Background

With increasing availability of sensor elements that become cheaper, smaller and smarter, the application area of such sensor units also increases. Specifically in health applications, sensors are implemented as wearables in different products, such as smart watches, shoes or even clothes. These application areas of sensors are mainly for health tracking of an individual. As standard, each of these sensors comes with its own App that gathers and processes the sensor data. Only few solutions exist that combine health data from different applications and different sensors and that make assumptions based on the combined health sensors.

Another increasing field of technology concerns the smart home concept, where a user via Smartphone or any other computing device controls different sensors and actuators in living areas. The typical approach of controlling such smart home solutions is a classical “if this, then that” (“ITTT”) control, where a user sets specific actions of an actuator when a specific sensor measures a predetermined event. For example, solutions exist that allow users to automate actions such as “if the light in sleeping room is switched on, then start heating the bathroom”.

However, the above concepts are rigid and inflexible when it comes to smart decisions, interoperability of different sensors and drawing smart conclusions given complex situations. Even though data throughout the entire life is measured, the measured data does not allow drawing an adequate image of the habits of life, which would be necessary to increase data analysis driven support of health and security.

An improved concept is therefore required that intelligently collects data from different sensors, recognizes complex situations, models behavioral patterns and takes steps accordingly based on the gathered data.

SUMMARY

In view of the above, it is therefore the object of the invention to provide an improved system and computer-implemented method for gathering and combining sensor data from a plurality of sensors in an apartment or living area. This object is solved by the subject matter of the independent claims. Preferred embodiments are defined by the dependent claims. According to an embodiment, the present invention relates to a system and method for gathering and combining sensor data from a plurality of sensors in an apartment or living area. The system may comprise a sensor grid, a sensor fusion unit and a processing unit. The sensor grid may comprise a plurality of sensors for sensing environmental data including at least one of motion, presence, behavior and state of a resident of the apartment or living area. The sensor fusion unit may be adapted to merge data measured by at least two sensors of the sensor grid. The processing unit may be adapted to process the merged data, wherein the processed data indicate at least one of a general state of the resident or the apartment or living area, a prediction of a future environmental state, an emergency situation and a prediction of a health state of the resident.

Optionally, the sensor grid may comprise at least one of an infrared sensor, a camera, a weight sensor, an electrical usage sensor, a door sensor, an air sensor, a light sensor, a noise sensor, a sleep sensor, a control sensor, and an underfloor pressure sensor. However, different sensors not listed may also be used in the sensor grid.

Optionally, processing the merged data may further comprise creating a profile of human activity for one or more residents of the apartment or living area, including inferring a current activity and/or state of the one or more residents of the apartment or living area. In addition or alternatively, processing may include analyzing at least part of available sensor data in order to detect health data and/or early signs of illness and to provide proactive illness prevention. Similarly, processing may include analyzing at least part of available sensor data in order to track energy and resource consumption and to provide energy and resource saving support.

According to an embodiment, the present invention relates to systems and methods for gathering and combining sensor data from a plurality of sensors in an apartment or living area. The system may comprise an alarm system comprising one or more motion sensors. The alarm system may operate in at least one of alarm mode and detection mode. The system may further comprise a door lock sensor for sensing whether an access to the apartment or living area is authorized.

If the door lock sensor determines that there is no authorized access, the alarm system may be set to alarm mode, and if the door lock sensor determines that there is authorized access, the alarm system may be set to detection mode. As such, if the alarm system operates in alarm mode and the one or more motion sensors detect a motion in the apartment or living area, the alarm system may activate an alert. On the other hand and if the alarm system operates in detection mode and the one or more motion sensors detect a motion in the apartment or living area, the motion may be tracked and processed. This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments and aspects will be described in the following description and together with the accompanying drawings, wherein:

Fig. 1 is a diagram showing the main components of the herein disclosed system;

Fig. 2 is a flow diagram of operating the system according to an embodiment;

Fig. 3 is a flow diagram of operating the system according to an embodiment;

Fig. 4 is a diagram showing an exemplary living area where motion sensors are implemented; and

Fig. 5 is a diagram showing an exemplary use case for identifying a potential break-in. DETAILED DESCRIPTION

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings.

Embodiments described herein relate to concepts, methods and systems for utilizing as many sensors as possible in a multifamily apartment complex, in a single apartment or any other living area in order to gather data with the purpose of merging the available data and recognizing a state of the apartment, of one or more residents or any other person or animal in the living area. The system as discussed herein is capable of gathering data from each sensor/appliance installed in the home to model behavioral patterns of each person living in the apartment, with the benefit of increasing the tenant’s security and convenience.

Behavior models may be created with logical connections between data points of multiple sensors and appliances and other data sources available.

For example, assuming a living area comprising at least a door sensor, an air sensor and a security camera in the garage. The system utilizes the sensor data and creates a user profile. In this example, the time between the tenant closing the door of his apartment and entering his vehicle may be shorter than normal. This implies that the person is in a rush. Should it occur in the morning when leaving for work, then there is a high possibility that the person overslept and therefore is late for work. Now the cause for this behavior can be checked. In this example, the air sensor detected smoke in the apartment’s air the night before and the internet was used late in the night. Therefore, the person stayed up long and had less sleep as usual. This results in a higher chance of causing a car accident. Recognizing such correlations may help improving situational awareness for the tenant and provide additional support, if necessary. On the other hand, if such behavior repeats / or is normal, insurance premium of the individual may be raised as the possibility of causing a crash is elevated.

The above example is broken down in easy cause-result relationships for better understanding the concept of the present invention. A more detailed behavioral model implies a greater analysis. Thus, multiple sensors may be used and data from multiple sensors are merged together in an unusual way that is not intended, such as using sensors of an alarm system for tracking the everyday movement of tenants in the living area.

Sensors / cameras may track every activity after entering the property and/or the living area. The different sensors may form a sensor grid that is controlled by the system. Notably, all sensors described and disclosed herein are merely exemplary sensors and are not meant to be limiting the scope. In addition, sensors that are not yet developed may be easily added to the sensor grid and may be used for inferring a state of the living area and/or the tenants or other living beings within the living area.

Exemplary sensors of the sensor grid may be sensors for sensing environmental data including at least one of motion, presence, behavior and state of a resident of the apartment or living area. For example, the sensor grid comprises at least one of an infrared sensor, a camera, a weight sensor, an electrical usage sensor, a door sensor, an air sensor, a light sensor, a noise sensor, a sleep sensor, a control sensor, and an underfloor pressure sensor. Also, additional sensors may be possible. In an embodiment, sensors and data from a smart phone of the user may also be included and used by the system. Exemplary use cases and different sensors that may be used are further discussed below with regard to Tables 1 to 3.

The system may centrally control and process the sensors and sensor data. The system may further provide an API for accessing/controlling the system parameter via a computer, such as by mobile phone, tablet, or any other computing means. For example, an application running on a smartphone may be used to tie together all of the tenant’s benefits and enable easy communication with the landlord. Energy bills can be shared and allocated much faster and with a high level of automation.

In general, the system may be for gathering and combining sensor data from a plurality of sensors in an apartment or living area. Referring to Fig. 1 , main components of the system 100 may be the sensor grid comprising sensors 101-1 to 101-N, a sensor fusion unit 102 and a processing unit 103. The sensor grid may comprise a plurality of sensors 101-1 to 101-N for sensing environmental data including at least one of motion, presence, behavior and state of a resident of the apartment or living area. The sensors 101-1 to 101-N may also include different types of sensors, such as air quality sensors, etc. Details of the sensors 101-1 to 101- N and the sensor grid are disclosed herein.

The sensor fusion unit 102 is adapted to merge data measured by at least two sensors of the sensor grid together. For example, the sensor fusion unit 102 may receive data signals from the plurality of sensors 101-1 to 101-N and bring them together into a standardized format and may provide time markers for measured data signals, such that correlations between different sensor signals are easier to recognize in a later processing step. Merging data may further include generating combined sensor data, that may be stores as a new sensor signal.

The processing unit 103 is adapted to process the merged data as received from the sensor fusion unit 102. The processed data may indicate at least one of a general state of the resident or the apartment or living area, a prediction of a future environmental state, an emergency situation and a prediction of a health state of the resident. Also, other findings and patterns may be recognized and gained from the processing unit 102.

In an embodiment, the processing unit 103 may crop data streams from one or more sensors to increase efficiency of the processing. For example, data that may be identified as not required for processing may be deleted or ignored, thus saving storage and increasing coding and processing efficiency. An example of such data cropping may refer to data points that do not change over a predetermined period of time, while the predetermined time value may depend on the specific sensor. Another example may refer to signals of a video camera, where the video signal may be cropped by removing a background of the captured signal and/or processing only parts of the video stream, where motion is detected.

The processing unit may be implemented as a neural network or may include a neural network for identifying patterns in the behavior of a tenant of the living area.

Further, the processing unit 103 may be connectable to a database 104, a cloud service 105 and/or one or more actuators 106.

The database 104 may store additional data required for processing the sensor data and may be used to store sensor data in either processed or non-processed manner. Similarly, the data may also be stored in the cloud service 105. The cloud service may be externally accessible, such as via web service interface and may be the interface for third party services, such as for online services providing improved models for inferring data from the sensors. In an embodiment, the processing unit 103 may further be connected to one or more actuators 103, such as light switches, electrical switches, heat control, door and/or window opener, ventilation, sound output systems, plant watering systems, and other actuators.

In one embodiment, the system used to gather and combine sensor data from a plurality of sensors in an apartment or living area may comprise an alarm system, comprising one or more motion sensors. The motion sensors of the alarm system may be standardized motion sensors that are state of the art and that are commonly used in alarm systems, such as infrared sensors, proximity sensors, heat sensors, pressure sensors, sound sensors and any other sensor that is capable of sensing, whether a person is present or is moving.

The alarm system may operate in multiple operating modes. For example, two operating modes of the alarm system may be alarm mode and detection mode. The alarm mode refers to the standard mode of the alarm system and means that an alarm is triggered if motion or presence of a person is detected, for example by one of the motion sensors of the alarm system. Switching from one mode to another mode may be done by a user manually, for example by using the mobile phone app or by a hardware button within or near the living area. However, switching from one mode to another mode may also be performed automatically, as discussed below.

For example, the system may further comprise a door lock sensor. The door lock sensor may be capable of sensing/determining whether an access to the apartment or living area is authorized. The determination triggers the system to switch to one of the two modes, i.e. alarm mode or detection mode. For example, if a user leaves the living area and uses the proper key to lock the main door to the living area, the door lock sensor may register the door locking event and assume that the user leaves the house. In this case, the alarm system is set to alarm mode. On the contrary, if the user opens the main door to the living area, the system may assume that the user is coming home and the operating mode is switched from alarm mode to detection mode.

More generally, if the door lock sensor determines that there is no authorized access, the alarm system is set to alarm mode, and if the door lock sensor determines that there is authorized access, the alarm system is set to detection mode.

If the alarm system operates in alarm mode and one of the motion sensors detects a motion in the apartment or living area, the alarm system may activate an alert. Activating an alert may comprise the standard procedure of the alarm system, e.g. sending an alert message to the user and/or to a police department and/or outputting an acoustic alarm. With the improved sensor grid system, however, more advanced operations may be involved, when unauthorized access to the living area is determined. For example, activating an alert when unauthorized access is determined may comprises collecting data of a person within the apartment or living area using at least a subset of the available sensors of the sensor grid. For example, if floor pressure sensors are available in the sensor grid, an approximate weight and/or an approximate step length of the person can be determined, resulting in an estimation of weight and size of the person in the living area. In addition, if sound or noise sensors, such as one or more microphones are present in the sensor grid, the voice of the person in the living area may be recorded and recognized. Using a plurality of sensors for identifying the intruder may also help avoiding false alarm in cases where the tenant is incorrectly determined to be an intruder.

With the sensor data gathered in alarm mode, a report including the data for identifying the person within the apartment may be created. The data for identifying the person within the apartment or living area may be further processed and a profile for the person may be created. If the evaluation of the profile exceeds a threshold, it may be determined that the person is not an intruder, but may be the tenant of the living area. In this case, the operating mode may be switched back to detection mode, or alternatively, may proceed in alarm mode, but sends an alert to the user/tenant of the apartment asking to identify himself and to acknowledge that he is the person within the apartment, while the operating mode of the alarm system is in alarm mode.

On the other hand, if the created profile of the person in the living area is determined to be not the tenant, the profile may be added to the report for the police such that identifying the unauthorized accessor may be easier.

In case the alarm system operates in detection mode, a motion sensor detects a motion in the apartment or living area, the motion is tracked and processed. Processing the data of the sensors (i.e. not only the motion sensor data, but also any other sensor data available) may comprise creating a profile of human activity for one or more residents of the apartment or living area. This may furthermore include inferring a current activity and/or state of the one or more residents of the apartment or living area.

Alternatively or in addition, the processing of the data sensors may results in at least one of a general state of the resident or the apartment or living area, a prediction of a future environmental state, an emergency situation and a prediction of a health state of the resident.

Alternatively or in addition, processing may include analyzing at least part of available sensor data in order to detect health data and/or early signs of illness and to provide proactive illness prevention, and/or processing includes analyzing at least part of available sensor data in order to track energy and resource consumption and to provide energy and resource saving support.

The above scenario is further described with regard to Figs. 4 and 5. For example, referring to Fig 4, a living area is shown that comprises four rooms, i.e. rooms 1 to 4. Each of the rooms 1 to 4 may comprise at least one motion sensor 405-1 to 405-8. The motion sensors may be part of the sensor grid and may be controlled by the alarm system, which is either in alarm mode or in detection mode, based on the door lock sensor, such as door lock sensor of door 410. As can be seen in Fig. 4, a human being 430 may currently stand in room 2, which is detected by motion sensor 405-8. When the human being 430 leaves room 2 through door 420 and enters room 1 , the motion sensors 405-8 and 405-1 may detect such motion for the purpose of tracking or otherwise using the motion information.

Even though, the example of Fig. 4 merely shows motion sensors 405 as sensors of the sensor grid, it is to be understood that also additional and/or other sensors may be used in this example and that the motion sensor is not limiting.

With regard to Fig. 5, different possibilities for unlocking a door, such as the main door 410 of Fig. 4 are shown. The door may be equipped with an electronic door opening mechanism that can be controlled by App via a smartphone, a keycard or a normal key. In an embodiment, also other opening mechanisms may be used, such as via web interface, WiFi, Bluetooth, ZigBee, fingerprint, secret code, face recognition, voice recognition or a combination thereof.

The door lock sensor may register any activity relating to door openings and may determine, whether the door opening attempt relates to an authorized access or whether the door may be opened by force or any other unauthorized door opening attempt, which results in determination of an unauthorized access.

Any time the door is opened, the door lock sensor may thus perform such determination and provide the determination result to the alarm system that may then operate either in alarm mode or detection mode.

Referring back to Fig. 2, a method 200 is illustrated for gathering and combining sensor data from the sensors 201-1 to 201 -N in an apartment or living area.

The method 200 starts with step S210, where the sensor grid is operated, comprising the sensors 101-1 to 101-N for sensing environmental data including at least one of motion, presence, behavior and state of a resident of the apartment or living area are operational. Operating the sensor grid may refer to simply receiving data from the sensors 101-1 to 101-N, but may also include actively starting measurement processes, pulling data from the sensors 101-1 to 101-N, and controlling measuring modes, setting time intervals for measuring and controlling other parameters of the sensors 101-1 to 101-N.

In step S220, the data measured by at least two sensors of the sensor grid may be merged together. This may include bringing data from multiple sensors together by considering time of the data points and location and type of the different sensors. Merging may further include standardizing the data format of the received data.

Continuing to step S230, the merged data is processed. The processed data may indicate at least one of a general state of the resident or the apartment or living area, a prediction of a future environmental state, an emergency situation and a prediction of a health state of the resident.

Fig. 3 refers to method 300 for gathering and combining sensor data from a plurality of sensors 101-1 to 101-N in an apartment or living area comprising an alarm system and a door lock sensor. As described above, the alarm system may comprise one or more motion sensors, but also further sensors for sensing presence of a living being may be used. The method 300 starts with step S310, where the door lock sensor senses whether an access to the apartment or living area is authorized. As disclosed above, there may be multiple concepts of determining such authorized access.

In steps S320 and S330, the alarm system is operated in at least one of alarm mode and detection mode. If it is determined that there is no authorized access, the alarm system is operated in alarm mode, and if the door lock sensor determines that there is authorized access, the alarm system is operated in detection mode.

If the alarm system operates in alarm mode and if the motion sensors detect motion (step S340), an alert is triggered (step S345). On the other hand, if the alarm system operates in detection mode and if at least one of the motion sensors detects motion (step S350), the system tracks and processes the motion (step S355).

In the following Table 1, an exemplary system in use is shown, where different sensors are used to gather data and where the human activity can be tracked and identified. Table 1

Notably, Table 1 is merely an example for different sensors and the data gathered by the sensors. There may be more or less sensors in the system and there may be also additional data gathered by a sensor.

In the following Table 2, an exemplary sensor grid installation in an individual apartment is shown. The different installations are adapted to provide data that can be gathered and used in sensor fusion to create a profile of the tenants as described above.

Table 2

Similarly, in the following Table 3, an exemplary sensor grid installation in a house that may comprise multiple individual apartments is shown. The different installations are adapted to provide data that can be gathered and used in sensor fusion to create a profile of the tenants as described above.

Table 3

Different possible areas of interest for the gathered data are possible, which may include health, security, marketing, financial and insurance, and emergency and government.

The following shows some possible (simplified) use cases of using the data gathered from data points from multiple sensors.

1. Marketing: Weight loss products, Special insurance and financial products ...

2. Insurance: Health insurance will cost the insurance company more as person faces more illnesses

3. Health: Proactive illness prevention by recommending changes in behavior

4. Financial: Important data for credit risk profile, as person will behave similar in certain situations which involves serving loans 5. Emergency: If it is detected, that person might be unconscious. Emergency response already can be informed of a possible heart attack / stroke due to lifestyle.

Another field of use with regard to intelligent health tracking may refer to monitoring and tracking food and eating behavior. For this purpose, cupboards for storing food and beverages, the refrigerator, the freezer and other places where food is stored may be equipped with sensors that identifies stored food and beverages. For example, a camera may be installed in these areas that are able to scan bar codes of food packages and identify vegetables, fruits and other food. This data may be combined with other sensor data, such as sleep data, body weight data, heart sensor data, air data, etc. in order to gather improved health and lifestyle data.

The terms “system,” “device,” "computer," “computer platform” or "computing device" as used herein can mean any type of device that has some amount of processing capability and/or storage capability. Processing capability can be provided by one or more processors that can execute data in the form of computer-readable instructions to provide a functionality. Data, such as computer-readable instructions and/or user-related data, can be stored on storage, such as storage that can be internal or external to the device. The storage can include any one or more of volatile or non-volatile memory, hard drives, flash storage devices, and/or optical storage devices (e.g., CDs, DVDs, Blu-ray disks etc.), remote storage (e.g., cloud- based storage), among others. As used herein, the term "computer-readable media" can include signals. In contrast, the term "computer-readable storage media" excludes signals. Computer-readable storage media includes "computer-readable storage devices." Examples of computer-readable storage devices include volatile storage media, such as RAM, and non volatile storage media, such as hard drives, optical discs, and flash memory, among others.

Examples of devices and computer platforms/systems can include traditional computing devices, such as personal computers, desktop computers, servers, notebook computers, vehicles, smart cameras, surveillance devices/systems, safety devices/systems, wearable smart devices, appliances, and other developing and/or yet to be developed device types, etc.

As mentioned above, device configuration can be thought of as a system on a chip (SOC) type design. In such a case, functionality provided by the device can be integrated on a single SOC or multiple coupled SOCs. One or more processors can be configured to coordinate with shared resources, such as memory/storage, etc., and/or one or more dedicated resources, such as hardware blocks configured to perform certain specific functionality. Thus, the term “processor” as used herein can also refer to central processing units (CPUs), graphical processing units (GPUs), field programmable gate arrays (FPGAs), controllers, microcontrollers, processor cores, or other types of processing devices.

Generally, any of the functions described herein can be implemented using software, firmware, hardware (e.g., fixed-logic circuitry), or a combination of these implementations. The term “component” as used herein generally represents software, firmware, hardware, whole devices or networks, or a combination thereof. In the case of a software implementation, for instance, these may represent program code that performs specified tasks when executed on a processor (e.g., CPU or CPUs). The program code can be stored in one or more computer- readable memory devices, such as computer-readable storage media. The features and techniques of the component are platform-independent, meaning that they may be implemented on a variety of commercial computing platforms having a variety of processing configurations.

The order in which the disclosed methods are described is not intended to be construed as a limitation, and any number of the described blocks can be combined in any order to implement the method, or an alternate method. Furthermore, the methods can be implemented in any suitable hardware, software, firmware, or combination thereof, such that a computing device can implement the method. In one case, the methods are stored on one or more computer-readable storage media as a set of instructions such that execution by a processor of a computing device causes the computing device to perform the method.

Although techniques, methods, devices, systems, platforms, etc., pertaining to building, deploying and executing software applications are described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as example forms of implementing the claimed methods, devices, systems, platforms, etc.

List of further preferred embodiments Embodiment 1:

A system for gathering and combining sensor data from a plurality of sensors in an apartment or living area, the system comprising: a sensor grid comprising a plurality of sensors for sensing environmental data including at least one of motion, presence, behavior and state of a resident of the apartment or living area; a sensor fusion unit for merging data measured by at least two sensors of the sensor grid; and a processing unit for processing the merged data, wherein the processed data indicate at least one of a general state of the resident or the apartment or living area, a prediction of a future environmental state, an emergency situation and a prediction of a health state of the resident.

Embodiment 2:

The system of embodiment 1, wherein said sensor grid comprises at least one of an infrared sensor, a camera, a weight sensor, an electrical usage sensor, a door sensor, an air sensor, a light sensor, a noise sensor, a sleep sensor, a control sensor, and an underfloor pressure sensor.

Embodiment 3:

The system of embodiments 1 or 2, wherein processing the merged data further comprises creating a profile of human activity for one or more residents of the apartment or living area, including inferring a current activity and/or state of the one or more residents of the apartment or living area. embodiment 4:

The system of any of embodiments 1 to 3, wherein processing includes analyzing at least part of available sensor data in order to detect health data and/or early signs of illness and to provide proactive illness prevention.

Embodiment 5: The system of any of embodiments 1 to 4, wherein processing includes analyzing at least part of available sensor data in order to track energy and resource consumption and to provide energy and resource saving support.

Embodiment 6:

The system of any of embodiments 1 to 5, further comprising an alarm system comprising one or more motion sensors, wherein the alarm system operates in at least one of alarm mode and detection mode; and wherein the sensor grid comprises a door lock sensor for sensing whether an access to the apartment or living area is authorized, wherein: if the door lock sensor determines that there is no authorized access, the alarm system is set to alarm mode, and if the door lock sensor determines that there is authorized access, the alarm system is set to detection mode, wherein if the alarm system operates in alarm mode and the one or more motion sensors detect a motion in the apartment or living area, the alarm system activates an alert; and wherein if the alarm system operates in detection mode and the one or more motion sensors detect a motion in the apartment or living area, the motion is tracked and processed.

Embodiment 7:

The system of embodiment 6, wherein activating an alert when unauthorized access is determined comprises collecting data of a person within the apartment or living area using at least a subset of available sensors, and creating a report including said data for identifying the person within the apartment.

Embodiment 8:

A computer-implemented method for gathering and combining sensor data from a plurality of sensors in an apartment or living area, the method comprising: operating a sensor grid comprising a plurality of sensors for sensing environmental data including at least one of motion, presence, behavior and state of a resident of the apartment or living area; merging data measured by at least two sensors of the sensor grid; and processing the merged data, wherein the processed data indicate at least one of a general state of the resident or the apartment or living area, a prediction of a future environmental state, an emergency situation and a prediction of a health state of the resident.

Embodiment 9:

The computer-implemented method of embodiment 8, wherein said sensor grid comprises at least one of an infrared sensor, a camera, a weight sensor, an electrical usage sensor, a door sensor, an air sensor, a light sensor, a noise sensor, a sleep sensor, a control sensor, and an underfloor pressure sensor.

Embodiment 10:

The computer-implemented method of embodiment 8 or 9, wherein processing the merged data further comprises creating a profile of human activity for one or more residents of the apartment or living area, including inferring a current activity and/or state of the one or more residents of the apartment or living area.

Embodiment 11:

The computer-implemented method of any of embodiments 8 to 10, wherein processing includes analyzing at least part of available sensor data in order to detect health data and/or early signs of illness and to provide proactive illness prevention.

Embodiment 12:

The computer-implemented method of any of embodiments 8 to 11 , wherein processing includes analyzing at least part of available sensor data in order to track energy and resource consumption and to provide energy and resource saving support.

Embodiment 13:

The computer-implemented method of any of embodiment 8 to 12, further comprising operating an alarm system comprising one or more motion sensors, wherein the alarm system operates in at least one of alarm mode and detection mode; and wherein the sensor grid comprises a door lock sensor for sensing whether an access to the apartment or living area is authorized, wherein: if the door lock sensor determines that there is no authorized access, the alarm system is set to alarm mode, and if the door lock sensor determines that there is authorized access, the alarm system is set to detection mode, wherein if the alarm system operates in alarm mode and the one or more motion sensors detect a motion in the apartment or living area, the alarm system activates an alert; and wherein if the alarm system operates in detection mode and the one or more motion sensors detect a motion in the apartment or living area, the motion is tracked and processed.

Embodiment 14:

The computer-implemented method of embodiment 13, wherein activating an alert when unauthorized access is determined comprises collecting data of a person within the apartment or living area using at least a subset of available sensors, and creating a report including said data for identifying the person within the apartment.

Embodiment 15:

A computer-readable medium comprising computer-readable instructions, that, when executed by a processor, cause the processor to perform a method according to one of embodiments 8 to 14.