Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEMS AND METHODS FOR DETECTING MOVEMENTS OF MULTIPLE RESIDENTS USING UNOBSTRUSIVE SENSING TECHNIQUES

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD
[001] The disclosure herein generally relates to unobtrusive sensing techniques, and, more particularly, to systems and methods for detecting movements of multiple residents using unobtrusive sensing techniques.

BACKGROUND
[002] When there are multiple residents in a premise (e.g., house), the residents are monitored using the same passive infrared sensor (PIR) sensors. Observation data captured through these PIR sensors does not have any individual identifiable parameter. Hence, it is hard and challenging to accurately distinguish between individuals’ observations. Further, conventional approaches address this problem using additional sensors and/or devices such as cameras, radio frequency (RF) tags, location-based sensors, etc. which makes the overall system and its deployment costlier and also at times makes the system intrusive.

SUMMARY
[003] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.
[004] For example, in one aspect, there is provided a processor implemented method for detecting movements of multiple residents using unobtrusive sensing techniques. The method comprises retrieving, via one or more hardware processors, a plurality of sensor observations pertaining to at least a first resident and a second resident, wherein the plurality of sensor observations are captured using one or more unobtrusive sensing techniques pertaining to the first resident and the second resident; sorting the plurality of sensor observations based on a corresponding timestamp to obtain a set of sorted sensor observations; grouping the set of sorted sensor observations to obtain a plurality of groups of sensor observations; and iteratively performing, via the one or more hardware processors, for each group of sensor observations amongst the plurality of groups of sensor observations, until a last group of sensor observations: defining, for a current group, a first location and a second location for a sensor observation in the current group, and a last location identified for a resident based on an associated timestamp; performing a comparison of (i) the last location of the resident and the first location and the second location, and (ii) a difference between (a) the associated timestamp and (b) a timestamp of the sensor observation in the current group and a time duration; and performing, based on the comparison: tagging the resident to a first desired location, wherein the first desired location is the first location or the second location; or identifying a second desired location for the resident based on an activities daily living (ADL) pattern and tagging the second desired location to the resident, wherein the resident is the first resident or the second resident.
[005] In an embodiment, the time duration is a pre-determined time duration.
[006] In an embodiment, the time duration is a dynamically determined time duration based on the ADL pattern.
[007] In an embodiment, the step of the grouping of the set of sorted sensor observations is based on two or more activities being performed simultaneously.
[008] In an embodiment, the method further comprises identifying at least one activity performed by the resident based on the first desired location or the second desired location being tagged.
[009] In another aspect, there is provided a processor implemented system for detecting movements of multiple residents using unobtrusive sensing techniques. The system comprises: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: retrieve a plurality of sensor observations pertaining to at least a first resident and a second resident, wherein the plurality of sensor observations are captured using one or more unobtrusive sensing techniques pertaining to the first resident and the second resident; sort the plurality of sensor observations based on a corresponding timestamp to obtain a set of sorted sensor observations; group the set of sorted sensor observations to obtain a plurality of groups of sensor observations; and iteratively perform, for each group of sensor observations amongst the plurality of groups of sensor observations, until a last group of sensor observations: defining, for a current group, a first location and a second location for a sensor observation in the current group, and a last location identified for a resident based on an associated timestamp; performing a comparison of (i) the last location of the resident and the first location and the second location, and (ii) a difference between (a) the associated timestamp and (b) a timestamp of the sensor observation in the current group and a time duration; and perform, based on the comparison: tagging the resident to a first desired location, wherein the first desired location is the first location or the second location; or identifying a second desired location for the resident based on an activities daily living (ADL) pattern and tagging the second desired location to the resident, wherein the resident is the first resident or the second resident.
[010] In an embodiment, the time duration is a pre-determined time duration.
[011] In an embodiment, the time duration is a dynamically determined time duration based on the ADL pattern.
[012] In an embodiment, the step of the grouping of the set of sorted sensor observations is based on two or more activities being performed simultaneously.
[013] In an embodiment, the one or more hardware processors are further configured by the instructions to identify at least one activity performed by the resident based on the first desired location or the second desired location being tagged.
[014] In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause detecting movements of multiple residents using unobtrusive sensing techniques by retrieving a plurality of sensor observations pertaining to at least a first resident and a second resident, wherein the plurality of sensor observations are captured using one or more unobtrusive sensing techniques pertaining to the first resident and the second resident; sorting the plurality of sensor observations based on a corresponding timestamp to obtain a set of sorted sensor observations; grouping the set of sorted sensor observations to obtain a plurality of groups of sensor observations; and iteratively performing for each group of sensor observations amongst the plurality of groups of sensor observations, until a last group of sensor observations: defining, for a current group, a first location and a second location for a sensor observation in the current group, and a last location identified for a resident based on an associated timestamp; performing a comparison of (i) the last location of the resident and the first location and the second location, and (ii) a difference between (a) the associated timestamp and (b) a timestamp of the sensor observation in the current group and a time duration; and performing, based on the comparison: tagging the resident to a first desired location, wherein the first desired location is the first location or the second location; or identifying a second desired location for the resident based on an activities daily living (ADL) pattern and tagging the second desired location to the resident, wherein the resident is the first resident or the second resident.
[015] In an embodiment, the time duration is a pre-determined time duration.
[016] In an embodiment, the time duration is a dynamically determined time duration based on the ADL pattern.
[017] In an embodiment, the step of the grouping of the set of sorted sensor observations is based on two or more activities being performed simultaneously.
[018] In an embodiment, the one or more instructions which when executed by one or more hardware processors further cause identifying at least one activity performed by the resident based on the first desired location or the second desired location being tagged.
[019] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
[020] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[021] FIG. 1 depicts an exemplary system for detecting movements of multiple residents using unobtrusive sensing techniques, in accordance with an embodiment of the present disclosure.
[022] FIG. 2 depicts an exemplary flow chart illustrating a method for detecting movements of multiple residents using unobtrusive sensing techniques, using the system of FIG. 1, in accordance with an embodiment of the present disclosure.
[023] FIG. 3 depicts a multiple residents activity graph, in accordance with an embodiment of the present disclosure.
[024] FIG. 4 depicts an activity graph of a first resident amongst multiple residents, in accordance with an embodiment of the present disclosure.
[025] FIG. 5 depicts an activity graph of a second resident, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[026] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
[027] Referring now to the drawings, and more particularly to FIGS. 1 through 5, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[028] FIG. 1 depicts an exemplary system 100 for detecting movements of multiple residents using unobtrusive sensing techniques, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more hardware processors 104, communication interface device(s) or input/output (I/O) interface(s) 106 (also referred as interface(s)), and one or more data storage devices or memory 102 operatively coupled to the one or more hardware processors 104. The one or more processors 104 may be one or more software processing components and/or hardware processors. In an embodiment, the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is/are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices (e.g., smartphones, tablet phones, mobile communication devices, and the like), workstations, mainframe computers, servers, a network cloud, and the like.
[029] The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
[030] The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic-random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, a database 108 is comprised in the memory 102, wherein the database 108 comprises information sensor observation pertaining to residents capturing through one or more unobtrusive sensing techniques. The database 108 further comprises sorted sensor observations, wherein the sorted sensor observations are further grouped, and the like. The memory 102 further comprises (or may further comprise) information pertaining to input(s)/output(s) of each step performed by the systems and methods of the present disclosure. In other words, input(s) fed at each step and output(s) generated at each step are comprised in the memory 102 and can be utilized in further processing and analysis.
[031] FIG. 2, with reference to FIG. 1, depicts an exemplary flow chart illustrating a method for detecting movements of multiple residents using unobtrusive sensing techniques, using the system 100 of FIG. 1, in accordance with an embodiment of the present disclosure. In an embodiment, the system(s) 100 comprises one or more data storage devices or the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The steps of the method of the present disclosure will now be explained with reference to components of the system 100 of FIG. 1, and the flow diagram as depicted in FIG. 2.
[032] At step 202 of the method of the present disclosure, the one or more hardware processors 104 retrieve a plurality of sensor observations pertaining to at least a first resident and a second resident. The plurality of sensor observations are captured using one or more unobtrusive sensing techniques pertaining to the first resident and the second resident. The one or more unobtrusive sensing techniques are sensing techniques as known in the art. Examples of unobtrusive sensing techniques include but are not limited to passive infrared (PIR) sensors, door contact sensors, or combination thereof. The plurality of sensor observations (also referred to as observational data and interchangeably used herein) pertain to the first resident (e.g., a resident R1). The plurality of sensor observations (also referred to as observational data and interchangeably used herein) also contains information pertaining to the second resident. In other words, the plurality of sensor observations also contains all sensor observations/data created by the second resident (e.g., a resident R2) too since they are monitored using the same sensing environment. The sensor observations include one or more activities performed by the residents (R1 and R2) in a given environment/premise (e.g., say house). These activities include performing one or more actions in various rooms (e.g., bedroom, bathroom, and the like) of the house. Hence, all the sensor observations include an associated timestamp for the activities captured from the one or more unobtrusive sensing techniques. The step 202 and above example of sensor observation and activities captured may be better understood in conjunction with step 204 described below.
[033] At step 204 of the method of the present disclosure, the one or more hardware processors 104 sort the plurality of sensor observations based on a corresponding timestamp to obtain a set of sorted sensor observations. Below Table 1 depicts the set of sorted sensor observations.
Table 1
id
[PK] bigint object_unique_id
Character varying (255) observation_timestamp
Character varying (255) tagged_agent_id
Character varying (255)
1 resident_1_bedroom 2023-06-12 00:00:00 LWCR00001
3 resident_1_bedroom 2023-06-12 02:00:00 LWCR00001
5 resident_1_bedroom 2023-06-12 03:00:00 LWCR00001
7 resident_1_bedroom 2023-06-12 04:00:00 LWCR00001
9 resident_1_bathroom 2023-06-12 04:25:00 LWCR00001
11 resident_1_bedroom 2023-06-12 04:25:00 LWCR00001
13 resident_1_bedroom 2023-06-12 04:45:00 LWCR00001
15 resident_1_bedroom 2023-06-12 05:15:00 LWCR00001
17 resident_1_bedroom 2023-06-12 05:45:00 LWCR00001
19 resident_1_kitchen 2023-06-12 05:50:00 LWCR00001
21 resident_1_bathroom 2023-06-12 05:50:00 LWCR00001
23 resident_1_kitchen 2023-06-12 05:51:00 LWCR00001
25 resident_1_kitchen 2023-06-12 05:52:00 LWCR00001
27 resident_1_kitchen 2023-06-12 05:53:00 LWCR00001
29 resident_1_kitchen 2023-06-12 05:54:00 LWCR00001
31 resident_1_kitchen 2023-06-12 05:55:00 LWCR00001
33 resident_1_kitchen 2023-06-12 05:56:00 LWCR00001
35 resident_1_kitchen 2023-06-12 05:57:00 LWCR00001
37 resident_1_kitchen 2023-06-12 05:58:00 LWCR00001
39 resident_1_livingroom 2023-06-12 05:58:05 LWCR00001

[034] In the above Table 1, if the sensor observations 9 and 11 are examined, it can be observed that there are parallel/concurrent/simultaneous activities in both bathroom and bedroom. In other words, the step of the grouping of the set of sorted sensor observations is based on two or more activities being performed simultaneously. Up until observation 7, there are no parallel activities, and all observations are assigned to the first resident R1. At this point, the last activity assigned to R1 is in the bedroom at 04:00:00 (time).
[035] At step 206 of the method of the present disclosure, the one or more hardware processors 104 group the set of sorted sensor observations to obtain a plurality of groups of sensor observations. Below Table 2 depicts a plurality of groups of sensor observations.
Table 2
id
[PK] bigint object_unique_id
Character varying (255) observation_timestamp
Character varying (255) tagged_agent_id
Character varying (255)
5 resident_1_bedroom 2023-06-12 03:00:00 LWCR00001
7 resident_1_bedroom 2023-06-12 04:00:00 LWCR00001
9 resident_1_bathroom 2023-06-12 04:25:00 LWCR00001
11 resident_1_bedroom 2023-06-12 04:25:00 LWCR00001
13 resident_1_bedroom 2023-06-12 04:45:00 LWCR00001
15 resident_1_bedroom 2023-06-12 05:15:00 LWCR00001

[036] As can be seen from above Table 2, the highlighted rows (or bold text of id 9 and 11) belong to the same group as they both have same observation_timestamp. So, the set of sorted observations having same observation_timestamp is grouped into one (or referred as 1 group or group 1).
[037] At step 208 of the method of the present disclosure, the one or more hardware processors 104 iteratively perform a plurality of steps for each group of sensor observations amongst the plurality of groups of sensor observations, until a last group of sensor observations. More particularly, at step 208a, the one or more hardware processors 104 define, for a current group, a first location and a second location for a sensor observation in the current group, and a last location identified for a resident based on an associated timestamp. At step 208b, the one or more hardware processors 104 perform a comparison of (i) the last location of the resident and the first location and the second location, and (ii) a difference between (a) the associated timestamp and (b) a timestamp of the sensor observation in the current group and a time duration. At step 208c, based on the comparison outcome of step 208b, the one or more hardware processors 104 perform at least one step. For instance, at step 208c-1, the one or more hardware processors 104 tag the resident to a first desired location, the first desired location is either the first location or the second location. More specifically, the system 100 checks whether (i) the last location of the resident is the same first location or the second location, and (ii) the difference between (a) the associated timestamp and (b) a timestamp of the sensor observation in the current group is less than or equal to the time duration. In an embodiment, the time duration is a pre-determined time duration. For instance, say the time duration is 1 minute (or 60 seconds). The system 100 checks whether the difference between the associated timestamp and the timestamp of the sensor observation in the current group is less than or equal to 1 minute. If this check is satisfied (if the difference is less than or equal to 1 minute), then the system 100 tags the resident to a first desired location, which could be either the first location or the second location. If the above check is not satisfied, i.e., if the last location of the resident is not the first location or the second location and (if the difference is greater 1 minute (second case scenario), then at step 208c-2, the one or more hardware processors 104 identify a second desired location for the resident based on an activities daily living (ADL) pattern and tag the second desired location to the resident. In this case scenario, the resident is the first resident or the second resident. In such a scenario, the time duration (e.g., 1 minute being determined) is a dynamically determined time duration based on the ADL pattern. The above steps of 208 through 208c-2 are better understood by way of following description:
[038] After grouping, the system 100 defines the first location and last location of the resident based on the time difference. In a first case, time difference is greater than the transmission time from one room to another. As shown in above Table 2, it can be seen that there is a considerable time difference between the last observation i.e.., R2_Last = (Bedroom, 04:00:00) and current observation, which is a time gap of 25 minutes. Hence, the system 100 refers to the activities daily living (ADL) pattern for identifying the individual/resident. Below Table 3 depicts an exemplary activities daily living (ADL) pattern for residents:
Table 3
location_id
Character varying (255) observation_time
Character varying (255) resident_id
Character varying (255)
bedroom 00:00:00-04:00:00 LWCR00001
bathroom 04:25:00-04:45:00 LWCR00001
bedroom 04:45:00-05:50:00 LWCR00001
kitchen 05:50:00 -05:58:00 LWCR00001
Livingroom 05:58:00 -06:00:00 LWCR00001
bedroom 00:00:00-05:50:00 LWCR00002
bathroom 05:50:00-06:00:00 LWCR00002

[039] By referring to the above Table 3, the system 100 analysis and outputs that the resident 1 has high probability to be seen in the bathroom and that observation is assigned to R1 and R1_last updated as R1_Last = (Bathroom, 04:25).
[040] In the second case scenario, when the time difference is not greater than the transmission time from one room to another room, below Table 4 depicts an exemplary sorted sensor observations.
Table 4
id
[PK] bigint object_unique_id
Character varying (255) observation_timestamp
Character varying (255) tagged_agent_id
Character varying (255)
33 resident_1_kitchen 2023-06-12 05:56:00 LWCR00001
35 resident_1_kitchen 2023-06-12 05:57:00 LWCR00001
37 resident_1_kitchen 2023-06-12 05:58:00 LWCR00001
39 resident_1_livingroom 2023-06-12 05:58:05 LWCR00001
40 Resident_1_kitchen 2023-06-12 05:59:00 LWCR00001

[041] From the above Table 4, time difference between current group can be seen i.e., ObservationTime_05:58 = (Kitchen, Livingroom) and R1_last = (kitchen, 05:57) is less than the room transition threshold time between 2 rooms. So, R1_last = (kitchen, 05:58) is assigned from the group as the last location.
[042] Once the resident is tagged to desired location (e.g., the first desired location or the second desired location), the one or more hardware processors 104 identify at least one activity performed by the resident based on the first desired location or the second desired location being tagged.
[043] FIG. 3, with reference to FIGS. 1-2, depicts a multiple residents activity graph, in accordance with an embodiment of the present disclosure. FIG. 4, with reference to FIGS. 1-3, depicts an activity graph of the first resident, in accordance with an embodiment of the present disclosure. FIG. 5, with reference to FIGS. 1-4, depicts an activity graph of the second resident, in accordance with an embodiment of the present disclosure.
[044] As mentioned earlier, conventional approaches use additional sensors and/or devices such as cameras, radio frequency (RF) tags, location-based sensors, etc. for activity detection which makes the overall system and its deployment costlier and also at times makes the system intrusive. Embodiments of the present disclosure provide systems and methods that identify the owner of each activity from a set of sensors observations that are generated from the same source by two individuals. More specifically, the system of the present disclosure identifies the person by relate the current observation with neighboring observation and previous observation location for the person. The system of the present disclosure further implements a predefined possible room presence data as a validation point. More specifically, the implementation of the system and the method of the present disclosure requires only low-cost unobtrusive PIR sensors and hence is cost effective. Furthermore, identifying individuals from these sensor observations helps in deriving pattern and detecting anomalies effectively for multiple residents living in the same home and this further reduces the possibility of false positive.
[045] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[046] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.
[047] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[048] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[049] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[050] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

, Claims:
1. A processor implemented method, comprising:
retrieving, via one or more hardware processors, a plurality of sensor observations pertaining to at least a first resident and a second resident (202), wherein the plurality of sensor observations are captured using one or more unobtrusive sensing techniques pertaining to the first resident and the second resident;
sorting, via the one or more hardware processors, the plurality of sensor observations based on a corresponding timestamp to obtain a set of sorted sensor observations (204);
grouping, via the one or more hardware processors, the set of sorted sensor observations to obtain a plurality of groups of sensor observations (206); and
iteratively performing, via the one or more hardware processors, for each group of sensor observations amongst the plurality of groups of sensor observations, until a last group of sensor observations (208):
defining (208a), for a current group, a first location and a second location for a sensor observation in the current group, and a last location identified for a resident based on an associated timestamp;
performing (208b) a comparison of (i) the last location of the resident and the first location and the second location, and (ii) a difference between (a) the associated timestamp and (b) a timestamp of the sensor observation in the current group and a time duration; and
performing (208c), based on the comparison:
tagging the resident to a first desired location (208c-1), wherein the first desired location is the first location or the second location; or
identifying a second desired location for the resident based on an activities daily living (ADL) pattern and tagging the second desired location to the resident (208c-2), wherein the resident is the first resident or the second resident.

2. The processor implemented method as claimed in claim 1, the step of the grouping of the set of sorted sensor observations is based on two or more activities being performed simultaneously.

3. The processor implemented method as claimed in claim 1, wherein the time duration is a pre-determined time duration.

4. The processor implemented method as claimed in claim 1, wherein the time duration is a dynamically determined time duration based on the ADL pattern.

5. The processor implemented method as claimed in claim 1, comprising identifying at least one activity performed by the resident based on the first desired location or the second desired location being tagged.

6. A system (100), comprising:
a memory (102) storing instructions;
one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
retrieve a plurality of sensor observations pertaining to at least a first resident and a second resident, wherein the plurality of sensor observations are captured using one or more unobtrusive sensing techniques pertaining to the first resident and the second resident;
sort the plurality of sensor observations based on a corresponding timestamp to obtain a set of sorted sensor observations;
group the set of sorted sensor observations to obtain a plurality of groups of sensor observations; and
iteratively perform, for each group of sensor observations amongst the plurality of groups of sensor observations, until a last group of sensor observations:
defining, for a current group, a first location and a second location for a sensor observation in the current group, and a last location identified for a resident based on an associated timestamp;
performing a comparison of (i) the last location of the resident and the first location and the second location, and (ii) a difference between (a) the associated timestamp and (b) a timestamp of the sensor observation in the current group and a time duration; and
performing, based on the comparison:
tagging the resident to a first desired location, wherein the first desired location is the first location or the second location; or
identifying a second desired location for the resident based on an activities daily living (ADL) pattern and tagging the second desired location to the resident, wherein the resident is the first resident or the second resident.

7. The system as claimed in claim 6, wherein the set of sorted sensor observations is grouped and is further based on two or more activities being performed simultaneously.

8. The system as claimed in claim 6, wherein the time duration is a pre-determined time duration.

9. The system as claimed in claim 6, wherein the time duration is a dynamically determined time duration based on the ADL pattern.

10. The system as claimed in claim 6, wherein the one or more hardware processors are further configured by the instructions to identify at least one activity performed by the resident based on the first desired location or the second desired location being tagged.