FIELD OF THE DISCLOSURE

The present disclosure is directed generally to infection risk assessment and workstation recommendation using Internet of Things (loT) sensors.

BACKGROUND

In open office settings impacted by the COVD-19 pandemic, social distancing, contact tracing, and disinfection will be necessary to control virus spread. Current contact tracing technology aims to detect person-to-person contacts that could lead to COVID-19 infection, such as contact with a potentially infectious individual less than 6 feet away from a user for 15 to 30 minutes. Many software applications have been developed to mitigate this risk. For example, some contact tracing systems rely on smartphones sending out anonymous Bluetooth (BLE) “chirps”. BLE chirps are random, rotating numbers which do not reveal from where or whom they were sent.

Along with contact tracing, social distancing and disinfection is increasingly becoming mandatory in open offices as a result of the pandemic. However, in practice, viruses are not only transmitted from person-to-person interactions, but also via shared surfaces. Hence, if an office worker occupies a flex desk previously used by an infected person, the worker may be exposed to infection risks. Similarly, a currently empty desk may have been contaminated by occupants of neighboring desks beyond the standard 6 feet of social distancing if the neighbor has coughed or sneezed a significant amount, transmitting the virus beyond the 6 foot distance.

As a smartphone-based BLE contact tracing app only tracks the instant contacts through BLE beacons, the empty desk’s potential accumulation of virus originating from adjacent desks across the time and multiple occupants will be missed. As such, social distancing and smartphone based BLE contact tracing only account for the instant situation; they are unable to account for if a desk has previously been used by an affected person, if the desk has been disinfected, if a desk has been empty long enough (e.g. over a weekend) so disinfection is not needed, or if the people sitting at neighboring desks, beyond the 6 feet of social distance, have coughed and/or sneezed. Accordingly, there is a need for a workstation infection risk assessment and recommendation tool which considers workstation occupancy history, workstation disinfection, and coughing and/or sneezing from occupants of neighboring workstations.

SUMMARY OF THE DISCLOSURE

The present disclosure is directed generally to infection risk assessment and workstation recommendation using Internet of Things (loT) sensors, such as temperature sensors configured to capture temperature data corresponding to a first workstation, and microphones configured to capture a plurality of audio signals corresponding to the plurality of workstations. The system uses this temperature data to determine a number of cleanliness factors. First, the system determines a vacancy time, representing the length of time the first workstation has been unoccupied. Second, the system determines a radiation disinfection score of the first workstation, representing the time, distance, and strength of the most recent ultraviolet (UV) or infrared (IR) disinfection event. Third, the system determines if and when the first workstation was manually cleaned. Fourth, if no cleaning or disinfection has occurred, the system determines an environmental disinfection score based on the temperature data and/or a relative humidity of the workstation’s environment. Based on these four cleanliness factors, the system determines a cleanliness score for the first workstation. Concurrently, the system also determines a health score for the first workstation based on coughs and/or sneezes detected in the audio signals. Based on the cleanliness score and health score, the system determines an infection risk score for the first workstation, representative of the risk a user would face by working at the first workstation. The system also displays a workstation recommendation, such as “THE FIRST WORKSTATION IS SAFE FOR USE” or “DO NOT USE THE FIRST WORKSTATION” based on the infection risk score. The system may also calculate infection risk scores for other workstations of the plurality of workstations, and recommend use of one of the other workstations instead of the first workstation.

Generally, in one aspect, a system for workstation recommendation based on infection risk is provided. The system may include a controller. The controller may be communicatively coupled to one or more temperature sensors. The temperature sensors may be configured to capture a temperature data set. The temperature data set may correspond to a first workstation of a plurality of workstations. According to an example, the one or more temperature sensors may be MPT sensors and/or PIR sensors. The controller may also be communicatively coupled to one or more microphones. The microphones may be configured to capture a plurality of audio signals. The audio signals may correspond to the plurality of workstations. According to an example, at least one of the temperature sensors and at least one of the microphones may be arranged in a luminaire.

The controller may be configured to determine a health score. The health score may be determined based on the plurality of audio signals.

According to an example, the controller may be further configured to detect one or more coughs and/or one or more sneezes in the plurality of audio signals. The controller may be further configured to determine a coughing direction and a coughing distance for each cough, and a sneezing direction and a sneezing distance for each sneeze. In this example, the health score may be further based on the coughs, sneezes, the coughing direction and the coughing distance of each cough, and the sneezing direction and the sneezing distancing of each sneeze.

The controller may be further configured to determine a vacancy time for the first workstation. The vacancy time may be determined based on the temperature data set.

The controller may be further configured to determine a radiation disinfection score for the first workstation. The radiation disinfection score may be determined based on an absorbing layer portion of the temperature data set. The absorbing layer portion of the temperature data set may correspond to an absorbing label. The absorbing label may be arranged on the first workstation. The absorbing label may include a reflecting portion and an absorbing portion. The reflecting portion may be aluminum.

The controller may be further configured to determine a manual cleaning status for the first workstation. The manual cleaning status may be based on the temperature data set. According to an example, the controller may be further configured to generate a major motion count. The major motion count may be based on the temperature data set. In this example, the manual cleaning status for the first workstation may be further based on the major motion count.

The controller may be further configured to determine an environmental disinfection score for the first workstation. The environmental disinfection score may be based on the temperature data set and/or a humidity data set. According to an example, the controller may be communicatively coupled to one or more humidity sensors. The one or more humidity sensors may be configured to capture the humidity data set.

The controller may be further configured to determine a cleanliness score for the first workstation. The cleanliness score may be based on the vacancy time, the radiation disinfection score, the manual cleaning status, and the environmental disinfection score. The controller may be further configured to determine a first infection risk score for the first workstation. The first infection risk score may be based on the health score and the disinfection score.

According to an example, the controller may be further configured to capture, via the temperature sensors, a previous occupant temperature data set. The controller may be further configured to generate a previous occupant temperature profile. The previous occupant temperature profile may be based on the previous occupant temperature data set. In this example, the workstation recommendation may be further based on the previous occupant temperature profile and a user temperature profile.

The controller may be further configured to display a workstation recommendation. The workstation recommendation may be based on the first infection risk score. The workstation recommendation may be displayed via a user interface. According to an example, the workstation recommendation may be further based on a second infection risk score for a second workstation.

According to another aspect, a method for workstation recommendation based on infection risk is provided. The method may include capturing, via one or more microphones, a plurality of audio signals corresponding to a plurality of workstations. The method may further include determining, via a controller, a health score based on the plurality of audio signals. The method may further include capturing, via one or more temperature sensors, a temperature data set corresponding to a first workstation of the plurality of workstations. The method may further include determining, via the controller, a vacancy time for the first workstation based on the temperature data set. The method may further include determining, via the controller, a radiation disinfection score for the first workstation based on an absorbing layer portion of the temperature data set, wherein the absorbing layer portion of the temperature data set corresponds to an absorbing label arranged on a surface of the first workstation. The method may further include determining, via the controller, a manual cleaning status for the first workstation based on the temperature data set. The method may further include determining, via the controller, an environmental disinfection score for the first workstation based on the temperature data set and/or a humidity data set. The method may further include determining, via the controller, a cleanliness score for the first workstation based on the vacancy time, the radiation disinfection score, the manual cleaning status, and the environmental disinfection score. The method may further include determining, via the controller, a first infection risk score for the first workstation based on the health score and the cleanliness score. The method may further include displaying, via a user interface, a workstation recommendation based on the first infection risk score.

In various implementations, a processor or controller may be associated with one or more storage media (generically referred to herein as “memory,” e.g., volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM, floppy disks, compact disks, optical disks, magnetic tape, etc.). In some implementations, the storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform at least some of the functions discussed herein. Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into a processor or controller so as to implement various aspects as discussed herein. The terms “program” or “computer program” are used herein in a generic sense to refer to any type of computer code (e.g., software or microcode) that can be employed to program one or more processors or controllers.

It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. It should also be appreciated that terminology explicitly employed herein that also may appear in any disclosure incorporated by reference should be accorded a meaning most consistent with the particular concepts disclosed herein.

These and other aspects of the various embodiments will be apparent from and elucidated with reference to the embodiment s) described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the same parts throughout the different views. Also, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the various embodiments.

FIG. l is a top-level schematic of a system for infection risk assessment and workstation recommendation, in accordance with an example.

FIG. 2 is a schematic of a luminaire in a system for infection risk assessment and workstation recommendation, in accordance with an example. FIG. 3 is a schematic of a controller in a system for infection risk assessment and workstation recommendation, in accordance with an example.

FIG. 4 is a schematic of a workstation in a system for infection risk assessment and workstation recommendation, in accordance with an example.

FIG. 5 is a layout of an environment monitored for social distancing, in accordance with an example.

FIGS. 6A and 6B show heat maps of an environment with a laptop in active use, and a laptop in sleep mode, respectively.

FIG. 7 is a first portion of a flowchart of a method for infection risk assessment and workstation recommendation, in accordance with an example.

FIG. 8 is a second portion of a flowchart for a method for infection risk assessment and workstation recommendation, in accordance with an example.

DETAILED DESCRIPTION OF EMBODIMENTS

The present disclosure is directed generally to infection risk assessment and workstation recommendation using Internet of Things (loT) sensors, such as temperature sensors configured to capture temperature data corresponding to a first workstation, and microphones configured to capture a plurality of audio signals corresponding to the plurality of workstations. The temperature sensors may be multipixel thermopile (MPT) sensors and/or passive infrared (IR) sensors embedded in a luminaire above the first workstation. The system uses this temperature data to determine a number of cleanliness factors. First, the system determines a vacancy time, representing the length of time the first workstation has been unoccupied. Second, the system determines a radiation disinfection score of the first workstation, representing the time, distance, and strength of the most recent ultraviolet (UV) or infrared (IR) disinfection. The radiation disinfection score may be determined by analyzing the temperature of an absorbing label arranged on a surface of the first workstation. Third, the system determines if and when the first workstation was manually cleaned. Fourth, if no disinfection has occurred, the system determines an environmental disinfection score based on the temperature data and/or a relative humidity of the workstation’s environment. Based on these four cleanliness factors, the system determines a cleanliness score for the first workstation. Concurrently, the system also determines a health score for the first workstation based on coughs and/or sneezes detected in the audio signals. Based on the cleanliness score and health score, the system determines an infection risk score for the first workstation, representative of the risk a user would face by working at the first workstation. The infection risk score may also factor in whether the user was the most recent occupant of the first workstation based on a temperature profile. The system also displays a workstation recommendation, such as “THE FIRST WORKSTATION IS CLEAR FOR USE” or “DO NOT USE THE FIRST WORKSTATION” based on the infection risk score. The system may also calculate infection risk scores for other workstations of the plurality of workstations, and recommend use of one of the other workstations instead of the first workstation.

Generally, in one aspect, a system 100 for workstation recommendation based on infection risk is provided. Broadly, and with reference to FIG. 1, the system 100 may include a controller 102, one or more luminaires 166, a network 400, and a user interface 136. With reference to FIGS. 1 and 2, each of the luminaires 166 may include components such as temperature sensors 104, microphones 106, light sources 172, humidity sensors 170, and/or transceivers 420. The sensors 104, 170 and microphones 106 may be packaged together as part of a sensor bundle, such as an Advanced Sensor Bundle (ASB). The controller 102 may be capable of communication with the components of the luminaires 166 via wired or wireless network 400. FIG. 1 depicts one example system 100 which includes three luminaires 166a-c, each luminaire 1 lOa-c having a temperature sensor 104a-c, microphone 106a-c, light source 172a-c, humidity sensor 170, and transceiver 420a-c.

With reference to FIGS. 1 and 3, the controller 102 may include a memory 250, a processor 300, and a transceiver 410. The memory 250 and processor 300 may be communicatively coupled via a bus to facilitate processing of data stored in memory 250. Transceiver 410 may be used to receive data from the temperature sensors 104 and microphones 106 via the network 400. The data received by the transceiver 410 may be stored in memory 250 and/or processed by processor 300. In an example, the transceiver 410 may facilitate a wireless connection between the controller 102 and the network 400. The transceiver 410 may also be used to operate the light sources 172 of the luminaires 166.

The network 400 may be configured to facilitate communication between the controller 102, the one or more temperature sensors 104, the one or more microphones 106, the one or more light sources 172, the one or more humidity sensors 170, and/or any combination thereof. The network 400 may be a wired and/or wireless network following communication protocols such as cellular network (5G, LTE, etc.), Bluetooth, Wi-Fi, Zigbee, and/or other appropriate communication protocols. In an example, the temperature sensors 104 may wirelessly transmit, via the network 400, a temperature data set 112 to the controller 102 for storage in memory 250 and/or processing by the processor 300. With reference to FIGS. 1 and 5, the controller 102 may be communicatively coupled to one or more temperature sensors 104. The temperature sensors 104 may be configured to capture a temperature data set 112. The temperature data set 112 may correspond to a first workstation 110 of a plurality of workstations 108. According to an example, the one or more temperature sensors 104 may be MPT sensors and/or PIR sensors. In a further example, the one or more temperature sensors may be IR cameras. The temperature data set 112 may include data captured at a plurality of time intervals throughout a monitoring period. For example, the temperature data set 112 may include data captured over a 24-hour period. Other data capture periods may be used depending on the particulars of the monitored environment.

In a preferred example, and with reference to FIG. 5, the plurality of workstations 108 under assessment are arranged in an open office environment. However, the system 100 may be employed in any environment in which a user desires to occupy a workstation-like area with other users within a larger space. Accordingly, the system 100 may be utilized to assess infection risk and offer recommendations in environments such as libraries, warehouses, manufacturing centers, assembly lines, slaughterhouse, gyms, and restaurants. In the example, of a gym, the system 100 may be able to assess the infection risk to a user seeking to use a first workstation 110, such as a weight machine or treadmill. In these environments, a user entering the environment may interact with the system via a user interface 136, such as a mobile application on their smartphone or a standalone computer terminal at the entrance to the environment. The user may select the first workstation 110 on the user interface 136 to receive a corresponding workstation recommendation 136, such as “DO NOT USE THE FIRST WORKSTATION” or “PLEASE PROCEED TO THE FIRST WORKSTATION”. Alternatively, the workstation recommendation 136 may suggest an alternate workstation for use, such as “PLEASE PROCEED TO THE SECOND WORKSTATION”. In a further example, the user interface 136 may contain a list (or a map view) of every workstation 108 in the environment with workstation recommendations 136 for each workstation 108.

In certain embodiments, the system 100 may perform an initial occupancy check to determine if the first workstation 110 is currently occupied by another user. In these instances, the user interface 136 may inform the user that the first workstation 110 is OCCUPIED, and thus unavailable for use. The initial occupancy check may be performed by analyzing the temperature data set 112. Workstations 108 determined to be occupied by the initial occupancy check may still be evaluated for infection risk, but will not be recommended to the user for immediate use.

In a further example, additional temperature data sets may be captured corresponding to other workstations 108, such as the second 164, third 174, or fourth 176 workstations shown in FIG. 5. In FIG. 5, two temperature sensors 104a, 104b with corresponding fields of view monitor an open office environment with four workstations 108. As will be described below, and as shown in FIG. 4, an absorbing label 124 is arranged on each workstation 108. This absorbing label 124 is used by the system 100 to determine if the workstation 108 has been disinfected via UV or IR radiation.

In one example, the temperature data set 112 may only consist of data captured by a single temperature sensor 104. For example, in FIG. 5, the temperature data set 104 corresponding to the first workstation 110 may only include data captured by temperature sensor 104a, as temperature sensor 104a is the only temperature sensor 112 with the first workstation 110 within its field of view. In a further example, the temperature data set 112 for another workstation may include data collected by two or more temperature sensors 104. For example, in FIG. 5, the temperature data set corresponding to the second workstation 164 may include data collected by both the first 104a and second 104b temperature sensors, as each of these temperature sensors have the second workstation 164 within their respective fields of view.

The temperature data set 112 may be used to generate a series of heat maps as shown in FIG. 6. If more than one temperature sensor 104 is used to capture the temperature data set 112, the controller 104 may use an image stitching algorithm to combine data from each sensor 104 and generate a composite heat map. As shown in FIG. 6, a laptop in active use generates significantly more heat than a laptop in sleep mode. This indication of a laptop in active use may be used by the system 100 in its initial occupancy check of a workstation 108. Thus, if a heat map shows an active laptop on the first workstation 110, the user interface 136 may inform the user that the first workstation 110 is OCCUPIED, and thus unavailable for use.

The controller 102 may also process the temperature data set 112 into a series of motion counts, such as minor motion count, medium motion count, and major motion count. A minor motion may correspond to the normal motions of a worker sitting at their workstation, such as typing on a keyboard, moving a mouse, or talking into a telephone. A medium motion may correspond to a worker standing up or sitting down. A major motion may correspond to a worker entering or exiting their workstation area. As will be described below, these motion counts may be analyzed to determine if the monitored workstation 108 has been manually cleaned.

The controller 102 may also be communicatively coupled to one or more microphones 106. The microphones 106 may be configured as one or more arrays of microphones 106. For example, one “microphone” may include several microphone array elements. The microphones 106 may be configured to capture a plurality of audio signals 114. The audio signals 114 may correspond to the plurality of workstations 108. According to an example, the controller 102 may be further configured to detect one or more coughs 144 and/or one or more sneezes 146 in the plurality of audio signals 114. The controller 102 may be further configured to determine a coughing direction 148 and a coughing distance 150 for each cough 144 relative to the first workstation 110, and a sneezing direction 152 and a sneezing distance 154 for each sneeze 146 relative to the first workstation 110. By monitoring coughs 144 and sneezes 146 occurring in the environment, originating both from occupants of the first workstation 110 as well as neighboring workstations, the system 100 may assess the risk of aerosol viral transmission to the future occupants for the first workstation 110. The system 100 may identify coughs 144 and/or sneezes 146 in the audio signals using binary classification systems or other machine learning processes. The sneezing direction 152, sneezing distance 154, coughing direction 148, and coughing distance 150 may be determined based on the audio arrival angle of the audio signals 114 at the microphones 106 and further machine learning algorithms. In a further example, the sneezing direction 152 and/or the coughing direction 148 may be determined based on a fusion of the audio signals 114 and the temperature data set 112.

The controller 102 may be configured to determine a health score 116. The health score 116 may be determined based on the plurality of audio signals 114. According to an example, the health score 116 may be further based on the coughs 144, sneezes 146, the coughing direction 148 and the coughing distance 150 of each cough 144, and the sneezing direction 152 and the sneezing distance 154 of each sneeze 146. According to an example, the health score 116 for a workstation k for coughs 144 and sneezes 146 originating at workstations i may be calculated using the following equation: where the coughs 144 and/or sneezes 146 originate from workstation z, Ncough is the number of coughs in the time window t (such as 30 minutes), N _sne eze is number of sneezes in the time window /, d(k,i) is the distance between workstation k and workstation z, (k,i) is the relative direction of the cough 144 or sneeze 146 relative to workstation k. In this equation, coughs 144 and sneezes 146 which are closer in distance to the first workstation 110 are weighted more heavily than coughs 146 and sneezes 148 from a further distance away. For example, with reference to FIG. 5, a cough 144 from the second workstation 164 will likely be weighted more heavily than a cough 144 from the fourth workstation 176 when evaluating the health score 116 of the first workstation 110. Similarly, a cough 144 with a coughing direction 148 towards the first workstation 110 will likely be weighted more heavily than a cough 144 with a coughing direction 148 away from the first workstation 110.

In an example, the detected cough 144 or sneeze 146 may originate from the workstation 108 under analysis. Using equation 1, the coughs 144 and sneezes 146 originating from a first workstation 110 will likely be weighted significantly more heavily than coughs 144 or sneezes 146 from the second 164, third 174, or fourth 176 workstations when determining the health score 116 of the first workstation 110.

The health score 116 analyses of the audio signals 114 may be calibrated based on the expected behavior of users at the first workstation 110.

The controller 102 may be further configured to determine a vacancy time 118 for the first workstation 110. By determining the vacancy time 118 and comparing the vacancy time 118 to the lifetime of viruses on workstation surfaces, the system 100 can determine if cleaning or disinfection is required. For example, if the vacancy time 118 of the first workstation 110 is 24 hours, cleaning or disinfection may not be needed.

The vacancy time 118 may be determined based on the temperature data set 112. By analyzing the temperature data around the first workstation 110 for human temperature profiles, the system 100 can determine when the first workstation 110 was last occupied. Alternatively, the system 100 can also make this determination by analyzing the heat emitted by electronics at the first workstation 110, such as a computer desktop or laptop. As shown in FIG. 6, a laptop in active use emits significantly more heat than a laptop in sleep mode. Accordingly, the heat discrepancy between active and sleep mode may be used to determine whether the workstation 110 is occupied (laptop active mode) or unoccupied (laptop in sleep mode).

The controller 102 may be further configured to determine a radiation disinfection score 120 for the first workstation 110. The disinfection score 120 represents the quality of an irradiative UV or IR disinfection event upon the first workstation 100. The radiation disinfection score 120 may be based on the following equation:

^radiation ( ^— h(t,S, d) (2) where t represents the time of the disinfection event, .s represents the strength of the disinfection event as captured by the temperature sensors 106, and d represents the distance from the disinfection source to the first workstation 110. In one example, the disinfection may be performed by a UV or IR light source arranged on the ceiling above the workstations 108. In other examples, the disinfection may be performed by an automated robot with a UV or IR light source.

In a preferred example, the disinfection source utilizes intense, short-duration pulses of UV or IR light. These pulses improve germicidal effects upon workstation 108 surfaces through both photochemical and photothermal processes. As a result, several high intensity flashes of broad-spectrum light pulses per second can render microbes inactive rapidly and effectively. Specifically, far-infrared (FIR) light rays are absorbed by certain materials immediately, and these light pulses lead to pronounced temperature-rises and temperature-cool downs resulting in deactivation of viruses and bacteria.

The aforementioned pulsed UV or IR disinfection event will lead to a significant temperature rise of a wet surface, for instance a workstation 108 recently wiped down by a cleaning person. In a preferred example, when preparing a room ready for the next occupants, a cleaning person may first wipe the workstation 108, and then activate a 254 nm UV disinfecting light source. With reference to FIG. 4, an absorbing label 124 may be arranged on the first workstation 110 to aid in the evaluation of the strength of this UV disinfection event. The absorbing layer 124 may include a highly UV or IR absorbent thin film on top of a thermally insulating layer.

To evaluate the strength of the disinfection event, the system 100 may analyze an absorbing layer portion 122 of the temperature data set 112. The absorbing layer portion 122 of the temperature data set 112 represents the area of the first workstation 110 covered by the absorbing label 124. Accordingly, the system 100 may analyze the dynamic temperature rise and drop of the absorbing layer portion 122, which represents the temperature change of the absorbing label 124 as a result of the pulsed UV or IR disinfection lighting.

Disinfection lighting is typically administered at high doses of energy, such as doses up to 500mJ/cm ² . When utilizing UV disinfection, high peak radiance may be achieved by pulsing a xenon flash. Xenon flashes may be administered by hospital disinfection robots. Due to the very high peak radiance of the Xenon flash, the heating pattern of the to-be- monitored surface will be pronounced. To determine the strength of the pulsed Xenon flashes, the dynamic heating and cooling pattern of the absorbing label 124 may be monitored. As described above, the absorbing label 124 may be purposefully placed on the target surface, such as the surface of the workstation 110. The absorbing label 124 may consist of a very thin metal film laminated on a thermally isolating tape. During a disinfection event, the strong UV disinfection pulses easily heat up the thin sheet of metal due to its low thermal mass.

According to an example, the absorbing label 124 may include a reflecting portion 140 and an absorbing portion 142. The reflecting portion 142 may be aluminum. Aluminum is an excellent UV reflector, as it is the only material with a high reflectivity for UV light in the range of 250 nm to 400 nm. Accordingly, aluminum foil that is lightweight and has high workability may be suitable as the ultraviolet reflecting material. Aluminum sputtered on glass or another material may also be a suitable reflecting material. Further, expanded Poly TetraFluor Ethylen (e-PTFE) also has a high reflectivity, making it a suitable reflecting material. On the other hand, stainless steel has a relative low reflectivity, making it unsuitable as the reflecting material.

The absorbing portion 142 may be composed of an organic and/or non- metallic absorbent material. All organic materials, and many non-metallic materials, absorb greater than 95% of incident UV light. The reflection portion 140 and absorbing portion 142 may each be arranged as narrow stripes. Accordingly, during a disinfection event, the absorbing portion 142 rises in temperature when illuminated by UV light, while the temperature of the reflecting portion 140 remains relatively stable, enabling the system 100 to perform a differential temperature measurement analysis between the absorbing portion 142 and the reflecting portion 140. For instance, if the system 100 sees a dynamic temperature differential pattern between the absorbing portion 142 and reflecting portion 140, it can conclude that the disinfection light is successfully reaching the target surface on the first workstation 110 and is not shielded by obstacles.

Alternatively, the system 100 may analyze other portions of the temperature data set 112 to determine if a UV or IR disinfection event has occurred. For example, if the first workstation 110 lacks an absorbing label 124, the system 100 may analyze temperature increases in other objects placed on the first workstation 110, such as the leaves on a plant during IR disinfection. In this example, the system 100 may identify a plant portion of the temperature data set 112 corresponding to the placement of the plant on the first workstation 110, and analyze the temperature increase in the plant portion to determine if IR disinfection has taken place. In further examples, other objects placed on the first workstation 110 may be utilized to detect UV or IR disinfection, assuming the objects absorb UV or IR radiation in a measurable manner.

In an even further example, portions of the first workstation 110 itself may be used to detect UV or IR disinfection. For example, the first workstation 110 may be a desk with a solid aluminum frame characterized by high UV reflectance. The surface of the desk may be a thin layer of UV absorbing material on top of an insulating layer. Accordingly, similar to the label 124 with reflecting 140 and absorbing 142 portions, the system 100 may analyze the temperature of the aluminum frame and the absorbing surface for a dynamic temperature pattern indicative of a UV disinfection event.

In a further example, the system 100 may be configured to detect a disinfection lighting event which utilizes broadband disinfection lighting. The broadband disinfection lighting may include light of a wide array of wavelengths, including UV, IR, and/or visible light. For example, the broadband disinfection lighting may emit a range of UV light with wavelengths from 200 nm to 280 nm, covering the entire germicidal UV spectrum.

In a further example, the broadband disinfection lighting system may be a Xenon flash system emitting UV, IR, and visible light. Such wide ranging Xenon flash systems are often used in hospital settings. In order to detect Xenon broadband disinfection, an absorbing label 124 may be used with two portions: a first portion to absorb UV light, and a second portion to absorb IR light.

The controller 102 may be further configured to determine a manual cleaning status 126 for the first workstation 110. The manual cleaning status 126 represents whether the first workstation 110 has been manually disinfected by a cleaning person. The manual cleaning status may be determined based on the temperature data set 112.

According to an example, and as described above, the controller 102 may be further configured to generate a major motion count 168 based on the temperature data set 112. In this example, the manual cleaning status 126 for the first workstation 110 may be based on the major motion count 168. The system 100 may determine the manual cleaning status 126 based on the major motion count 168 in a time window (such as five minutes) because a cleaning service will typically require a cleaning person to stand close to the workstation with many major motions (such as entering and exiting the area around the first workstation 110, moving cleaning equipment, wiping down the surfaces of the workstation, etc.). The activity pattern of the workstation surface wiping will be distinctly different from a person performing computer work at the workstation, which will generate many minor motions (such as typing on keyboard, etc.) rather than major motions. In order to detect manual cleaning based on the major motion count, temperature data sets of a workstation cleaning service may be captured and transformed into training major motion counts. The training major motion counts may be used to train a neural network such as a recurrent neural network (RNN) to detect manual cleaning.

In an alternative example, the manual cleaning status 126 may be determined by analyzing the temperature of a recently cleaned surface, rather than by motion count analysis. For example, a wet surface, such as a surface manually wiped down with disinfectant, absorbs IR radiation to a higher degree than a dry surface. Accordingly, the manual cleaning status 126 may be determined by analyzing the portion of the temperature data set 112 corresponding to the surface of the first workstation 110 receiving IR radiation.

In another alternative example, the manual cleaning status 126 may be determined based upon the plurality of audio signals 114. In this example, the system 100 may utilize an artificial intelligence algorithm to detect sounds associated with the cleaning process (such as vacuuming, etc.).

In another alternative example, the system 100 may include one or more volatile organic compound (VOC) sensors. The VOC sensors may be used to detect disinfectant and/or detergent applied during the manual cleaning process.

The controller 102 may be further configured to determine an environmental disinfection score 128 for the first workstation 110. The environmental disinfection score 128 is an evaluation of how viruses and bacteria on the surface of the first workstation 110, which has not been manually cleaned or disinfected via a light source, have survived based on environmental factors. For example, the environmental disinfection 128 may be based on the temperature data set 112 and a humidity data set 130. According to an example, the environmental disinfection score 128 may be determined based on the following equation: environmental (t) = /(t, T, RH) (3) where t represents vacancy time 118, T represents the temperature of the environment, and RH represents the relative humidity in the environment. The temperature of the environment may be derived from the temperature data set 112, while the relative humidity may be derived from the humidity data set 130. According to an example, the controller 102 may be communicatively coupled to one or more humidity sensors 170 configured to capture the humidity data set 130. In another example, the environmental temperature and relative humidity may be provided by an external source, such as a thermostat. In further examples, the environmental disinfection score may be calculated based on solely on either the temperature data set 112 or the humidity data set. The controller 102 may be further configured to determine a cleanliness score 132 for the first workstation 110. The cleanliness score 132 may be based on the vacancy time 118, the radiation disinfection score 120, the manual cleaning status 126, and the environmental disinfection score 128. According to an example, the cleanliness score 128 may be determined based on t (he following equation: 1, vacant clean workstation h(t, s, d), rad. disinfection u( zt.>)., manua il cileani-ng ( ^v 4) ⁷ f t, T, RH), env. disinfection

Under this equation, the cleanliness score is 1 if the vacancy time 118 exceeds the lifetime of viruses or bacteria on the surface of the first workstation 110. If the vacancy time 118 does not exceed this lifetime, the equation may evaluate the manual cleaning status 126 to determine if the workstation 108 has been manually disinfected. If so, the cleanliness score may also be set to 1.

If the first workstation 110 has not been manually disinfected, the cleanliness score 128 may be based on the radiation disinfection score 120. As described above, the radiation disinfection score 120 evaluates the quality of UV or IR disinfection.

If the first workstation 110 has not been disinfected manually or radiatively, the cleanliness score 128 may be based on the environmental disinfection score 128. As described above, the environmental disinfection score 128 evaluates the effect of temperature and relative humidity on any viruses or bacteria present on the surface of the first workstation 110 over time.

The controller 102 may be further configured to determine a first infection risk score 134 for the first workstation 110. The first infection risk score 134 may be based on the health score 116 and the disinfection score 132. The first infection risk score 134 represents the viral or bacterial risk a user may face while working at the first workstation 110. The infection risk score 134 is not a measurement of viruses or bacteria actually present on or near the first workstation 110, but rather a risk estimate based on the cleanliness of the first workstation 110, as well as the presumed health of others around the first workstation 110.

According to an example, the first infection risk score 134 may be determined based on the following equation:

In this example, an infection risk score 134 may be between 0 and 1, with a score 134 close to 1 representing a workstation 108 relatively risk free, and a score 134 close to 0 representing a very risky workstation. The controller 102 may be further configured to display a workstation recommendation 138 based on the first infection risk score 134. In this example, the system 100 may compare the first infection risk 134 to a risk threshold to determine whether or not to recommend the first workstation 110 to a user.

According to an example, the workstation recommendation 138 may be displayed via a user interface 136. The user interface 136 could also display a textual or graphical representation of the workstation recommendation, such as “THE FIRST WORKSTATION IS CLEAR FOR USE” or “DO NOT USE THE FIRST WORKSTATION”.

According to an example, the workstation recommendation 134 may be further based on the previous occupant temperature profile 158 and a user temperature profile 160. The temperature profiles are analyzed to ensure that a user who has previously used the first workstation 110 is recommended to use the first workstation 110, rather than another of the plurality of workstations 108. By ensuring users consistently use the same workstations 108, infection spread may be further limited.

The previous occupant temperature profile 158 represents the most recent occupant of the first workstation 110, while the user temperature profile 160 represents the user seeking to occupy one of the plurality of workstations 108, such as the first workstation. The previous occupant temperature profile 158 may be based on a previous occupant temperature data set 156 captured by the temperature sensors 104. Similarly, the user temperature profile 160 may be also be based on temperature data captured by the temperature sensors 104. Alternatively, the user temperature profile may have been saved in memory 250 from a previous workstation recommendation 138 analysis. The previous occupant temperature profile 158 and the user temperature profile 160 may be characterized by peak temperature, cluster size, and/or any other temperature information which may be used to identify a unique user.

According to an example, the workstation recommendation 138 may be determined based on the following equation: where p is the user temperature profile 160, pk is the previous occupant temperature profile 158, and 5(p — p _fc ) determines whether the temperature profiles 158, 160 represent the same user. If the temperature profiles 158, 160 represent the same user, the first workstation 110 will typically be recommended to the user even if it has not been disinfected or cleaned since last use. However, in some instances, the risk score S _ns k(t,k) of the first workstation 110 at time t may be significantly lower than the risk score at a previous time t _prev . This lower risk score 134 may be indicative of increased infection risk to a potential user due to new environmental factors, such as new occupants of neighboring workstations 108 who are coughing and/or sneezing. In instances where the risk score Srisk(t, k) has decreased significantly, the system 100 will consider assigning a new workstation 108 to the user, even if they had previously used the first workstation 110.

According to an example, the workstation recommendation 138 may be further based on a second infection risk score 162 for a second workstation 164. The second infection risk score 162 may be calculated in a similar manner as the first infection risk score 134. If the second infection risk 162 is lower than the first infection risk score 134, and the user has not used either the first 110 or second 164 workstations, the workstation recommendation 138 should result in a “THE SECOND WORKSTATION IS CLEAR FOR USE” or similar message on the user interface 136. In a related example, infection scores 134 for each of the plurality of workstations 108 may be continuously calculated. In this example, the user interface 136 may continuously provide workstation recommendations 138 for each of the plurality of workstations 108. The user interface may provide the continuously updated workstation recommendations 108 on a map of the environment depicting the physical location of each workstation 108.

By continually calculating infection risk scores 134 for each of the plurality of workstations 108, the system 100 with K available workstations 108 may continually generate a pool of available (empty) workstations Ek, k = 0, ... K-l), whose scores 134 meet a certain threshold (such as greater than 0.9). If a new user enters the environment, the algorithm will recommend a workstation 108 using a minimax algorithm to maximize the distance to existing occupied workstations (O i = 0, . . . A-l) as shown in the equation below index _k = argmax k

According to another aspect, and with reference to FIGS. 7 and 8, a method 500 for workstation recommendation based on infection risk is provided. The method may include capturing 502, via one or more microphones, a plurality of audio signals corresponding to a plurality of workstations. The method 500 may further include determining 504, via a controller, a health score based on the plurality of audio signals. The method 500 may further include capturing 506, via one or more temperature sensors, a temperature data set corresponding to a first workstation of the plurality of workstations. The method 500 may further include determining 508, via the controller, a vacancy time for the first workstation based on the temperature data set. The method 500 may further include determining 510, via the controller, a radiation disinfection score for the first workstation based on an absorbing layer portion of the temperature data set, wherein the absorbing layer portion of the temperature data set corresponds to an absorbing label arranged on a surface of the first workstation. The method 500 may further include determining 512, via the controller, a manual cleaning status for the first workstation based on the temperature data set. The method 500 may further include determining 514, via the controller, an environmental disinfection score for the first workstation based on the temperature data set and/or a humidity data set. The method 500 may further include determining 516, via the controller, a cleanliness score for the first workstation based on the vacancy time, the radiation disinfection score, the manual cleaning status, and the environmental disinfection score. The method 500 may further include determining 518, via the controller, a first infection risk score for the first workstation based on the health score and the cleanliness score. The method 500 may further include displaying 520, via a user interface, a workstation recommendation based on the first infection risk score.

All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.

The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified.

As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of’ or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.”

As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified.

It should also be understood that, unless clearly indicated to the contrary, in any methods claimed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited.

In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of’ and “consisting essentially of’ shall be closed or semi-closed transitional phrases, respectively.

The above-described examples of the described subject matter can be implemented in any of numerous ways. For example, some aspects may be implemented using hardware, software or a combination thereof. When any aspect is implemented at least in part in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single device or computer or distributed among multiple devices/computers.

The present disclosure may be implemented as a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non- exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user’s computer, partly on the user's computer, as a stand-alone software package, partly on the user’ s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some examples, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to examples of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

The computer readable program instructions may be provided to a processor of a, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various examples of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

Other implementations are within the scope of the following claims and other claims to which the applicant may be entitled.

While various examples have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the examples described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific examples described herein. It is, therefore, to be understood that the foregoing examples are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, examples may be practiced otherwise than as specifically described and claimed. Examples of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.