and the low-pass filter is a Butterworth filter having four taps. In another example configuration, the low-pass filter is a bidirectional IIR filter with a corner frequency of 0.2 Hz. The disclosure is, of course, not limited to such example configurations.

[0040] Similarly, to yield filtered accelerometer signals that are appropriate for determining estimates of heart rate, the conditioning module 126 can filter detrended accelerometer signals by applying a band-pass filter configured to remove frequencies exceeding a cutoff frequency within a pulse bandwidth and frequencies less than a lower bound of the pulse bandwidth. In some configurations, the band-pass filter is a Butterworth filter that removes frequencies outside the interval from 0.5 Hz to 20 Hz, to remove effects of the respiratory modulation and other higher frequency effects.

[0041] The sampling rate of the accelerometer device 110 is greater that the RR and/or the HR of the subject being monitored. Thus, in some cases, the conditioning module 126 can optionally decimate detrended accelerometer signals before filtering those signals as is described above. In one example, by decimating a detrended accelerometer signal, the conditioning module 126 can downsample the detrended acceleration signal to 5 Hz to monitor respiratory rate. In another example, by decimating a detrended accelerometer signal, the conditioning module 126 can downsample the detrended acceleration signal to 50 Hz to monitor heart rate. For heart rate, 50 Hz is used instead of 5 Hz because HR has a shorter period than RR. [0042] Conditioning the accelerometer signals received at the computing device 120 results in conditioned accelerometer signals for respective accelerometer channels. Namely, in cases where the accelerometer device 110 is a low-noise three-axis accelerometer device, the conditioning module 126 can generate a first conditioned accelerometer signal corresponding to channel x, a second conditioned accelerometer signal corresponding to channel y, and third accelerometer signals corresponding to channel z. As mentioned, the conditioned accelerometer signals can be physiological signals corresponding to vital bodily functions associated with vital signs being monitored in the contactless fashion as is described herein.

[0043] Because the monitoring of vital signs in accordance with this disclosure is contemporaneous with the generation of the accelerometer signals while a subject sleeps or is lying in the bed 104 at rest, accelerometer signals received at the computing device 120 may, in some instances, have poor quality due to noise or other factors, such as the subject being absent from the bed 104. As such, the computing device 120 can determine if a received accelerometer signal, e.g., a current sample a _g [n] (p = x, y, or z), is valid or otherwise suitable for inclusion in the analysis involved in the determination of a vital sign. To that end, as is illustrated in FIG. 1, the computing device 120 can include a validation module 130 that can determine if an energy of the received accelerometer signal has a magnitude within a defined range. A valid accelerometer signal has an energy within the defined range. Conversely, an invalid accelerometer signal has an energy outside the defined range. Without intending to be bound by theory, the energy E[n] of a current acceleration signal, a _g [n], is defined in terms of an energy contribution of the immediately prior acceleration signal, al 'll — 1], and another energy contribution of a^n]. Defining E[n] in such a fashion provides added computation stability to the techniques described herein. The lower bound (or threshold) of the defined range serve as a check on whether a subject is present in the bed 104. The upper bound serves as a guard on movement that causes large spikes in the accelerometer data. Accordingly, the upper bound can be several orders of magnitude greater than the lower bound. Simply as an illustration, FIG. 3 is a plot 300 of conditioned acceleration signals in a particular channel, in accordance with aspects described herein. The conditioned acceleration signals include a period 310 of invalid signals.

[0044] The validation module 130 can determine, using conditioned accelerometer signals, the presence or absence of valid signals in respective accelerometer channels and can update valid signal datasets (e.g., time series of valid signals) for the respective accelerometer channels. Each valid signal dataset of the multiple valid datasets defines a conditioned acceleration waveform in a particular channel of the respective channels. The conditioned acceleration waveform is a physiologic signal a physiological signal corresponding to vital bodily functions associated with a vital sign (e.g., RR or HR) being monitored in contactless fashion. That is, in case the accelerometer device 110 is a three-axis accelerometer device, a first valid dataset of the multiple datasets defines a first conditioned acceleration waveform (or first physiological signals) for channel x, a second valid dataset of the multiple datasets defines a second conditioned acceleration waveform (or second physiological signals) for channel y, and a third valid dataset of the multiple datasets defines a third acceleration waveform (or third physiological signals) for channel z.

[0045] A conditioned acceleration waveform defined by a valid signal dataset for an accelerometer channel (x, y, or z, for example) can be substantially cyclic in time because pulmonary ventilation and blood circulation in a subject are substantially cyclic. Such cyclic characteristic is visible in portions of valid signal in FIG. 3. Accordingly, depending on the manner of conditioning the raw acceleration signals received by the computing device 120, the conditioned acceleration waveform is a surrogate of either pulmonary ventilation or blood circulation in the subject. Consequently, the computing device 120 can determine a respiratory rate or heart rate for the accelerometer channel based on local maxima (peaks) and local minima (troughs) in the acceleration waveform.

[0046] To identify peaks and troughs in a conditioned acceleration waveform (or physiological signals), the computing device 120 can include an extrema identification module 134. The extrema identification module 134 can determine a peak by determining that a particular physiological signal of the physiological signals exceeds a peak threshold value, and also determining that the particular physiological signal is a local maximum of the conditioned acceleration waveform including the particular physiological signal. In some cases, the extrema identification module 134 can update the peak threshold value after a current peak has been identified. As an illustration, an example of the temporal dependence of the peak threshold value is shown as trace 310 in FIG. 3.

[0047] Additionally, as part of determining the peak, the extrema identification module 134 can determine that a separation in time-domain between the local maximum and a second local maximum is equal to or greater than a first threshold time interval. The second local maximum immediately precedes the local maximum and may have been determined in a prior determination of a peak based on the conditioned acceleration waveform.

[0048] The extrema identification module 134 can determine a trough by determining that a particular physiological signal of the physiological signals is less than a trough threshold value, and also determining that the particular physiological signal is a local minimum of the conditioned acceleration waveform including the particular physiological signal. In some cases, the extrema identification module 134 can update the trough threshold value after a current trough has been identified. As an illustration, an example of the temporal dependence of the trough threshold value is shown as trace 320 in FIG. 3.

[0049] Additionally, as part of determining the trough, the extrema identification module 134 can determine that a separation in time-domain between the local minimum and a second local minimum is equal to or greater than a second threshold time interval. The second local minimum immediately precedes the local minimum and may have been determined in a prior determination of a trough based on the conditioned acceleration waveform. In some cases, the second threshold time interval and the first threshold time interval can be equal to one another. In other cases, the second threshold time interval can be different from the first threshold time interval can be equal to one another.

[0050] After a peak has been identified in a conditioned acceleration waveform (or physiological signals) for a particular accelerometer channel, the extrema identification module 134 can update a peak dataset indicative of multiple peaks that have been detected up to a current time t _n . The peak dataset includes timestamps corresponding to the measurement times of respective peaks. In some cases, the peak dataset is updated if the peak satisfies an inclusion rule. In one example, the inclusion rule dictates that in order to include a newly identified peak in the peak dataset, a separation in time-domain between the newly identified peak and an immediately prior peak must exceed a threshold time interval. The peak dataset can be part of data 154 retained in a memory 152. The threshold time interval is configurable and can be based on the expected minimum RR and HR for an adult healthy human. In some implementations, the threshold time interval can be personalized to the subject being monitored, in case the subject has an underlying cardiologic condition, cardiopulmonary condition, particular physical attributes (e.g., the subject is an infant or a non-human animal), or the like. [0051] Similarly, after a trough has been identified in the conditioned acceleration waveform (or physiological signals) for the particular accelerometer channel, the extrema identification module 134 can update a trough dataset indicative of multiple troughs that have been detected up to a current time t _n . The peak dataset includes timestamps corresponding to the measurement times corresponding to respective peaks. In some cases, the trough dataset is updated if the trough satisfies an inclusion rule. In one example, the inclusion rule dictates that in order to include a newly identified trough in the trough dataset, a separation in time-domain between the newly identified trough and an immediately prior trough must exceed a threshold time interval. The trough dataset can be part of data 154 retained in a memory 152. The threshold time interval is configurable and can be based on the expected minimum RR and HR for an adult healthy human. In some implementations, the threshold time interval can be personalized to the subject being monitored, in case the subject has an underlying cardiologic condition, cardiopulmonary condition, particular physical attributes (e.g., the subject is an infant or a non-human animal), or the like.

[0052] The extrema identification module 134 can update peak datasets for respective accelerometer channels, and also can update trough datasets for the respective accelerometer channels. As such for a channel p (x, y, or, z, for example), the extrema identification module 134 can update, over time, a peak dataset and a trough dataset. The extrema identification module 134 can retain the peak dataset and the trough dataset within the memory 152, as part of multiple datasets 154.

[0053] The computing device 120 can determine, using a peak dataset for a channel p (x, y, or, z, for example), an estimate of a vital sign of the subject being monitored. For example, the vital sign can be RR or HR, and the estimate can be denoted by > ^peak where V indicates either RR or HR. To determine such an estimate, the computing device 120 can include an estimator module 138. Such an estimate can be determined contemporaneously as accelerometer signal for the channel p becomes available. In other words, in sharp contrast to existing technologies, the estimate quantifies the vital sign in real-time or nearly real-time.

[0054] For the same channel p, using a trough dataset for that channel, the computing device 120 also can determine another estimate of the vital sign of the subject. As mentioned, in one ( troual example, the vital sign can be RR or HR, and the estimate can be denoted by , where V indicates either RR or HR. Such an estimate can be determined contemporaneously as acceleration signal for the channel m become available. Again, in other words, in sharp contrast to existing technologies, the estimate quantifies the vital in real-time or nearly real-time.

[0055] As is illustrated in FIG. 1, the estimator module 138 can include an evaluator component 142 that can determine, using respective peak datasets for at least one of channels x, y, and z, one or more first estimates of a vital sign. The evaluator component 142 also can determine, using respective trough datasets for at least one of channels x, y, and z, one or more second estimates of the vital sign. In an example scenario where channel x and channel y are monitored (e.g., the intake module 122 rejects or otherwise excludes acceleration signals in channel z), the evaluator component 142 can determine and where V indicates either RR or HR in some cases.

[0056] Having access to contemporaneous estimates of a vital sign on a per-channel basis provides information that is largely unavailable in existing technologies. A more practical application, however, involves providing a single estimate of the vital sign in real-time or essentially real-time. Such a practical application is absent from commonplace noncontact technologies. To provide a single estimate of a vital sign, the estimator module 138 includes a merger component 144 that can combine multiple estimates of the vital sign in order to determine the single estimate of the vital sign. Such a single estimate can be referred to as observed estimate. [0057] The computing device 120, via the merger component 144, can combine multiple channel-specific estimates differently depending on the vital sign being monitored. In cases where the vital sign is respiratory rate, the computing device 120 can determine an observed estimate of the respiration rate as follows. The computing device 120 can generate a list of accelerometer channels having first estimates of the vital sign and second estimates of the vital sign that are satisfactory. An accelerometer channel (e.g., x, y, or z) is considered to have a satisfactory estimate of the vital sign if the accelerometer channel is valid and a magnitude of the difference between the first estimates of the vital sign and the second estimates of the vital sign is less than or equal to a defined threshold value. That is, the fist estimates of the vital sign and the second estimates of the vital sign satisfy a matching criterion, where the matching criterion dictates that the different between the first estimates of the vital sign and the second estimates of the vital sign must be less than or equal to a defined threshold value. Thus, the list of channels can be an empty set or can include at least one channel. Because the monitoring is performed in real-time or nearly real-time, the number of channels present in the list of channels can be time dependent. It is noted that this disclosure is not limited to matching first estimates of the vital sign and second estimates of the vital sign. In some configurations, the computing device 120, via the merger component 144 can determine the list of channels by determining that a group of particular estimates satisfy the matching criterion, where the group of estimates includes a combination of a particular estimate of the first estimates, a second particular estimate of the first estimates, a third estimate of the first estimates, a particular estimate of the second estimates, a second particular estimate of the second estimates, or a third particular estimate of the second estimates. Further, the computing device 120 also can update a group of matching estimates to include the group of particular estimates. The list can correspond to the group of matching estimates.

[0058] In a situation where the list of accelerometer channels contains at least one channel, the computing device 120 can determine a median of the first estimates of the vital sign and the second estimates of the vital sign corresponding to the at least one channel. The disclosure is not limited to determining such a median. Indeed, in some cases, as part of determining an observed estimate of the respiratory rate, the computing device 120 can determine a median of the group of matching estimates. Irrespective of how the median has been determined, the computing device 120 can configure the observed estimate of the respiratory rate as the median that has been determined.

[0059] In a situation where the list of accelerometer channels is the empty set and one or more valid accelerometer channels are present at a current time, the computing device 120 can determine if any of the first estimates of the vital sign for a particular channel of the channel(s) is within a defined threshold value of one of the second estimates of the respiratory rate for a second particular channel of the channel(s). In some instances, the computing device 120 can determine that one or more particular first estimates of the respiratory rate are within the defined threshold value from one of the second estimates of the respiratory rate for the second particular channel of the channel(s). In those instances, the computing device 120 can determine a mean or average of the particular first estimate(s) and can then configure the mean or average as the observed estimate of the respiratory rate. In other instances, the computing device 120 can determine that none of the first estimates of the respiratory rate are within the defined threshold value from one of the second estimates of the respiratory rate for the second particular channel of the channel(s). In those other instances, the computing device 120 can configure the observed estimate of the respiratory rate as undefined.

[0060] After the merger component 144 configures an observed estimate of the respiratory rate, the merger component 144 can update a particular dataset of the datasets 154 to retain the observed estimate of the respiratory rate. The merger component 144 can update the particular dataset at a defined retention rate, in some cases, in order to retain a desired portion of the observed estimates of the respiratory rate that have been determined over time. The particular dataset can include a series of observed estimates of the respiratory rate. The series begins with an initial observed estimate of respiratory rate and ends with a current observed estimate of the respiratory rate. As instantaneous acceleration continues to be sampled by the accelerometer device 110 and obtained by the computing device 120, the computing device 120 can determine additional observed estimates of the respiratory rate. As a result, the particular dataset can be updated in real-time or nearly real-time with current observed estimates of the respiratory rate.

[0061] In situations where the vital sign is heart rate, the computing device 120 can determine the observed estimate of the heart rate as follows. The recoil forces involved in the pulsating movement of blood that is caused by a subject’s heart are smaller in magnitude relative to the force resulting from movement of the chest wall during breathing. As a result, compared to respiratory signals, cardiac function signals have substantially smaller magnitude and can be more significantly affected by subject movement and position on the bed 104. Therefore, some accelerometer signals in a channel may be more reliable that other accelerator signals in another channel. Accordingly, determining the observed estimate of the heart rate can include performing channel selection in order to identify one or more channels suitable for determining the observed estimate of the heart rate. As is described herein, in cases where the accelerometer device 110 is a three-axis accelerometer device, the one or more accelerometer channels can be a combination of channel x, channel y, and channel z. To select a channel, the merger component 144 can generate a channel score indicative of channel quality for each one of the accelerometer channels for which accelerometer signals have been obtained by the computing device 120. In some implementations, the channel score is defined as a ratio of energy of filtered acceleration signal and energy of detrended acceleration signal. That is, the channel score is larger for good signal quality and smaller for poor signal quality. The merger component 144 can then rank accelerometer channels in decreasing order of channel score. [0062] Further, also as part of determining the observed estimate of the heart rate, the merger component 144 can traverse the ranking of accelerometer channels and can determine if, for a current accelerometer channel in the traversal, a magnitude of the difference between the first estimates of the vital sign (e.g., heart rate) and the second estimates of the vital sign (e.g., heart rate) is less than or equal to a defined threshold value. In the affirmative case, the merger component 144 can select the current accelerator channel and can determine an average of the first estimates of the vital sign for the selected accelerometer channel and the second estimates of the vital sign for the selected accelerometer channel. Still also as part of determining the observed estimate of the heart rate, the merger component 144 can configure the average as the observed estimate of the heart rate. The merger component 144 can then update a particular dataset of the datasets 154 to retain the observed estimate of the heart rate. The merger component 144 can update the particular dataset at a defined retention rate, in some cases, in order to retain a desired portion of the observed estimates of the heart rate that have been determined over time. The particular dataset can include a series of observed estimates of the heart rate. The series begins with an initial observed estimate and ends with a current observed estimate. As instantaneous acceleration continues to be sampled by the accelerometer device 110 and obtained by the computing device 120, the merger component 144 can determine additional observed estimates of the heart rate. As a result, the particular dataset can be updated in real-time or nearly real-time with current observed estimates of the heart rate.

[0063] It is noted that while an observed estimate of a vital sign can be obtained using (i) first estimates of the vital sign based on peak datasets and (ii) second estimates of the vital sign based on trough datasets, the disclosure is not limited in that respect. Indeed, in some cases, the observed estimate of the vital sign can be determined using multiple estimates of the vital sign based on one of peak datasets or trough datasets. In such cases, the computing device 120 determine the observed estimate of the vital sign as is described herein, but using multiple estimates arising from the peak datasets or the trough datasets.

[0064] In response to determining a current observed estimate of a vital sign (e.g., RR or HR), the computing device 120 can cause a display device 160 to present the current observed estimate of the vital signal. Because the current observed estimate of the vital sign is part of a series of observed estimates of the vital sign, the computing device 120 can cause the display device 160 to present observed estimates of the vital sign at a defined presentation rate. The defined presentation rate is configurable. Examples of the defined presentation rate are 1 Hz and 5 Hz.

[0065] As illustrated in FIG. 1, the computing device 120 can include a reporting module 148. The reporting module 148 can cause the display device 160 to present observed estimates of a vital sign over time. To that end, the reporting module 148 can access a dataset containing a series of observed estimates of the vital sign to obtain an observed estimate of the vital sign. The reporting module 148 can access the dataset at the defined presentation rate. The reporting module 148 can then instruct the display device 160 to present the observed estimate of the vital sign. To that end, the reporting module 148 can send display data 156 to the display device 160. The display data 156 can include first data defining the observed estimate of the vital sign and, in some cases, second data defining formatting attribute(s) to present the observed estimate of the vital sign. The observed estimate of the vital sign can be presented in a graphical user interface (GUI) according to a defined format. The reporting module 148 can provide formatting information defining the visual appearance of the GUI. Simply as an illustration, FIG. 4 is an example of a GUI 400 where HR and RR are presented as a function of time, as the observed estimates of those vital signs become available in accordance with aspects described herein. The example GUI 400 incudes a first pane 400 presenting heart rate as a function of time, and a second pane 450 presenting respiratory rate as a function of time.

[0066] As mentioned, the computing device 120 can retain observed HR estimates over time, as accelerometer signals become available. In some configurations, the computing device 120 can determine heart rate variability (HRV) using the observed HR estimates. To that end, in those configurations, the estimator module 138 can determine a difference AHR between a current observed HR estimate and an immediately prior observed HR estimate. The time difference between such consecutive heart beats can be determined, in units of seconds, as AT = 60(AHR) ^_1 . The estimator module 138 can retain the obtained current value of A in a dataset of the datasets 154 within the memory 150. As further accelerometer signals become available, the estimator module 138 can determine a series of values {AT} over a defined time interval AT (e.g., 180 s, 240 s, 300 s, or similar) and can retain the series of values { T} in a particular dataset of the datasets 154. The estimator module 138 can then determine the variance of the series of values {AT} and can configure such a variance as an observed estimate of HRV for a subject being monitored. The reporting module 148, at the direction of the estimator module 138, for example, can send or otherwise made available the observed estimate of HRV for the subject. In some cases, the reporting module 148 can cause the display device to present the observed estimate of heart rate variability.

[0067] In some cases, the computing device 120 can supply observed estimates of a vital sign or several vital signs to a data storage platform (not depicted) remotely located relative to the computing device 120. For example, the reporting module 148 can cause the data storage platform (not depicted in FIG. 1) to retain observed estimates of a vital sign in a data repository containing medical record of the subject being monitored.

[0068] As it has been described, FIG. 1 illustrates an example system 100 having the accelerometer device 110 and the computing device 120 assembled at disparate locations, with only the accelerometer device 110 being integrated into the bed 104. The disclosure, of course, is not limited in that respect. Indeed, the systems for the contactless monitoring of vital signs in accordance with aspects of this disclosure can include a contactless monitoring unit that is integrated into a bed or another furniture and has enclosed therein the accelerometer device 110 and the computing device 120. To that point, simply as an illustration, FIG. 5 is a schematic block diagram of a contactless monitoring unit 510 that includes the computing device 120 and the accelerometer device 110 being functionally coupled with one another by the communication architecture 114.

[0069] FIG. 6 illustrates an example of an operating environment 600 for the contactless monitoring of vital signs, in accordance with one or more aspects of this disclosure. The operating environment 600 includes a bed 602 having a bed frame 604 and a mattress 606, and also includes the contactless monitoring unit 510 and a reference sensor device 608. The bed 602 can be an example of the bed 104 (FIG. 1). As is described herein, the operating environment 600 includes a setup for a subject (e.g., a patient) to recline or rest in a supine, prone, right/left lateral recumbent, seated, or other various positions on the mattress 606. In some examples, the contactless monitoring unit 510 is embedded within the mattress 606. In other examples, the contactless monitoring unit 510 is embedded within a pillow 610a or a pillow 610b. In some examples, the contactless monitoring unit 510 is positioned proximate the mattress (e.g., on or within the bed frame 604; not shown). The reference sensor system 608 is positioned on (or within) the bed frame 604 and can sense noises and vibrations that are external to the mattress 606 and do not originate from the mattress 606. For example, the reference sensor system 608 senses vibrations and interference that does not originate from the subject being monitored.

[0070] In some embodiments, one or more accelerometer devices are configured at a specified location on the bed 602. In one configuration, two accelerometer devices can be assembled on top of the mattress 606, with one accelerometer device close to a location where a subject would lie and another accelerometer on the ground. The accelerometer device on the ground can generate accelerometer signals from other equipment (e.g., a refrigerator, HVAC equipment, or similar) in the space where the bed 602 is located. Thus, the accelerometer device on the ground can be used to remove environment noise from the accelerometer signals from the accelerometer device(s) in assembled in the bed 602. In some cases, additional sensor devices and accelerometer devices may be placed proximate the mattress 606 in various configurations. For example, an additional accelerometer device may be placed beside the subject within a first range (e.g., up to 15 cm away from the subject) primarily for noise filtration. The accelerometer data from the one or more accelerometer devices can be used to filter out outside vibrations (e.g., noise) by aggregating datasets from the one or more accelerometer devices. After acceleration signals are conditioned in accordance with aspects described herein, the physiological signal can be recovered.

[0071] In some cases, the configuration of the one or more accelerometer devices includes mounting the one or more accelerometer devices to a rigid plate to reduce noise and vibrations. One or more additional accelerometer devices can be used to filter out additional external noise (e.g., walking, clapping, and/or shouting sounds). The one or more additional accelerometer devices can be useful to enhance the signal quality and to improve fidelity of the tracked accelerometer signals.

[0072] In some implementations, signal from a reference sensor device 608 is used to filter out external noise. Vibrations originating from outside of the mattress 606 or bed frame 604 propagate into them and can introduce significant noise into the acceleration signals measured by an accelerometer device integrated into the contactless monitoring unit 510. The vibrations can be caused by various external sources, such as footsteps in a space that contains the bed 602 or a vehicle passing outside such a space. This can disrupt the contactless monitoring techniques implemented by the contactless monitoring unit 510 and also can corrupt the output of such techniques. In some cases, the external vibrations can be mitigated by placing a reference sensor device 608 on the floor next to one of the bed posts or on a leg of the bedframe 604 itself. This 1 reference sensor device 608 measures vibration signals in the same way as the accelerometer device(s) integrated into the contactless monitoring unit 510. However, measurements generated by the reference sensor device 608 can exclude the vital signs because of the distance that separates the reference sensor device 608 and the subject being monitored.

[0073] The contactless monitoring unit 510 can include one or more low-noise accelerometer devices. The contactless monitoring unit 510 also can include one or more electronic devices having a processor configured to receive, process, transmit, and/or analyze data from the one or more low noise accelerometer devices. The one or more accelerometer devices can be configured on a rigid structure and placed in distinct locations with respect to the other one or more accelerometer devices. The mechanical movements of the heart can be detected by one or more accelerometer devices placed within a reasonable distance of the subject being monitored, without the subject being in direct contact with the contactless monitoring unit 510. Each one of the accelerometer device(s) are coupled to a processor to ensure that raw acceleration signals are timestamped and recorded.

[0074] The contactless monitoring unit 510 can include one or more accelerometer devices functionally coupled (e.g., mechanically, electrically, and/or communicatively coupled) with a microcontroller. One or many insulation-displacement contact (IDC) connectors can provide such functional coupling.

[0075] In some embodiments, the contactless monitoring unit 510 may be coupled to various external devices for further analysis and/or distribution. In some examples, the contactless monitoring unit 510 may include one or more cables coupling the monitoring system to the various external devices. In some examples, the contactless monitoring unit 510 wirelessly communicates with one or more external devices.

[0076] Additional considerations include introducing a rigid plastic plate beneath one or more of the accelerometers to house the accelerometer, signal processing and data transmission device to increase the sensitivity. This can be achieved by physically affixing the housing to the frame of a chair and/or a bed.

[0077] In some cases, a second contactless vital signal monitoring system is placed on the other side of the bed 602 to measure vital signs from a second subject. When multiple subjects are laying on the bed 602, additional processing may be required to differentiate between their physiological signals as observed by an in-bed sensor. To account for this, a system is provided in which multiple sensors are placed on or in the bed and algorithmic processing is applied to separate the signals corresponding to the occupants. This enables multi-point sensing and reporting of vital signs for each subject. In various examples, the contactless monitoring unit 510 receives second signals recorded by the second contactless vital sign monitoring system and uses the second signals to filter out noise and interference from the signals measured at the contactless monitoring unit 510. The algorithmic processing can include one or more of several multi-channel techniques including beamforming and/or source separation. Beamforming is a well-known technique in array signal processing. Source separation is often used in the audio signal processing.

[0078] In some cases, the contactless monitoring unit 510 can determine an orientation of subject with respect to the bed. An adult subject is usually oriented longitudinally along the length of the bed. In contrast, in infant care, the orientation of the subject can be inconsistent or a priori unknown. Using the contactless monitoring unit 510, the orientation of a subject can be determined from data collected by an accelerometer device by analyzing signals in the x-y plane of the accelerometer device in the frequency range occupied by heart-related vibrations, for example. A subject's heart naturally pumps blood upwards in the direction of the head, which causes a recoil force towards the subject's feet. This causes a major axis of vibration to emerge in the recorded signals that can be detected with principal component analysis or similar techniques. [0079] In some cases, respiratory signals extracted from accelerometer signals can be used to identify patterns of breathing indicative of respiratory conditions such as obstructive sleep apnea. Sleep apnea is characterized by occasional cessation of breathing for at least several seconds. Counting the number of such events during a subject's night's sleep and dividing by the duration of sleep gives the apnea-hypopnea index (AHI), a commonly used metric to quantify severity of apnea. For the specific case of sleep apnea, the respiration signal is extracted, and periods of cessation of breathing are identified and counted.

[0080] FIG. 7 illustrates an example of a computing device 710 that can provide various functionalities of contactless monitoring of vital signs in accordance with aspects of this disclosure. The computing device 710 can embody the computing device 120 (FIG. 1). The computing device 710 can provide such functionalities in response to execution of one or more software components retained within the computing device 710. Such component(s) can render the computing device 710 a particular machine for contactless monitoring of vital signs, among other functional purposes that the device 710 may have. A software component can be embodied in or can comprise one or more processor-accessible instructions, e.g., processor-readable and/or processor-executable instructions. In one scenario, at least a portion of the processor-accessible instructions can embody and/or can be executed to perform at least a part of one or more of the techniques described herein. The one or more processor-accessible instructions that embody a software component can be arranged into one or more program modules, for example, that can be compiled, linked, and/or executed at the computing device 710 or other computing devices. Generally, such program modules comprise computer code, routines, programs, objects, components, information structures (e.g., data structures and/or metadata structures), etc., that can perform particular tasks (e.g., one or more operations) in response to execution by one or more processors 712 integrated into the computing device 712.

[0081] The various example aspects of the disclosure can be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that can be suitable for implementation of various aspects of the disclosure in connection contactless monitoring of vital signs can include personal computers; server computers; laptop devices; handheld computing devices, such as mobile tablets or laptop computers; wearable computing devices; and multiprocessor systems. Additional examples can include network personal computers (PCs), mainframe computers, blade computers, programmable logic controllers, embedded systems, distributed computing environments that comprise any of the above systems or devices, and the like.

[0082] As is illustrated in FIG. 7, the computing device 710 includes one or multiple processors 712, one or multiple input/output (I/O) interfaces 716, one or more memory devices 720 (referred to as memory 720), and a bus architecture 722 (referred to as bus 722) that functionally couples various functional elements of the computing device 710. The computing device 710 can include, optionally, a radio unit 714. The radio unit 714 can include one or more antennas and a communication processing unit that can permit wireless communication between the computing device 710 and another device, such as a remote computing device, a display device, and/or a remote sensor device. The bus 722 can include at least one of a system bus, a memory bus, an address bus, or a message bus, and can permit the exchange of information (data and/or signaling) between the processor(s) 712, the I/O interface(s) 716, and/or the memory 720, or respective functional elements therein. In some cases, the bus 722 in conjunction with one or more internal programming interfaces 740 (also referred to as interface 740) can permit such exchange of information. In cases where the processor(s) 712 include multiple processors, the computing device 712 can utilize parallel computing.

[0083] The I/O interface(s) 716 can permit communication of information between the computing device 710 and an external device, such as another computing device, a display device, or similar device. Such communication can include direct communication or indirect communication, such as the exchange of information between the computing device 710 and the external device via a network or elements thereof. As illustrated, the I/O interface(s) 716 can include one or more of network adapter(s), peripheral adapter(s), and display unit(s). Such adapter(s) can permit or otherwise facilitate connectivity between the external device and one or more of the processor(s) 712 or the memory 720. For example, the peripheral adapter(s) can include a group of ports, which can include at least one of parallel ports, serial ports, Ethernet ports, V.35 ports, or X.21 ports. In certain aspects, the parallel ports can comprise General Purpose Interface Bus (GPIB), IEEE-1284, while the serial ports can include Recommended Standard (RS)-232, V.l l, Universal Serial Bus (USB), FireWire or IEEE-1394.

[0084] The I/O interface(s) 716 can include a network adapter that can functionally couple the computing device 710 to one or more remote computing devices, display devices, or sensors devices (not depicted in FIG. 7) via one or more traffic and signaling pipes that can permit or otherwise facilitate the exchange of traffic and/or signaling between the computing device 710 and such one or more remote computing devices or sensors. Such network coupling provided at least in part by the network adapter can be implemented in a wired environment, a wireless environment, or both. The information that is communicated by the network adapter can result from the implementation of one or more operations of a method in accordance with aspects of this disclosure. The I/O interface(s) 716 can include more than one network adapter in some cases.

[0085] In addition, or in some cases, depending on the architectural complexity and/or form factor the computing device 710, the I/O interface(s) 716 can include a user-device interface unit that can permit control of the operation of the device 710, or can permit conveying or revealing the operational conditions of the computing device 710. The user-device interface can be embodied in, or can include, a display unit. The display unit can include a display device that, in some cases, has touch-screen functionality. In addition, or in some cases, the display unit can include lights, such as light-emitting diodes, that can convey an operational state of the computing device 710.

[0086] The bus 722 can have at least one of several types of bus structures, depending on the architectural complexity and/or form factor the computing device 710. The bus structures can include a memory bus or a memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. As an illustration, such architectures can comprise an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, an Accelerated Graphics Port (AGP) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express bus, a Personal Computer Memory Card International Association (PCMCIA) bus, a Universal Serial Bus (USB), and the like.

[0087] The computing device 710 can include a variety of computer-readable media. Computer-readable media can be any available media (transitory and non-transitory) that can be accessed by a computing device. In one aspect, computer-readable media can comprise computer non-transitory storage media (or computer-readable non-transitory storage media) and communications media. Examples of computer-readable non-transitory storage media include any available media that can be accessed by the computing device 710, including both volatile media and non-volatile media, and removable and/or non-removable media. The memory 720 can include computer-readable media in the form of volatile memory, such as random access memory (RAM), and/or non-volatile memory, such as read-only memory (ROM).

[0088] The memory 720 can include functionality instructions storage 724 and functionality data storage 728. The functionality instructions storage 724 can include computer-accessible instructions that, in response to execution (by at least one of the processor(s) 724, for example), can implement one or more of the functionalities of this disclosure in connection with keyphrase detection. The computer-accessible instructions can embody, or can include, one or more software components illustrated as vitals monitoring component(s) 726. Execution of at least one component of the vitals monitoring components 726 can implement one or more of the methods described herein. Such execution can cause a processor (e.g., one of the processor(s) ) that executes the at least one component to carry out at least a portion of the methods disclosed herein. In some cases, the vitals monitoring component(s) 726 can include the intake module 122, the conditioning module 126, the validation module 130, the extrema identification module 134, the estimator module, and the reporting module 148.

[0089] A processor of the processor(s) 712 that executes at least one of the vitals monitoring components 726 can retrieve data from or retain data in one or more memory elements 720 in the functionality data storage 728 in order to operate in accordance with the functionality programmed or otherwise configured by the vitals monitoring components 726. The one or more memory elements 730 may be referred to as vitals monitoring data 730. Such information can include at least one of code instructions, data structures, or similar. For example, at least a portion of such data structures can define the datasets 154 (FIG. 1) and, thus, can define time series of acceleration values for various accelerometer channels, time series of vital sign values, time series of HRV values, and/or any other type of data relevant to contactless monitoring vital signs in accordance with aspects of this disclosure.

[0090] The interface 740 (e.g., an application programming interface) can permit or facilitate communication of data between two or more components within the functionality instructions storage 724. The data that can be communicated by the interface 740 can result from implementation of one or more operations in a method of this disclosure. In some cases, one or more of the functionality instructions storage 724 or the functionality data storage 728 can be embodied in or can comprise removable/non-removable, and/or volatile/non-volatile computer storage media.

[0091] At least a portion of at least one of the vitals monitoring components 726 or the vitals monitoring data 480 can program or otherwise configure one or more of the processor(s) 712 to operate at least in accordance with the functionality described herein. One or more of the processor(s) 712 can execute at least one of the vitals monitoring components 726, and also can use at least a portion of the data in the functionality data storage 728 in order to provide contactless monitoring of vital signs in accordance with one or more aspects described herein. In some cases, the functionality instructions storage 724 can embody or can include a computer-readable non-transitory storage medium having computer-accessible instructions that, in response to execution, cause at least one processor (e.g., one or more of the processor(s) 712) to perform a group of operations comprising the operations or blocks described in connection with example methods disclosed herein. [0092] In addition, the memory 720 can include processor-accessible instructions and information (e.g., data, metadata, and/or program code) that permit or facilitate the operation and/or administration (e.g., upgrades, software installation, any other configuration, or the like) of the computing device 710. Accordingly, as illustrated, the memory 720 can include a memory element 732 (labeled operating system (O/S) instructions 732) that contains one or more program modules that embody or include one or more operating systems, such as Windows operating system, Unix, Linux, Symbian, Android, Chromium, and substantially any OS suitable for mobile computing devices or tethered computing devices. In one aspect, the operational and/or architectural complexity of the computing device 710 can dictate a suitable O/S. The memory 720 also includes system information storage 736 having data, metadata, and/or program code that permits or facilitates the operation and/or administration of the computing device 710. Elements of the O/S instructions 732 and the system information storage 736 can be accessible or can be operated on by at least one of the processor(s) 712.

[0093] It should be recognized that while the components retained in the functionality instructions storage 724 and other executable program components, such as the O/S instructions 732, are illustrated herein as discrete blocks, such software components can reside at various times in different memory components of the computing device 710, and can be executed by at least one of the processor(s) 712.

[0094] The computing device 710 can include a power supply (not shown), which can power up components or functional elements within such devices. The power supply can be a rechargeable power supply, e.g., a rechargeable battery, and it can include one or more transformers to achieve a power level suitable for the operation of the computing device 710 and components, functional elements, and related circuitry therein. In some cases, the power supply can be attached to a conventional power grid to recharge and ensure that such devices can be operational. To that end, the power supply can include an I/O interface (e.g., one of the interface(s) 716) to connect to the conventional power grid. In addition, or in other cases, the power supply can include an energy conversion component, such as a solar panel, to provide additional or alternative power resources or autonomy for the computing device 710.

[0095] In some scenarios, the computing device 710 can operate in a networked environment by utilizing connections to one or more remote computing devices and/or sensor devices (not depicted in FIG. 7). As an illustration, a remote computing device can be a personal computer, a portable computer, a server, a router, a network computer, a peer device or other common network node, and so on. As described herein, connections (physical and/or logical) between the computing device 710 and a remote computing device or sensor can be made via one or more traffic and signaling pipes, which can comprise wired link(s) and/or wireless link(s) and several network elements (such as routers or switches, concentrators, servers, and the like) that form a local area network (LAN), a wide area network (WAN), and/or other networks (wireless or wired) having different footprints.

[0096] One or more of the techniques disclosed herein can be practiced in distributed computing environments, such as grid-based environments, where tasks can be performed by remote processing devices (e.g., network servers) that are functionally coupled (e.g., communicatively linked or otherwise coupled) through a network having traffic and signaling pipes and related network elements. In a distributed computing environment, one or more software components (such as program modules) may be located in both the computing device 710 and at least one remote computing device.

[0097] In view of the aspects described herein, example methods that may be implemented in accordance with this disclosure can be better appreciated with reference, for example, to the flowcharts in FIG. 8. Such example methods constitute at least a subset of the techniques described herein in connection with contactless monitoring a vital sign. For the sake of simplicity of explanation, the example methods disclosed herein are presented and described as a series of blocks (with each block representing an action or an operation in a method, for example). However, the example methods are not limited by the order of blocks and associated actions or operations, as some blocks may occur in different orders and/or concurrently with other blocks from those that are shown and described herein. Further, not all illustrated blocks, and associated action(s), may be required to implement an example method in accordance with one or more aspects of this disclosure. Two or more of the example methods (and any other methods disclosed herein) may be implemented in combination with each other. It is noted that the example methods (and any other methods disclosed herein) may be alternatively represented as a series of interrelated states or events, such as in a state diagram.

[0098] FIG. 8 is a flowchart of an example of a method for contactless monitoring a vital sign, in accordance with one or more aspects of this disclosure. The monitoring can be implemented in real-time or nearly real-time. The vital sign can be respiratory rate or heart rate, in some cases. A computing device or a system of computing devices can implement the example method 800 in its entirety or in part. To that end, each one of the computing devices includes computing resources that may implement at least one of the blocks included in the example method 800 and other methods described herein. The computing resources include, for example, central processing units (CPUs), graphics processing units (GPUs), tensor processing units (TPUs), memory, disk space, incoming bandwidth, and/or outgoing bandwidth, interface(s) (such as I/O interfaces or APIs, or both); microcontroller devices(s); power supplies; a combination of the foregoing; and/or similar resources. The computing device and the computing system can include multiple modules, and can implement the example method 800 by executing one or more instances of such modules. The computing device that implements the example method 800 can be the computing device 120 or the computing device 710, and the multiple modules can be the modules included in the computing device 120 or the computing device 710. More specifically, in some cases, the computing device that implements the example method 800 hosts the intake module 122, the conditioning module 126, the validation module 130, the extrema identification module 134, the estimator module 138, and the reporting module 148, and implements the example method 800 by executing one or more instances of such modules.

[0099] At block 810, the computing device (via the intake module 122, for example) can obtain multiple accelerometer signals corresponding to respective channels of an accelerometer device. As is described herein, in some cases the accelerometer device is a low-noise three-axis MEMS accelerometer, where each axis corresponds to a respective channel. Thus, a first signal of the multiple accelerometer signals corresponds to a first channel, a second signal of the multiple accelerometer signal corresponds to a second channel, and a third signal of the multiple accelerometer signals corresponds to a third channel. The disclosure is, of course not limited in that respect, and other types of accelerometers and associated signals are contemplated.

[0100] At block 820, the computing device (via the conditioning module 126, for example) can condition (or pre-process) the accelerometer signals. Conditioning an accelerometer signal includes operating on the accelerometer signal to remove components of the accelerometer signal that are not germane of otherwise representative of the vital sign being monitored. Conditioning the accelerometer signal result in a conditioned accelerometer signal. In some implementations, conditioning the accelerometer signal includes detrending the accelerometer signal and filtering the de-trended accelerometer signal. In other implementations, conditioning the accelerometer signal includes detrending the accelerometer signal, decimating the de-trended accelerometer signal, and filter the decimated accelerometer signal. In cases where the vital sign is respiratory rate, the filtering includes applying a low-pass filter having a defined cutoff frequency. An example of the cut-off frequency is of about 0.2 Hz. In cases where the vital sign is heart rate, the filtering includes applying a band-pass filter having a defined lower cutoff frequency and a defined higher cutoff frequency. In one example, the defined lower cutoff frequency is about 1.0 Hz and a higher cutoff frequency is about 20 Hz.

[0101] At block 830, the computing device (via the validation module 130, for example) can determine, using the conditioned accelerometer signals, multiple valid signal datasets corresponding to the respective channels. Each valid signal dataset of the multiple valid signal datasets defines an acceleration waveform in a particular channel of the respective channels.

[0102] The implementation of blocks 810 to 830, collectively, generates physiological signals corresponding to the multiple valid signal datasets, as is described hereinbefore. Thus, for one of the respective accelerometer channels, first physiological signals are generated, and for another one of the respective accelerometer channels, second physiological signals are generated.

[0103] At block 840, the computing device (via the extrema identification module 134, for example) can determine respective peak datasets for the multiple valid datasets. Determining the respective peak datasets includes determining that a particular physiological signal of the physiological signals exceeds a threshold value, and determining that the particular physiological signal is a local maximum of a waveform including the particular physiological signal. Additionally, determining the respective peak datasets also includes determining that a separation in time-domain between the local maximum and a second local maximum is equal to or greater than a threshold time interval. The second local maximum immediately precedes the local maximum and may have been determined in a prior iteration of the implementation of the example method 800, as part of contactless monitoring of vital signs in real-time or nearly real-time. After having determined a peak, the example method 800 can include updating a particular peak dataset, and updating the threshold value.

[0104] At block 850, the computing device (via the extrema identification module 134, for example) can determine respective trough datasets for the multiple valid datasets. Determining the respective trough datasets includes determining that a particular physiological signal of the physiological signals is less than a threshold value, and determining that the particular physiological signal is a local minimum of a waveform including the particular physiological signal. Additionally, determining the respective peak datasets also includes determining that a separation in time-domain between the local minimum and a second local minimum is equal to or greater than a threshold time interval. The second local minimum immediately precedes the local minimum and may have been determined in a prior iteration of the implementation of the example method 800, as part of contactless monitoring of vital signs in real-time or nearly real-time. After having determined a peak, the example method 800 can include updating a particular trough dataset, and updating the threshold value.

[0105] At block 860, the computing device (via the estimator module 138, for example) can determine, using the respective peak datasets, first estimates of a vital sign. In some cases, as is described herein, determining the first estimates of the vital sign can include identifying a first timestamp corresponding to a first peak in a particular one of the first datasets, and identifying a second timestamp corresponding to a second peak in the particular one of the first datasets. The first peak is a current peak and the second peak is an immediately prior peak, and the first timestamp defines a first time and the second timestamp defines a second time less than the first time. In addition, determining the first estimates also includes determining a rate based on an inverse of a difference between the first time and the second time; and configuring the rate as a particular estimate of the first estimates. As mentioned, the vital sign can be respiratory rate or heart rate. In example scenarios where the accelerometer device is a three-axis accelerometer device, the computing device can determine three first estimates of the vital sign, one for each channel. In an example scenario where the vital sign is respiratory rate and two channels of the accelerometer device are used — first channel a (x, y, or z) and second channel 0 (x, y, or z) — the computing device can generate .

[0106] At block 870, the computing device (via the estimator module 138, for example) can determine, using the respective trough datasets, second estimates of the vital sign. In some cases, determining the second estimates of the vital sign includes identifying a first timestamp corresponding to a first trough in a particular one of the second datasets, and also identifying a second timestamp corresponding to a second trough in the particular one of the second datasets. The first trough is a current trough and the second trough is an immediately prior trough, and the first timestamp defines a first time and the second timestamp defines a second time less than the first time. In addition, determining the second estimates of the vital sign also includes determining a rate based on an inverse of a difference between the first time and the second time, and configuring the rate as a particular estimate of the second estimates. In example scenarios where the accelerometer device is a three-axis accelerometer, the computing device can determine three second estimates of the vital sign, one for each channel. Referring again to the example scenario where the vital sign is respiratory rate and two channels of the accelerometer device are used — first channel a (x, y, or z) and second channel 0 (x, y, or z) — the computing device can generate RR ^rou3h) and RRf ^OU3h) .

[0107] At block 880, the computing device (via the merger component 144, for example) can determine, using the first and second estimates of the vital sign, an observed estimate of the vital sign. The computing device can implement such a determination differently depending on whether the vital sign is respiration rate or heart rate.

[0108] In cases where the vital sign is respiration rate, the computing device can determine the observed estimate of the respiration rate as follows. The computing device can generate a list of channels having first estimates of the vital sign and second estimates of the vital sign that are satisfactory. A channel is considered to have a satisfactory estimate of the vital sign if the channel is valid and a magnitude of the difference between the first estimates of the vital sign and the second estimates of the vital sign is less than or equal to a defined threshold value. Thus, the list of channels can be an empty set or can include at least one channel. Because the monitoring is performed in real-time or nearly real-time, the number of channels present in the list of channels can be time dependent. The example method 800 is not limited to identifying first estimates of the vital sign and second estimates of the vital sign that are satisfactory. In some cases, at block 880, the the computing device 120 (via the merger component 144, for example) can generate the the list of channels by determining that a group of particular estimates satisfy the matching criterion, where the group of estimates includes a combination of a particular estimate of the first estimates, a second particular estimate of the first estimates, a third estimate of the first estimates, a particular estimate of the second estimates, a second particular estimate of the second estimates, or a third particular estimate of the second estimates. Further, the computing device also can update a group of matching estimates to include the group of particular estimates. The list can correspond to the group of matching estimates.

[0109] Regardless of how the list is generated, in a situation where the list of channels contains at least one channel, the computing device can determine a median of the first estimates of the vital sign and the second estimates of the vital sign corresponding to the at least one channel. As mentioned, the example method 800 is not limited to determining such a median. Indeed, in some cases, as part of block 880, the computing device can determine a median of the group of matching estimates. Additionally, irrespective of how the median has been determined, at block 880, the computing device can configure the observed estimate of the vital sign as the median that has been determined.

[0110] In a situation where the list of channels is the empty set and one or more valid channels are present, the computing device can determine if any of the first estimates of the vital sign for a particular channel of the channel(s) is within a defined threshold value of one of the second estimates of the vital sign for a second particular channel of the channel(s). In some instances, the computing device can determine that one or more particular first estimates of the vital sign are within the defined threshold value of one of the second estimates of the vital sign for the second particular channel of the channel(s). In those instances, the computing device can determine a mean or average of the particular first estimate(s) and can then configure the mean or average as the observed estimate of the vital sign. In other instances, the computing device can determine that none of the first estimates of the vital sign are within the defined threshold value of one of the second estimates of the vital sign for the second particular channel of the channel(s). In those other instances, the computing device can configure the observed estimate of the vital sign as undefined.

[0111] In situations where the vital sign is heart rate, the computing device can determine the observed estimate of the heart rate as follows. As is described herein, the recoil forces involved in the pulsating movement of blood that is caused by a subject’s heart are smaller in magnitude relative to the force resulting from movement of the chest wall during breathing. As a result, the accelerometer signals may not be reliable in each channel. Accordingly, determining the observed heart rate, can include performing channel selection to identify one or more channels suitable for determining the observed estimate of the heart rate. In a three-axis accelerometer, the one or more channels can be a combination of channel x, channel y, and channel z. Channel selection includes generating a channel score indicative of channel quality (or “goodness”) for each one of the channels for which accelerometer signals have been obtained at block 810. In some implementations, the channel score is defined as a ratio of energy of filtered accelerometer signal and energy of detrended accelerometer signals. That is, the channel score is larger for good signal quality and smaller for poor signal quality. Channels can then be ranked in decreasing order of channel score. Determining the observed heart rate also includes traversing the ranking of channels from the top-ranked channel, and determining if, for a current channel in the traversal, a magnitude of the difference between the first estimates of the vital sign (e.g., heart rate) and the second estimates of the vital sign (e.g., heart rate) is less than or equal to a defined threshold value. In the affirmative case, the computing device selects the current channel and determines an average of the first estimates of the vital sign for the selected channel and the second estimates of the vital sign for the selected channel. As part of determining the observed estimate of the heart rate, the computing device then configures the average as the observed estimate of the heart rate. [0112] At block 890, the computing device (via the reporting module 148, for example) can provide the observed estimate of the vital sign. In some cases, providing the observed estimate of the vital sign can include downsampling the observed estimate of the vital sign to a desired output data rate. Examples of the data rate are 1 Hz and 5 Hz.

[0113] Blocks 810 to 890 can be implemented sequentially, in a loop, as accelerometer signals become available, in order to monitor the vital sign in real-time or nearly real-time.

[0114] It is noted that, some of the blocks of the example method 800 may not be implemented and still an observed estimate of the vital sign can be obtained. For example, in some cases one of block 840 or block 850 can be implemented as is described herein. In such cases, depending on whether the respective peak datasets or the respective tough datasets have been determined, one of block 860 or block 870 may not be implemented. Notwithstanding, in such cases, the observed estimate of the vital sign can be determining with multiple estimates of the vital sign based on one of peak datasets or trough datasets.

[0115] Numerous other aspects emerge from the foregoing detailed description and annexed drawings. Those aspects are represented by the following Clauses.

[0116] Clause 1 includes a computer-implemented method comprising: generating, using accelerometer signals, physiological signals for a subject, wherein the accelerometer signals correspond to respective accelerometer channels of an accelerometer device integrated into a flexible solid medium that is configured to support a subject in one of laydown position or sitting position and further configured to deform under load by the subject; determining, using the physiological signals, first datasets indicative of peaks corresponding to respective accelerometer channels; determining, using the physiological signals, second datasets indicative of troughs corresponding to respective accelerometer channels; determining, using the first datasets, first estimates of a vital sign of the subject; determining, using the second datasets, second estimates of the vital sign; determining, using the first estimates and the second estimates, an observed estimate of the vital sign; and providing the observed estimate of the vital sign.

[0117] Clause 2 includes Clause 1 , where the providing comprises causing a display device to present the observed estimate of the vital sign at a defined presentation rate.

[0118] Clause 3 includes any of the preceding Clauses 1 or 2, where the generating comprises detrending a first accelerometer signal of the accelerometer signals, resulting in a first detrended accelerometer signal.

[0119] Clause 4 includes any of the preceding Clauses 1 to 3, where the generating further comprises: filtering the first detrended accelerometer signal; and validating the filtered first detrended accelerometer signal, resulting in a first physiological signal of the physiological signals.

[0120] Clause 5 includes any of the preceding Clauses 1 to 4, where the vital sign is respiratory rate, and wherein the filtering comprises applying a low-pass filter having a cutoff frequency of about 0.2 Hz.

[0121] Clause 6 includes any of the preceding Clauses 1 to 5, where the vital sign is heart rate, and wherein the filtering comprises applying a band-pass filter having a lower cutoff frequency of about 1.0 Hz and a higher cutoff frequency of about 20 Hz.

[0122] Clause 7 includes any of the preceding Clauses 1 to 6, where the determining the first datasets comprises: determining that a particular physiological signal of the physiological signals exceeds a threshold value; determining that the particular physiological signal is a local maximum of a waveform including the particular physiological signal; and determining that a separation in time-domain between the local maximum and a second local maximum is equal to or greater than a threshold time interval, wherein the second local maximum immediately precedes the local maximum.

[0123] Clause 8 includes any of the preceding Clauses 1 to 7, where the determining the first estimates comprises: identifying a first timestamp corresponding to a first peak in a particular one of the first datasets; identifying a second timestamp corresponding to a second peak in the particular one of the first datasets, wherein the first peak is a current peak and the second peak is an immediately prior peak, and wherein the first timestamp defines a first time and the second timestamp defines a second time less than the first time; determining a rate based on an inverse of a difference between the first time and the second time; and configuring the rate as a particular estimate of the first estimates.

[0124] Clause 9 includes any of the preceding Clauses 1 to 8, where the determining the second datasets comprises: determining that a particular physiological signal of the physiological signals is less than a threshold value; determining that the particular physiological signal is a local minimum of a waveform including the particular physiological signal; and determining that a separation in time-domain between the local minimum and a second local minimum is equal to or greater than a threshold time interval, wherein the second local minimum immediately precedes the local minimum.

[0125] Clause 10 includes any of the preceding Clauses 1 to 9, where the determining the second estimates comprises: identifying a first timestamp corresponding to a first trough in a particular one of the second datasets; identifying a second timestamp corresponding to a second trough in the particular one of the second datasets, wherein the first trough is a current trough and the second trough is an immediately prior trough, and wherein the first timestamp defines a first time and the second timestamp defines a second time less than the first time; determining a rate based on an inverse of a difference between the first time and the second time; and configuring the rate as a particular estimate of the second estimates.

[0126] Clause 11 includes any of the preceding Clauses 1 to 10, where the vital sign is respiratory rate, and wherein the determining, using the first estimates and the second estimates, the observed estimate of the vital sign comprises: determining that a group of particular estimates satisfy a matching criterion, wherein the group of estimates includes a combination of a particular estimate of the first estimates, a second particular estimate of the first estimates, a third estimate of the first estimates, a particular estimate of the second estimates, a second particular estimate of the second estimates, or a third particular estimate of the second estimates; updating a group of matching estimates to include the group of particular estimates; determining a median of the group of matching estimates; and configuring the median as the observed estimate of the vital sign.

[0127] Clause 12 includes any of the preceding Clauses 1 to 11, where the vital sign is heart rate, and wherein the determining, using the first estimates and the second estimates, the observed estimate of the vital sign comprises: identifying, based on channel quality, a select accelerometer channel of the respective accelerometer channels; determining an average of a first particular estimate of the first estimates and a second particular estimate of the second estimates, wherein the first particular estimate and the second particular estimate correspond to the select accelerometer channel; and configuring the average as the observed estimate of the vital sign.

[0128] Clause 13 includes a computing device, comprising: at least one processor; and at least one memory device storing processor-executable instructions that, in response to being executed by one or a combination of the at least one processor, cause the computing device to perform or facilitate any one of the methods of Clauses 1 to 12.

[0129] Clause 14 includes at least one non-transitory computer-readable medium having instructions stored thereon that, in response to execution by one or a combination of at least one processor, cause a computing device to perform or facilitate any one of the methods of Clauses 1 to 12.

[0130] Clause 15 include a system, comprising: a low-noise accelerometer device configured to generate accelerometer signals representative of motion of subject, the motion corresponding to a vital bodily function of the subject; and a computing device functionally coupled with the low-noise accelerometer device, the computing device including: at least one processor; and at least one memory device storing processor-executable instructions that, in response to being executed by one or a combination of the at least one processor, cause the computing device to perform or facilitate any one of the methods of Clauses 1 to 12.

[0131] Clause 16 includes a computer-implemented method, comprising: generating, using accelerometer signals, physiological signals for a subject, wherein the accelerometer signals correspond to respective accelerometer channels of an accelerometer device mechanically coupled with a subject via a solid medium; determining, using the physiological signals, one or more of (i) first datasets indicative of peaks corresponding to respective accelerometer channels or (ii) second datasets indicative of troughs corresponding to respective accelerometer channels; determining, using one or more of (a) the first datasets or (b) the second datasets, multiple estimates of a vital sign of the subject; determining, using the multiple estimates, an observed estimate of the vital sign; and providing the observed estimate of the vital sign.

[0132] Clause 17 includes the preceding Clause 16, where the providing comprises causing a display device to present the observed estimate of the vital sign at a defined presentation rate.

[0133] Clause 18 includes any of the preceding Clauses 16 or 17, where the generating comprises detrending a first accelerometer signal of the accelerometer signals, resulting in a first detrended accelerometer signal.

[0134] Clause 19 includes any of the preceding Clauses 16 to 18, wherein the generating further comprises: filtering the first detrended accelerometer signal; and validating the filtered first detrended accelerometer signal, resulting in a first physiological signal of the physiological signals.

[0135] Clause 20 includes any of the preceding Clauses 16 to 19, where the vital sign is respiratory rate, and wherein the filtering comprises applying a low-pass filter having a cutoff frequency of about 0.2 Hz.

[0136] Clause 21 includes any of the preceding Clauses 16 to 20, wherein the vital sign is heart rate, and wherein the filtering comprises applying a band-pass filter having a lower cutoff frequency of about 1.0 Hz and a higher cutoff frequency of about 20 Hz.

[0137] Clause 22 includes any of the preceding Clauses 16 to 21, where the determining the first datasets comprises: determining that a particular physiological signal of the physiological signals exceeds a threshold value; determining that the particular physiological signal is a local maximum of a waveform including the particular physiological signal; and determining that a separation in time-domain between the local maximum and a second local maximum is equal to or greater than a threshold time interval, wherein the second local maximum immediately precedes the local maximum.

[0138] Clause 23 includes any of the preceding Clauses 16 to 22, where the determining the multiple estimates comprises: identifying a first timestamp corresponding to a first peak in a particular one of the first datasets; identifying a second timestamp corresponding to a second peak in the particular one of the first datasets, wherein the first peak is a current peak and the second peak is an immediately prior peak, and wherein the first timestamp defines a first time and the second timestamp defines a second time less than the first time; determining a rate based on an inverse of a difference between the first time and the second time; and configuring the rate as a particular estimate of the multiple estimates.

[0139] Clause 24 includes any of the preceding Clauses 16 to 23, where the determining the second datasets comprises: determining that a particular physiological signal of the physiological signals is less than a threshold value; determining that the particular physiological signal is a local minimum of a waveform including the particular physiological signal; and determining that a separation in time-domain between the local minimum and a second local minimum is equal to or greater than a threshold time interval, wherein the second local minimum immediately precedes the local minimum.

[0140] Clause 25 includes any of the preceding Clauses 16 to 24, wherein the determining the multiple estimates comprises: identifying a first timestamp corresponding to a first trough in a particular one of the second datasets; identifying a second timestamp corresponding to a second trough in the particular one of the second datasets, wherein the first trough is a current trough and the second trough is an immediately prior trough, and wherein the first timestamp defines a first time and the second timestamp defines a second time less than the first time; determining a rate based on an inverse of a difference between the first time and the second time; and configuring the rate as a particular estimate of the multiple estimates.

[0141] Clause 26 includes any of the preceding Clauses 16 to 25, wherein the vital sign is respiratory rate, and wherein the multiple estimates include one or more of (A) first estimates determined using the first datasets or (B) second estimates determined using the second datasets, determining, using the multiple estimates, determining, using the first estimates and the second estimates, the observed estimate of the vital sign comprises: determining that a group of particular estimates satisfy a matching criterion, wherein the group of estimates includes a combination of a particular estimate of the multiple estimates, a second particular estimate of the first estimates, a third particular estimate of the first estimates, a particular estimate of the second estimates, a second particular estimate of the second estimates, or a third particular estimate of the second estimates; updating a group of matching estimates to include the group of particular estimates; determining a median of the group of matching estimates; and configuring the median as the observed estimate of the vital sign.

[0142] Clause 27 incudes any of the preceding Clauses 16 to 26, where the vital sign is heart rate, and wherein the determining, using the multiple estimates, the observed estimate of the vital sign comprises: identifying, based on channel quality, a select accelerometer channel of the respective accelerometer channels; determining an average of a first particular estimate of the multiple estimates and a second particular estimate of the multiple estimates, wherein the first particular estimate and the second particular estimate correspond to the select accelerometer channel; and configuring the average as the observed estimate of the vital sign.

[0143] Clause 28 includes a computing device, comprising: at least one processor; and at least one memory device storing processor-executable instructions that, in response to being executed by one or a combination of the at least one processor, cause the computing device to perform or facilitate any one of the methods of Clauses 16 to 27.

[0144] Clause 29 includes at least one non-transitory computer-readable medium having instructions stored thereon that, in response to execution by one or a combination of at least one processor, cause a computing device to perform or facilitate any one of the methods of Clauses 16 to 27.

[0145] Clause 30 includes a system, comprising: a low-noise accelerometer device configured to generate accelerometer signals representative of motion of subject, the motion corresponding to a vital bodily function of the subject; and a computing device functionally coupled with the low-noise accelerometer device, the computing device including: at least one processor; and at least one memory device storing processor-executable instructions that, in response to being executed by one or a combination of the at least one processor, cause the computing device to perform or facilitate any one of the methods of Clauses 16 to 27.

[0146] Clause 31 includes a computing device, comprising: at least one processor; and at least one memory device storing processor-executable instructions that, in response to being executed by one or a combination of the at least one processor, cause the computing device at least to: generate, using accelerometer signals, physiological signals for a subject, wherein the accelerometer signals correspond to respective accelerometer channels of an accelerometer device mechanically coupled with a subject via a solid medium; determine, using the physiological signals, one or more of (i) first datasets indicative of peaks corresponding to respective accelerometer channels or (ii) second datasets indicative of troughs corresponding to respective accelerometer channels; determine, using one or more of (a) the first datasets or (b) the second datasets, multiple estimates of a vital sign of the subject; determine, using the multiple estimates, an observed estimate of the vital sign; and provide the observed estimate of the vital sign.

[0147] Clause 32 includes Clause 31, where providing the observed estimate comprises causing a display device to present the observed estimate of the vital sign at a defined presentation rate.

[0148] Clause 33 includes any of Clauses 31 or 32, where generating the physiological signals comprises detrending a first accelerometer signal of the accelerometer signals, resulting in a first detrended accelerometer signal.

[0149] Clause 34 includes any of Clauses 31 to 33, where the generating further comprises: filtering the first detrended accelerometer signal; and validating the filtered first detrended accelerometer signal, resulting in a first physiological signal of the physiological signals.

[0150] Clause 35 includes any of the preceding Clauses 31 to 34, where the vital sign is respiratory rate, and wherein the filtering comprises applying a low-pass filter having a cutoff frequency of about 0.2 Hz.

[0151] Clause 36 includes any of the preceding Clauses 31 to 35, where the vital sign is heart rate, and wherein the filtering comprises applying a band-pass filter having a lower cutoff frequency of about 1.0 Hz and a higher cutoff frequency of about 20 Hz.

[0152] Clause 37 includes any of the preceding Clauses 31 to 36, where determining the first datasets comprises: determining that a particular physiological signal of the physiological signals exceeds a threshold value; determining that the particular physiological signal is a local maximum of a waveform including the particular physiological signal; and determining that a separation in time-domain between the local maximum and a second local maximum is equal to or greater than a threshold time interval, wherein the second local maximum immediately precedes the local maximum.

[0153] Clause 38 includes any of the preceding Clauses 31 to 37, where determining the multiple estimates comprises: identifying a first timestamp corresponding to a first peak in a particular one of the first datasets; identifying a second timestamp corresponding to a second peak in the particular one of the first datasets, wherein the first peak is a current peak and the second peak is an immediately prior peak, and wherein the first timestamp defines a first time and the second timestamp defines a second time less than the first time; determining a rate based on an inverse of a difference between the first time and the second time; and configuring the rate as a particular estimate of the multiple estimates.

[0154] Clause 39 includes any of the preceding Clauses 31 to 38, where determining the second datasets comprises: determining that a particular physiological signal of the physiological signals is less than a threshold value; determining that the particular physiological signal is a local minimum of a waveform including the particular physiological signal; and determining that a separation in time-domain between the local minimum and a second local minimum is equal to or greater than a threshold time interval, wherein the second local minimum immediately precedes the local minimum.

[0155] Clause 40 includes any of the preceding Clauses 31 to 39, where determining the multiple estimates comprises: identifying a first timestamp corresponding to a first trough in a particular one of the second datasets; identifying a second timestamp corresponding to a second trough in the particular one of the second datasets, wherein the first trough is a current trough and the second trough is an immediately prior trough, and wherein the first timestamp defines a first time and the second timestamp defines a second time less than the first time; determining a rate based on an inverse of a difference between the first time and the second time; and configuring the rate as a particular estimate of the multiple estimates.

[0156] Clause 41 includes any of the preceding Clauses 31 to 40, where the vital sign is respiratory rate, and wherein the multiple estimates include one or more of (A) first estimates determined using the first datasets or (B) second estimates determined using the second datasets, determining, using the multiple estimates, determining, using the multiple estimates, the observed estimate of the vital sign comprises: determining that a group of particular estimates satisfy a matching criterion, wherein the group of estimates includes a combination of a particular estimate of the first estimates, a second particular estimate of the first estimates, a third estimate of the first estimates, a particular estimate of the second estimates, a second particular estimate of the second estimates, or a third particular estimate of the second estimates; updating a group of matching estimates to include the group of particular estimates; determining a median of the group of matching estimates; and configuring the median as the observed estimate of the vital sign.

[0157] Clause 42 includes any of the preceding Clauses 31 to 41, where the vital sign is heart rate, and wherein determining, using the multiple estimates, the observed estimate of the vital sign comprises: identifying, based on channel quality, a select accelerometer channel of the respective accelerometer channels; determining an average of a first particular estimate of the multiple estimates and a second particular estimate of the multiple estimates, wherein the first particular estimate and the second particular estimate correspond to the select accelerometer channel; and configuring the average as the observed estimate of the vital sign.

[0158] Clause 43 includes a system, comprising: a low-noise accelerometer device configured to generate accelerometer signals representative of motion of subject, the motion corresponding to a vital bodily function of the subject; and a computing device functionally coupled with the low-noise accelerometer device, the computing device including: at least one processor; and at least one memory device storing processor-executable instructions that, in response to being executed by one or a combination of the at least one processor, cause the computing device at least to: generate, using the accelerometer signals, physiological signals for a subject, wherein the accelerometer signals correspond to respective accelerometer channels of an accelerometer device mechanically coupled with a subject via a solid medium; determine, using the physiological signals, one or more of (i) first datasets indicative of peaks corresponding to respective accelerometer channels or (ii) second datasets indicative of troughs corresponding to respective accelerometer channels; determine, using one or more of (a) the first datasets or (b) the second datasets, multiple estimates of a vital sign of the subject; determine, using the multiple estimates, an observed estimate of the vital sign; and provide the observed estimate of the vital sign.

[0159] Clause 44 includes Clause 43, where providing the observed estimate comprises causing a display device to present the observed estimate of the vital sign at a defined presentation rate.

[0160] Clause 45 includes any of Clauses 43 and 44, where generating the physiological signals comprises detrending a first accelerometer signal of the accelerometer signals, resulting in a first detrended accelerometer signal.

[0161] Clause 46 includes any of the preceding Clauses 43 to 45, where the generating further comprises: filtering the first detrended accelerometer signal; and validating the filtered first detrended accelerometer signal, resulting in a first physiological signal of the physiological signals.

[0162] Clause 47 includes any of the preceding Clauses 43 to 46, where the vital sign is respiratory rate, and wherein the filtering comprises applying a low-pass filter having a cutoff frequency of about 0.2 Hz.

[0163] Clause 48 includes any of the preceding Clauses 43 to 47, where the vital sign is heart rate, and wherein the filtering comprises applying a band-pass filter having a lower cutoff frequency of about 1.0 Hz and a higher cutoff frequency of about 20 Hz.

[0164] Clause 49 includes any of the preceding Clauses 43 to 48, where determining the first datasets comprises: determining that a particular physiological signal of the physiological signals exceeds a threshold value; determining that the particular physiological signal is a local maximum of a waveform including the particular physiological signal; and determining that a separation in time-domain between the local maximum and a second local maximum is equal to or greater than a threshold time interval, wherein the second local maximum immediately precedes the local maximum. [0165] Clause 50 includes any of the preceding Clauses 43 to 49, where determining the multiple estimates comprises: identifying a first timestamp corresponding to a first peak in a particular one of the first datasets; identifying a second timestamp corresponding to a second peak in the particular one of the first datasets, wherein the first peak is a current peak and the second peak is an immediately prior peak, and wherein the first timestamp defines a first time and the second timestamp defines a second time less than the first time; determining a rate based on an inverse of a difference between the first time and the second time; and configuring the rate as a particular estimate of the multiple estimates.

[0166] Clause 51 includes any of the preceding Clauses 43 to 50, where determining the second datasets comprises: determining that a particular physiological signal of the physiological signals is less than a threshold value; determining that the particular physiological signal is a local minimum of a waveform including the particular physiological signal; and determining that a separation in time-domain between the local minimum and a second local minimum is equal to or greater than a threshold time interval, wherein the second local minimum immediately precedes the local minimum.

[0167] Clause 52 includes any of the preceding Clauses 43 to 51, wherein determining the multiple estimates comprises: identifying a first timestamp corresponding to a first trough in a particular one of the second datasets; identifying a second timestamp corresponding to a second trough in the particular one of the second datasets, wherein the first trough is a current trough and the second trough is an immediately prior trough, and wherein the first timestamp defines a first time and the second timestamp defines a second time less than the first time; determining a rate based on an inverse of a difference between the first time and the second time; and configuring the rate as a particular estimate of the multiple estimates.

[0168] Clause 53 includes any of the preceding Clauses 43 to 52, where the vital sign is respiratory rate, and wherein the multiple estimates include one or more of (A) first estimates determined using the first datasets or (B) second estimates determined using the second datasets, determining, using the multiple estimates, determining, using the multiple estimates, the observed estimate of the vital sign comprises: determining that a group of particular estimates satisfy a matching criterion, wherein the group of estimates includes a combination of a particular estimate of the first estimates, a second particular estimate of the first estimates, a third estimate of the first estimates, a particular estimate of the second estimates, a second particular estimate of the second estimates, or a third particular estimate of the second estimates; updating a group of matching estimates to include the group of particular estimates; determining a median of the group of matching estimates; and configuring the median as the observed estimate of the vital sign.

[0169] Clause 53 includes any of the preceding Clauses 43 to 53, where the vital sign is heart rate, and wherein determining, using the multiple estimates, the observed estimate of the vital sign comprises: identifying, based on channel quality, a select accelerometer channel of the respective accelerometer channels; determining an average of a first particular estimate of the multiple estimates and a second particular estimate of the multiple estimates, wherein the first particular estimate and the second particular estimate correspond to the select accelerometer channel; and configuring the average as the observed estimate of the vital sign.

[0170] Various aspects of the disclosure may take the form of an entirely or partially hardware aspect, an entirely or partially software aspect, or a combination of software and hardware. Furthermore, as described herein, various aspects of the disclosure (e.g., systems and methods) may take the form of a computer program product comprising a computer-readable non-transitory storage medium having computer-accessible instructions (e.g., computer-readable and/or computer-executable instructions) such as computer software, encoded or otherwise embodied in such storage medium. Those instructions can be read or otherwise accessed and executed by one or more processors to perform or permit the performance of the operations described herein. The instructions can be provided in any suitable form, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, assembler code, combinations of the foregoing, and the like. Any suitable computer-readable non-transitory storage medium may be utilized to form the computer program product. For instance, the computer-readable medium may include any tangible non-transitory medium for storing information in a form readable or otherwise accessible by one or more computers or processor(s) functionally coupled thereto. Non-transitory storage media can include read-only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory, and so forth.

[0171] Aspects of this disclosure are described herein with reference to block diagrams and flowchart illustrations of methods, systems, apparatuses and computer program products. It can be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by computer-accessible instructions. In certain implementations, the computer-accessible instructions may be loaded or otherwise incorporated into a general purpose computer, a special purpose computer, or another programmable information processing apparatus to produce a particular machine, such that the operations or functions specified in the flowchart block or blocks can be implemented in response to execution at the computer or processing apparatus.

[0172] Unless otherwise expressly stated, it is in no way intended that any protocol, procedure, process, or method set forth herein be construed as requiring that its acts or steps be performed in a specific order. Accordingly, where a process or method claim does not actually recite an order to be followed by its acts or steps or it is not otherwise specifically recited in the claims or descriptions of the subject disclosure that the steps are to be limited to a specific order, it is in no way intended that an order be inferred, in any respect. This holds for any possible nonexpress basis for interpretation, including: matters of logic with respect to the arrangement of steps or operational flow; plain meaning derived from grammatical organization or punctuation; the number or type of aspects described in the specification or annexed drawings; or the like.

[0173] As used in this disclosure, including the annexed drawings, the terms “component,” “module,” “interface,” “system,” and the like are intended to refer to a computer-related entity or an entity related to an apparatus with one or more specific functionalities. The entity can be either hardware, a combination of hardware and software, software, or software in execution. One or more of such entities are also referred to as “functional elements.” As an example, a component can be a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. For example, both an application running on a server or network controller, and the server or network controller can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which parts can be controlled or otherwise operated by program code executed by a processor. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can include a processor to execute program code that provides, at least partially, the functionality of the electronic components. As still another example, interface(s) can include I/O components or Application Programming Interface (API) components. While the foregoing examples are directed to aspects of a component, the exemplified aspects or features also apply to a system, module, and similar.

[0174] In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. Moreover, articles “a” and “an” as used in this specification and annexed drawings should be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.

[0175] In addition, the terms “example” and “such as” are utilized herein to mean serving as an instance or illustration. Any aspect or design described herein as an “example” or referred to in connection with a “such as” clause is not necessarily to be construed as preferred or advantageous over other aspects or designs described herein. Rather, use of the terms “example” or “such as” is intended to present concepts in a concrete fashion. The terms “first,” “second,” “third,” and so forth, as used in the claims and description, unless otherwise clear by context, is for clarity only and doesn't necessarily indicate or imply any order in time or space.

[0176] The term “processor,” as utilized in this disclosure, can refer to any computing processing unit or device comprising processing circuitry that can operate on data and/or signaling. A computing processing unit or device can include, for example, single-core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can include an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), a discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. In some cases, processors can exploit nano-scale architectures, such as molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment. A processor may also be implemented as a combination of computing processing units.

[0177] In addition, terms such as “store,” “data store,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component, refer to “memory components,” or entities embodied in a “memory” or components comprising the memory. It will be appreciated that the memory components described herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Moreover, a memory component can be removable or affixed to a functional element (e.g., device, server).

[0178] Simply as an illustration, nonvolatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, the disclosed memory components of systems or methods herein are intended to comprise, without being limited to comprising, these and any other suitable types of memory.

[0179] Various aspects described herein can be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. In addition, various of the aspects disclosed herein also can be implemented by means of program modules or other types of computer program instructions stored in a memory device and executed by a processor, or other combination of hardware and software, or hardware and firmware. Such program modules or computer program instructions can be loaded onto a general purpose computer, a special purpose computer, or another type of programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create a means for implementing the functionality of disclosed herein.

[0180] The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. For example, computer readable media can include but are not limited to magnetic storage devices (e.g., hard drive disk, floppy disk, magnetic strips, or similar), optical discs (e.g., compact disc (CD), digital versatile disc (DVD), blu-ray disc (BD), or similar), smart cards, and flash memory devices (e.g., card, stick, key drive, or similar).

[0181] What has been described above includes examples of one or more aspects of the disclosure. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing these examples, and it can be recognized that many further combinations and permutations of the present aspects are possible. Accordingly, the aspects disclosed and/or claimed herein are intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the detailed description and the appended claims. Furthermore, to the extent that one or more of the terms “includes,” “including,” “has,” “have,” or “having” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.