FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION (See Section 10 and Rule 13)
Title of invention:
METHOD AND SYSTEM FOR UNOBTRUSIVE USER MONITORING
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
Preamble to the description
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 user monitoring, and, more particularly, to a method and system for unobtrusive monitoring of user.
BACKGROUND
[002] User monitoring, especially in an assisted living context, is carried out to determine variations from activity pattern of the user, and to trigger appropriate actions accordingly. The actions may include triggering alarms to notify the user or other authorized personnel about an anomaly condition of the user which needs immediate attention. As many elderly people live alone in their houses, such monitoring systems ensure that they get immediate medical attention as and when required.
[003] Different monitoring systems use different approaches to monitor the users. Many of the existing systems rely on sensors that can collect physiological parameters such as heart rate, blood pressure, temperature and so on, and process the collected information to determine health condition of the user(s). Even though this is a reliable approach in terms of accuracy, a disadvantage of this approach is that this is not an unobtrusive approach. The physiological sensors are required to be directly connected to the user being monitored, which may cause inconvenience to the user. In a practical scenario, there may have been several other indicators, for example a change in lifestyle of the user, which may have been a result of any physical condition (for example, fatigue) being experienced by the user. As the aforementioned approach of physiological signals monitoring is configured to trigger alarms only when a sudden change in any of the physiological parameters is noticed, they fail to notice such early warning conditions.
SUMMARY [004] 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. For example, in one embodiment, a processor implemented method of passive and unobtrusive monitoring of a user is proposed. In this method, the user’s movements and activities within a building are

monitored using a plurality of sensors, via one or more hardware processors. Using data collected during the monitoring process, an activity pattern of the user is generated. Generating the activity pattern includes the following steps: Initially a room transition pattern of the user is derived, wherein the room transition pattern comprises information on a) movement of the user between a plurality of rooms in the building, b) time spent in each of the plurality of rooms, and c) order in which the user accessed each of the plurality of rooms, collected over a specified period of time. One or more room activities performed by the user in each of the plurality of rooms are detected, wherein the one or more room activities performed by the user are deduced by correlation and contextualization. Further, user presence in each of the plurality of rooms is associated with the determined one or more room activities, and then the base activity pattern of the user is generated based on the derived room transition pattern and the one or more room activities associated with the user presence indicated in the derived room transition pattern, over the specified period of time. Further, variations in behavior of the user is determined by comparing a real-time activity pattern generated for the user at an instance with the generated base activity pattern, via the one or more hardware processors. Further, the determined variations are categorized as one of a) an anomaly in behavior of the user, and b) a change in activity pattern of the user, via the one or more hardware processors.
[005] In another aspect, a system for passive and unobtrusive monitoring of a user is provided. The system includes 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 perform monitoring of the user. The monitoring of the user involves the following steps. The system monitors the user’s movements and activities within a building using a plurality of sensors. Using data collected during the monitoring process, an activity pattern of the user is generated by the system, by executing the following steps. Initially a room transition pattern of the user is derived, wherein the room transition pattern comprises information on a) movement of the user between a plurality of rooms in the building, b) time spent in each of the plurality of rooms, and c) order in which the user accessed each of the plurality of rooms, collected

over a specified period of time. One or more room activities performed by the user in each of the plurality of rooms are detected, wherein the one or more room activities performed by the user are deduced by correlation and contextualization. Further, the system associates user presence in each of the plurality of rooms with the determined one or more room activities, and then the base activity pattern of the user is generated based on the derived room transition pattern and the one or more room activities associated with the user presence indicated in the derived room transition pattern, over the specified period of time. Further, the system determines variations in behavior of the user by comparing a real-time activity pattern generated for the user at an instance with the generated base activity pattern, via the one or more hardware processors. Further, the determined variations are categorized as one of a) an anomaly in behavior of the user, and b) a change in activity pattern of the user, via the one or more hardware processors.
[006] In yet another aspect, a non-transitory computer readable medium for passive and unobtrusive monitoring of a user is proposed. The non-transitory computer readable medium is comprised of a plurality of instructions, which when executed, cause one or more hardware processors to perform the following steps as part of the user monitoring. The user’s movements and activities within a building are monitored using a plurality of sensors, via one or more hardware processors. Using data collected during the monitoring process, an activity pattern of the user is generated. Generating the activity pattern includes the following steps: Initially a room transition pattern of the user is derived, wherein the room transition pattern comprises information on a) movement of the user between a plurality of rooms in the building, b) time spent in each of the plurality of rooms, and c) order in which the user accessed each of the plurality of rooms, collected over a specified period of time. One or more room activities performed by the user in each of the plurality of rooms are detected, wherein the one or more room activities performed by the user are deduced by correlation and contextualization. Further, user presence in each of the plurality of rooms is associated with the determined one or more room activities, and then the base activity pattern of the user is generated based on the derived room transition pattern and the one or more room activities associated with the user presence indicated in the derived room transition pattern, over the specified period
4

of time. Further, variations in behavior of the user is determined by comparing a real-time activity pattern generated for the user at an instance with the generated base activity pattern, via the one or more hardware processors. Further, the determined variations are categorized as one of a) an anomaly in behavior of the user, and b) a change in activity pattern of the user, via the one or more hardware processors.
[007] 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
[008] 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:
[009] FIG. 1 illustrates an exemplary system for user monitoring, according to some embodiments of the present disclosure.
[010] FIG. 2 is a flow diagram depicting steps involved in the process of monitoring the user using the system of FIG. 1, according to some embodiments of the present disclosure.
[011] FIG. 3 is a flow diagram depicting steps involved in the process of categorizing variations in behaviour of the user, using the system of FIG. 1, in accordance with some embodiments of the present disclosure.
[012] FIGS. 4A – 4D are example graphical representations of patterns corresponding to time spent by the user in different rooms, deducted through correlation and contextualization by the system of FIG. 1, according to some embodiments of the present disclosure.
[013] FIG. 5 depicts example graphical representations of patterns corresponding to average time spent by the user in different rooms during each visit and room transition speed, deducted through correlation and contextualization by the system of FIG. 1, according to some embodiments of the present disclosure.

[014] FIG. 6 depicts example graphical representations of a base activity pattern corresponding to average time spent by the user in different rooms during each visit, deducted through correlation and contextualization by the system of FIG. 1, according to some embodiments of the present disclosure.
[015] FIG. 7 is an example graphical representation depicting variations from the base activity pattern depicted in FIG. 6, determined by the system of FIG. 1, according to some embodiments of the present disclosure.
[016] FIG. 8 is an example of room transition pattern generated for the user by the system of FIG. 1, according to some embodiments of the present disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS [017] 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. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims.
[018] Referring now to the drawings, and more particularly to FIG. 1 through FIG. 8, 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.
[019] FIG. 1 illustrates an exemplary system for user monitoring, according to some embodiments of the present disclosure. The system 100 includes one or more hardware processors 102, communication interface(s) or input/output (I/O) interface(s) 103, and one or more data storage devices or memory 101 operatively coupled to the one or more hardware processors 102. The one or more hardware processors 102 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital

signal processors, central processing units, state machines, graphics controllers, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) 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, workstations, mainframe computers, servers, a network cloud and the like.
[020] The communication interface(s) 103 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 communication interface(s) 103 can include one or more ports for connecting a number of devices to one another or to another server.
[021] The memory 101 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, one or more components (not shown) of the system 100 can be stored in the memory 101. The memory 101 is configured to store a plurality of operational instructions (or ‘instructions’) which when executed cause one or more of the hardware processor(s) 102 to perform various actions associated with the user monitoring being performed by the system 100. The system 100 can be implemented in a variety of ways as per requirements. Various steps involved in the process of user monitoring being performed by the system 100 are explained with description of FIGS. 2 and 3. All the steps in FIGS. 2 and 3 are explained with reference to the system of FIG. 1.
[022] FIG. 2 is a flow diagram depicting steps involved in the process of monitoring the user using the system of FIG. 1, according to some embodiments of the present disclosure. The system 100 is configured to monitor movement and activities of

a user within a building. The building may be home of the user. The building may have multiple rooms such as but not limited to kitchen, bedroom, utility room, living room, and so on. In each room, there may be certain activities (termed as ‘room activities’ hereafter) the user may perform. For example, a Television (TV) may be present only in the living room, hence ‘watching TV’ is an activity the user may perform when in the living room. The system 100 is configured to use non-intrusive sensors to monitor movements and activities of the user. For example, Passive Infrared (PIR) sensors may be placed near doors of rooms to track user movement. Also, contact sensors may be placed on the doors. Data from the contact sensors and the PIR sensors independently or in combination may indicate user movements within the building and to different rooms in the building. Similarly, sensors may be placed to monitor kettle operation, water flow from kitchen tap and so on, which indicates specific activities being performed by the user, without the user having to manually intervene to provide any inputs. Similarly, any appropriate sensor may be used to collect information on different types of activities. Even though only unobtrusive sensors are used to collect information, considering convenience of the user, in an alternate embodiment, the system 100 may use any type of sensor to collect any type of information required for the user monitoring, as required.
[023] The system 100 is configured to monitor (202) the user movements and activities of the user within the building. In various embodiments, the system 100 may perform the monitoring and data collection continuously or at periodic intervals, as required. By processing the sensor data with respect to the user movements and activities, the system 100 generates a room transition pattern of the user. The room transition pattern of the user for a given time period indicates information such as but not limited to a) movement of the user between a plurality of rooms in the building, b) time spent in each of the plurality of rooms, and c) order in which the user accessed each of the plurality of rooms, collected over a specified period of time. Along with the room transition information, the system 100 also collects data from different sensors associated with different activities in each of the plurality of rooms. The system 100 associates each of the identified activities with presence of the user in each of the rooms. For example, consider that the room transition pattern of the user indicates that the user spends time

between 8.30 AM and 10 AM in the kitchen, and during this period the kettle is ON for some time, waterflow meter in the kitchen sink detects activity for some time and so on. The system 100 also obtains time stamp of each of the activities. The system 100 performs correlation and contextualization, by which the system 100 determines relation between each of the identified activities and the user’s room presence in each of the rooms. The system 100 may consider the time stamp associated with the stay in each of the rooms, and the time stamp of the activities to perform the correlation and contextualization and determine the relation between the activities and the room presence. The term “correlation and contextualization” in this context refers to the step of determining relation between room stay/room presence and corresponding activities, based on the time stamp associated with the each of the room activities and the room presence. After identifying the relations, the system 100 associates each of the activities with the corresponding room presence information.
[024] The system 100 may be configured to derive the transition pattern, determine corresponding activities, and associate the activities with a derived room presence of the user, continuously. Based on the room transition pattern and the activities derived for the user over a period of time, the system 100 learns/derives/generates (204) a base activity pattern of the user. The base activity pattern of the user indicates/represents a pattern of room transitions and activities performed by the user on a day-to-day basis. For example, consider the example pattern given in FIG. 8. FIG. 8 is an example of room transition pattern generated for the user by the system of FIG. 1, according to some embodiments of the present disclosure.
[025] In an embodiment, the system 100 can be configured to collect information on one or more user activities outside the house and use the collected information on the one or more user activities outside the house along with the derived room transition pattern and the one or more room activities, to generate the base activity pattern of the user. The information on the one or more user activities may include information on one or more actions being performed by the user, outside the house, and movements associated with each of the actions. For example, the action may be user going for a walk outside the house. Sensors for non-intrusive detection of the action may be deployed

outside the house, maybe within vicinity of the house. In an alternate embodiment, suitable wearable sensors may be used to monitor user actions and movements in scenarios in which the user monitoring with only the non-intrusive sensors is not feasible, yet the user needs to be monitored.
[026] This is an example room transition pattern of the user between 6 AM and 9 AM. As indicated in the pattern, the user moves between Living room, Bedroom, Kitchen, and Bathroom during the specified time period, and spends some amount of time in each room. The transition between the rooms, as well as the time spent in each room during each visit by the user are monitored and learned, which the system 100 uses to generate the room transition pattern. From such a room transition pattern generated for a span of 24 Hrs, the system 100 can further deduce information such as but not limited to total time spent in each room in a day by the user, and from this information, the system 100 can further learn, for each room, how much time was spent early morning, before noon, afternoon, evening and so on by the user, average time spent in each room and so on. Such information helps the system 100 to learn the base activity pattern by capturing even granular level details with respect to user movements and user activities. This is depicted in the example diagrams given in FIGS. 4A through 4D and FIG. 5, respectively. FIGS. 4A – 4D are example graphical representations of patterns corresponding to time spent by the user in different rooms, deducted through correlation and contextualization by the system of FIG. 1, according to some embodiments of the present disclosure. FIG. 5 depicts example graphical representations of patterns corresponding to average time spent by the user in different rooms during each visit and room transition speed, deducted through correlation and contextualization by the system of FIG. 1, according to some embodiments of the present disclosure. When the system 100 associates the determined activities with the room transition data, the (base) activity pattern of the user is obtained. For example, consider the example diagram FIG. 6. FIG. 6 depicts example graphical representations of a base activity pattern corresponding to average time spent by the user in different rooms during each visit, deducted through correlation and contextualization by the system of FIG. 1, according to some embodiments of the present disclosure. The activity pattern given in FIG. 6 represents time spent by the user on two of the kitchen

activities (i.e., kettle operation and water flow duration) during his stay in the kitchen between 6 AM and 9 AM each day for an observation period of 15 days.
[027] The generated base activity pattern is stored as a reference in a database associated with the memory 101. Using an approach similar to the approach used for generating the base activity pattern, real-time movements and activities of the user are monitored and a real-time activity pattern is generated. The system 100 compares (206) the real-time activity pattern with the base activity pattern and checks for variation(s) in the real-time activity pattern in comparison with the base activity pattern. Such variations detected (208) may either represent an anomaly in behavior of the user or may indicate a change in activity pattern of the user. In order to categorize (210) the detected variation(s) as one of the anomaly or pattern change, the system 100 observes/monitors (302) occurrence of the variations for a pre-defined span of time (for example, 30 days, as configured). Within the pre-defined span of time if the variations are repeated and detected (304) for more than a specified duration of time, then the system 100 categorizes (308) the variations as a change in activity pattern of the user. If such a change in the activity pattern is detected, then the system 100 updates the base pattern of the user to capture the detected change in activity pattern. If within the pre-defined span of time the variations are repeated and detected for less than the specified duration of time, then the system 100 categorizes (306) the variations as an anomaly. An example of such variations is depicted in FIG. 7, and values are given in Table. 1. FIG. 7 is an example graphical representation depicting variations from the base activity pattern depicted in FIG. 6, determined by the system of FIG. 1, according to some embodiments of the present disclosure.

Date Kitchen presence (minutes) Waterflow duration (minutes) Kettle operation duration (minutes)
Persona average 44 14 30
5th January 10 (-34) 0 (-14) 0 (-30)
6th January 0 (-44) 0 (-14) 0 (-30)

7th January 0 (-44) 0 (-14) 0 (-30)
8th January 30 (-14) 6 (-8) 0 (-30)
[028] In comparison with the base activity pattern depicted in FIG. 6, the activity pattern in FIG. 7 shows a change for 3 days (5th to 7th of January). For example, on 5th of January, even though the user was in kitchen, no cooking happened between 6 AM and 9 AM, and this is a variation from the base activity pattern of the user. Similarly, for a user who is suffering from abnormal health conditions, information on room presence correlated with bed presence, movement outside with a wearable device information, room presence with gesture recognition, and information on activities such medication intake with time and so on may be used by the system 100 to identify the base activity pattern and in turn to identify anomalies which require attention. Upon detecting an anomaly, the system 100 may trigger one or more actions as configured. For example, the anomaly may require attention of a medical professional, and the system 100 may trigger an alert (for example, SMS or audio clip) to numbers of computing devices associated with friends/relatives/medical professionals, as pre-configured.
[029] As the room movements of the user are periodically/continuously monitored and the data is stored in an associated database, the system 100 may access and filter for data in any particular time span (for example, day-wise, month-wise, year-wise and so on) and accordingly generate the room transition pattern, the activity pattern, and in turn the base activity pattern for the user, for analysis purpose. Also, the user monitoring mechanism described herein can work irrespective of age of the user being monitored.
[030] 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.
[031] The embodiments of present disclosure herein address unresolved problem of unobtrusive user monitoring. The embodiment thus provide a mechanism for generating a base activity pattern for a user. Moreover, the embodiments herein further provide an option to detect anomaly in behavior of the user or automatically update the base activity pattern of the user.
[032] 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.
[033] 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.

[034] 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.
[035] 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.
[036] 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.

We Claim:
1. A processor implemented method (200) of passive and unobtrusive monitoring of a user, the method comprising:
monitoring (202) movements and activities of a user within a building,
using a plurality of sensors, via one or more hardware processors;
generating (204) a base activity pattern of the user, via the one or more
hardware processors, comprising:
deriving a room transition pattern of the user, wherein the room
transition pattern comprises information on a) movement of the
user between a plurality of rooms in the building, b) time spent in
each of the plurality of rooms, and c) order in which the user
accessed each of the plurality of rooms, collected over a specified
period of time;
determining one or more room activities performed by the user in
each of the plurality of rooms, wherein the one or more room
activities performed by the user are deduced by correlation and
contextualization;
associating user presence in each of the plurality of rooms with the
determined one or more room activities; and
generating the base activity pattern of the user, based on the
derived room transition pattern and the one or more room activities
associated with the user presence indicated in the derived room
transition pattern, over the specified period of time;
comparing (206) a real-time activity pattern generated for the user at an
instance with the generated base activity pattern, via the one or more
hardware processors;
determining (208) variations in behavior of the user, based on comparison
of the real-time activity pattern and the generated base activity pattern, via
the one or more hardware processors; and

categorizing (210) the determined variations as one of a) an anomaly in behavior of the user, and b) a change in activity pattern of the user, via the one or more hardware processors.
2. The method (200) as claimed in claim 1, wherein the determined variations at an instance are categorized as the anomaly in behaviour of the user if a determined extent of similarity of a real-time activity pattern generated for the instance with the base activity pattern is below a threshold of similarity, for less than a specified span of time.
3. The method (200) as claimed in claim 1, wherein the determined variations are categorized as indicative of a change in the activity pattern of the user if the determined variations are repeated for a pre-defined duration of time, within a specified span of time.
4. The method as claimed in claim 1, wherein the base activity pattern of the user is generated by collecting information on one or more user activities outside the house, in addition to the derived room transition pattern and the one or more room activities.
5. The method as claimed in claim 4, wherein the information on the one or more user activities comprises one or more actions being performed by the user, outside the house, and movements associated with each of the actions.
6. A system (100) for passive and unobtrusive monitoring of a user, the system 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:
monitor movements and activities of a user within a building, using
a plurality of sensors;
generate a base activity pattern of the user, comprising:
deriving a room transition pattern of the user, wherein the room transition pattern comprises information on a) movement of the user between a plurality of rooms in the building, b) time spent in each of the plurality of rooms, and c) order in which the user accessed each of the plurality of rooms, collected over a specified period of time; determining one or more room activities performed by the user in each of the plurality of rooms, wherein the one or more room activities performed by the user are deduced by correlation and contextualization;
associating user presence in each of the plurality of rooms with the determined one or more room activities; and generating the base activity pattern of the user, based on the derived room transition pattern and the one or more room activities associated with the user presence indicated in the derived room transition pattern, over the specified period of time;
compare a real-time activity pattern generated for the user at an
instance with the generated base activity pattern, via the one or
more hardware processors;
determine variations in behavior of the user, based on comparison
of the real-time activity pattern and the generated base activity
pattern; and

categorize the determined variations as one of a) an anomaly in behavior of the user, and b) a change in activity pattern of the user.
7. The system as claimed in claim 6, wherein the system categorizes the determined variations at an instance as the anomaly in behaviour of the user if a determined extent of similarity of an activity pattern generated for the instance with the base activity pattern is below a threshold of similarity, for less than a specified span of time.
8. The system as claimed in claim 6, wherein the system categorizes the determined variations as indicative of a change in the activity pattern of the user if the determined variations are repeated for a pre-defined duration of time, within a specified span of time.
9. The system as claimed in claim 6, wherein the system generates the base activity pattern of the user by collecting information on one or more user activities outside the house, in addition to the derived room transition pattern and the one or more room activities.
10. The system as claimed in claim 9, wherein the system collects information on one or more actions being performed by the user, outside the house, and movements associated with each of the actions, as the information on the more user activities outside the house.