Method and system for generating personalized environmental sensitivity profile

Abstract

The present disclosure relates to a method and system for generating personalized environmental sensitivity profile. The processor (102) is further configured to receive a set of physiological 5 parameters and a set of environmental parameters associated with at least one user. Thereafter, the at least one processor (102) using a trained model (206) analyses the received set of physiological and environmental parameters to identify correlations between the set of physiological parameters and the set of environmental parameters. Thereafter, the at least one processor (102) determines a threshold limit for at least one of the set of environmental parameters that causes breach in a 0 predefined threshold associated with the set of physiological parameters, and accordingly the at least one processor (102) generates a personalized sensitivity profile for the user based on the determined threshold limit.

Description

METHOD AND SYSTEM FOR GENERATING PERSONALIZED ENVIRONMENTAL SENSITIVITY PROFILEFIELD OF INVENTION

[0001] The present invention relates to generating personalized health recommendations. More particularly, the present disclosure relates to methods and systems for generating personalized environmental sensitivity profiles.BACKGROUND

[0002] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0003] In recent years, there has been a growing emphasis on personal health and wellbeing, driven by advancements in wearable technology, health monitoring devices, and the increasing awareness of the impact of environmental factors on human health. While significant strides have been made in tracking physiological parameters such as sleep quality, heart rate variability (HRV), and physical activity, current health monitoring systems often fail to consider the intricate interplay between these physiological factors and environmental conditions like temperature, humidity, noise levels, and air quality.

[0004] Traditional health monitoring systems primarily focus on isolated datasets. For example, wearable devices excel at tracking biological markers but provide little to no insight into how external environmental factors might influence these markers. Similarly, environmental sensors collect data on air quality, temperature, and humidity but lack the ability to correlate this information with an individual's physiological state. This fragmented approach leads to a limited understanding of the root causes of health issues and reduces the effectiveness of health recommendations. Poor sleep quality detected by a wearable device, for example, may result in generic suggestions like increasing sleep duration without identifying potential environmental causes such as high CO2 concentration, excessive noise, or improper room temperature. Similarly, physiological responses like an elevated resting heart rate might be attributed solely to stress or physical exertion, ignoring potential environmental triggers like high humidity or low air quality.

[0005] Numerous studies have demonstrated the critical influence of environmental factors on human health and wellbeing. Room temperature, air quality, noise levels, and lighting conditions can directly affect physiological responses such as sleep quality, heart rate, and overall stress levels . For example, high levels of CO2 or pollutants in the air can lead to respiratory distress, poor sleep, and increased stress. Excessive noise can disrupt sleep patterns and elevate heart rate. Inadequate or inappropriate lighting can misalign circadian rhythms, impacting sleep and productivity. Similarly, extremes in temperature and humidity can lead to discomfort, dehydration, and increased cardiovascular strain. Despite the growing body of evidence supporting these correlations, existing health technologies do not adequately integrate environmental data into their analyses, limiting their utility in providing comprehensive and actionable insights.

[0006] Therefore, there is a need for a solution which is able to provide a system, and method that bridges the gap between biological and environmental health monitoring, providing users with a holistic view of their wellbeing.OBJECTIVE OF THE DISCLOSURE

[0007] It is an object of the present disclosure to provide a system and a method for generating personalized environmental sensitivity profiles.

[0008] It is another object of the present disclosure to provide a system and a method for generating personalized environmental sensitivity profile that seamlessly integrate data from wearable devices and home health monitoring sensors to capture both physiological and environmental parameters in real time.

[0009] It is another object of the present disclosure to provide a system and a method for generating personalized environmental sensitivity profile that apply advanced machine learning models to detect and quantify correlations between an individual’s physiological markers and environmental conditions.

[0010] It is another object of the present disclosure to provide a system and a method for generating personalized environmental sensitivity profile that identifies threshold limits for environmental parameters, beyond which an individual’s physiological parameters experience detrimental effects.

[0011] It is another object of the present disclosure to provide a system and a method for generating personalized environmental sensitivity profile that predicts future physiological outcomes and recommends pre-emptive interventions to mitigate the risk of adverse health impacts.

[0012] It is another object of the present disclosure to provide a system and a method for generating personalized environmental sensitivity profile that enables real-time actuation of remedial measures, such as adjusting temperature or controlling air quality, based on user-specific sensitivity thresholds.

[0013] It is another object of the present disclosure to provide a system and a method for generating personalized environmental sensitivity profile that offer intuitive, user-facing interfaces displaying actionable insights, thereby empowering individuals to make informed adjustments for improved health and wellbeing.

[0014] It is another object of the present disclosure to provide a system and a method for generating personalized environmental sensitivity profile that continuously refine its predictive capabilities by incorporating ongoing user data and adaptive learning mechanisms.SUMMARY

[0015] This section is provided to introduce certain aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

[0016] An aspect of the present disclosure may relate to a method for generating a personalized environmental sensitivity profile. The method comprises receiving, by at least one processor, a set of physiological parameters associated with at least one user. Next, the method comprises receiving, by the at least one processor, a set of environmental parameters associated with the at least one user. Herein, the set of environmental parameters are sensed in close proximity of the user. Next, the method comprises analysing, by the at least one processor using a trained model, the set of physiological parameters and the set of environmental parameters to identify one or more correlations between the set of physiological parameters and the set of environmental parameters. Herein, the one or more correlations indicate change in the set of physiological parameters corresponding to change in the set of environmental parameters. Next, the method comprises determining, by the at least one processor, a threshold limit for at least one of the set of environmental parameters that causes breach in a predefined threshold associated with the set of physiological parameters. Next, the method comprises generating, by the at least one processor, at least one personalized sensitivity profile for the at least one user based on the determined threshold limit.

[0017] In an exemplary aspect of the present disclosure, the method further comprises predicting, by the at least one processor, future outcomes of the set of physiological parameters based on the set of environmental parameters.

[0018] In an exemplary aspect of the present disclosure, the method further comprises triggering, by the at least one processor using an actuation unit, based on the prediction, one or more corrective actions based on the predicted future outcomes.

[0019] In an exemplary aspect of the present disclosure, the predefined threshold is determined at least based on one of manual input from the at least one user, and the trained model.

[0020] In an exemplary aspect of the present disclosure, analysing the set of physiological parameters and the set of environmental parameters is performed for at least one of a short-term, a medium-term, and a long-term physiological term.

[0021] In an exemplary aspect of the present disclosure, the set of psychological parameters comprises at least one of sleep quality, heart rate (HR), resting heart rate (rHR), heart rate variability (HRV), physical movement, and skin temperature, associated with the at least one user.

[0022] In an exemplary aspect of the present disclosure, the set of environmental parameters comprises at least one of a room temperature, humidity, noise levels, CO2 level, lighting conditions, radon level, pollen level, formaldehyde (HCHO) level, and electromagnetic fields (EMF) level associated with the at least one user.

[0023] In an exemplary aspect of the present disclosure, the method further comprises rendering, by the at least one processor using a display unit, a set of details associated with the generated at least one personalized sensitivity profile.

[0024] In an exemplary aspect of the present disclosure, the method further comprises assigning, by the at least one processor, a score to each of the set of psychological parameters and the set of environmental parameters, associated with the at least one user.

[0025] In an exemplary aspect of the present disclosure, the model is trained based on historical data associated with the set environmental parameters, and the set of physiological parameters.

[0026] An aspect of the present disclosure may relate to a system for generating personalized environmental sensitivity profile comprising at least one processor. Further, the at least one processor is configured to receive a set of physiological parameters associated with at least one user. Thereafter, the at least one processor is configured to receive a set of environmental parameters associated with the at least one user. Herein, the set of environmental parameters are sensed in close proximity of the user. Thereafter, the at least one processor is configured to analyse, using a trained model, the set of physiological parameters and the set of environmental parameters to identify one or more correlations between the set of physiological parameters and the set ofenvironmental parameters. Herein, the one or more correlations indicate change in the set of physiological parameters corresponding to change in the set of environmental parameters. Thereafter, the at least one processor is configured to determine a threshold limit for at least one of the set of environmental parameters that causes breach in a predefined threshold associated with the set of physiological parameters. Thereafter, the at least one processor is configured to generate at least one personalized sensitivity profile for the at least one user based on the determined threshold limit.

[0027] Another aspect of the present disclosure relates to a non-transitory computer-readable storage medium storing instructions for generating personalized environmental sensitivity profile is disclosed. The instructions include executable code which, when executed by a processor, may cause the processor to receive a set of physiological parameters associated with at least one user; receive a set of environmental parameters associated with the at least one user, wherein the set of environmental parameters are sensed in close proximity of the user; analyse, using a trained model, the set of physiological parameters and the set of environmental parameters to identify one or more correlations between the set of physiological parameters and the set of environmental parameters, wherein the one or more correlations indicate change in the set of physiological parameters corresponding to change in the set of environmental parameters; determine a threshold limit for at least one of the set of environmental parameters that causes breach in a predefined threshold associated with the set of physiological parameters; and generate, at least one personalized sensitivity profile for the at least one user based on the determined threshold limit.BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Having thus described the subject matter of the present disclosure in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

[0029] FIG. 1 illustrates an exemplary system for generating personalized environmental sensitivity profile, in accordance with one or more exemplary embodiments of the present disclosure.

[0030] FIG. 2 illustrates an exemplary process flow diagram for generating personalized environmental sensitivity profile, in accordance with one or more exemplary embodiments of the present disclosure.

[0031] FIG. 3 illustrates an exemplary method flow diagram for generating personalized environmental sensitivity profile, in accordance with one or more exemplary embodiments of the present disclosure.

[0032] FIG. 4 illustrates an exemplary implementation of a system for generating personalized environmental sensitivity profile, in accordance with one or more exemplary embodiments of the present disclosure.

[0033] FIG. 5 illustrates an exemplary implementation for generating personalized environmental sensitivity profile, in accordance with one or more exemplary embodiments of the present disclosure.DETAILED DESCRIPTION

[0034] Exemplary embodiments now will be described with reference to the accompanying drawings. The invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey its scope to those skilled in the art. The terminology used in the detailed description of the particular exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting. In the drawings, like numbers refer to like elements.

[0035] The specification may refer to “an”, “one” or “some” embodiment(s) in several locations. This does not necessarily imply that each such reference is to the same embodiment(s), or that the feature only applies to a single embodiment. Single features of different embodiments may also be combined to enable other embodiments.

[0036] As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless expressly stated otherwise. It will be further understood that the terms “include”, “comprises”, “including” and / or “comprising” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It will be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. Furthermore, “connected” or “coupled” as used herein may include wirelessly connected or coupled. As used herein, the term “and / or” includes any and all combinations and arrangements of one or more of the associated listed items. Also, as used herein, the phrase “at least one” means and includes “one or more” and such phrases or terms can be used interchangeably.

[0037] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this invention pertains. It will be further understood that terms, such as those defined in commonlyused dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0038] The figures depict a simplified structure only showing some elements and functional entities, all being logical units whose implementation may differ from what is shown. The connections shown are logical connections; the actual physical connections may be different.

[0039] In addition, all logical units and / or controllers described and depicted in the figures include the software and / or hardware components required for the unit to function. Further, each unit may comprise within itself one or more components, which are implicitly understood. These components may be operatively coupled to each other and be configured to communicate with each other to perform the function of the said unit.

[0040] In the following description, for the purposes of explanation, numerous specific details have been set forth in order to enable a description of the invention. It will be apparent, however, that the invention may be practiced without these specific details and features.

[0041] Through one or more of its various aspects, embodiments and / or specific features or subcomponents of the present disclosure, are intended to bring out one or more of the advantages as specifically described above and noted below.

[0042] The examples may also be embodied as one or more non-transitory computer-readable storage media having instructions stored thereon for one or more aspects of the present technology as described and illustrated by way of the examples herein. The instructions in some examples include executable code that, when executed by one or more processors, causes the processors to carry out steps necessary to implement the methods of the examples of this

[0043] Existing health monitoring systems primarily focus on isolated datasets. For example, wearable devices excel at tracking biological markers but provide little to no insight into how external environmental factors might influence these markers. Similarly, environmental sensors collect data on air quality, temperature, and humidity but lack the ability to correlate this information with an individual's physiological state. This fragmented approach leads to a limited understanding of the root causes of health issues and reduces the effectiveness of health recommendations. Poor sleep quality detected by a wearable device, for example, may result in generic suggestions like increasing sleep duration without identifying potential environmental causes such as high CO2 concentration, excessive noise, or improper room temperature. Similarly, physiological responses like an elevated resting heart rate might be attributed solely to stress or physical exertion, ignoring potential environmental triggers like high humidity or low air quality.

[0044] To overcome the above-mentioned problems, the present disclosure provides a unified framework that analyses both physiological parameters and environmental factors in tandem, rather than in isolation. By combining data streams from wearable devices such as heart rate variability, sleep quality, and movement with environmental metrics such as CO2 levels, humidity, and noise levels. The present disclosure pinpoints specific correlations between changing environmental conditions and shifts in an individual’s physiological responses. The integration facilitates the identification of precise threshold limits at which environmental factors begin to negatively impact health, thereby offering more targeted recommendations than traditional systems that present only generic advice. Rather than merely noting, for example, that a user’s sleep quality is poor, the proposed method uncovers underlying causes like elevated CO2 or disruptive noise. Likewise, if a user exhibits increased resting heart rate, the system correlates data to determine whether environmental triggers like temperature extremes or poor air quality are contributing factors. By delivering these individualized insights, the present disclosure empowers users to make informed interventions ranging from adjusting indoor climate settings to refining overall lifestyle habits, thus bridging the gap between isolated health metrics and real-world environmental influences.

[0045] Referring to FIG. 1, an exemplary system 100 for generating personalized environmental sensitivity profile is illustrated, in accordance with one or more exemplary embodiments of the present disclosure. With reference to FIG. 1, there is shown a system 100. The system 100 includes at least one processor 102, at least one memory 104, a display unit 106, an input unit 108, and an actuation unit 110. A person of ordinary skill in the art will understand that the system 100 may also include other suitable components or systems, in addition to the components or systems which are illustrated herein to describe and explain the function and operation of the present disclosure. Detailed description of such components or systems has been omitted from the disclosure for the sake of brevity.

[0046] The at least one processor 102 can also be termed as “processing unit” or “operating processor” and may include one or more processors, wherein the processor refers to any logic circuitry for processing instructions. The at least one processor 102 mentioned herein may include multiple processors, parallel processors, or both. The at least one processor 102 may include one or a combination of a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a Digital Signal Processing (DSP) core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The at least one processor 102 may perform signal coding dataprocessing, input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the at least one processor 102 is an article of manufacture and / or a machine component. The at least one processor 102 is configured to execute software instructions in order to perform functions as described in the various implementations herein.

[0047] The at least one processor 102 serves as the central computing component orchestrating the functionalities of the system 100. It is configured to process and analyse incoming data streams from multiple sources, such as environmental sensors, wearable devices, and user inputs, using a set of instructions stored in or accessed by the at least one memory 104. By leveraging various data processing techniques, including real-time signal processing and machine learning techniques, the at least one processor 102 identifies correlations among physiological parameters and environmental factors, calculates threshold limits for specific triggers, and generates personalized environmental sensitivity profiles for the user. These insights drive actions that are then carried out by the actuation unit 110.

[0048] The system further includes at least one memory 104 communicatively coupled to the at least one processor 102. The at least one memory 104 may include a static memory, a dynamic memory, or both in communication. The at least one memory 104 described herein are tangible storage mediums that can store data and executable instructions and are non-transitory during the time instructions are stored therein. The at least one memory 104 is an article about manufacturing and / or machine components. The at least one memory 104 described herein are computer-readable storage mediums from which data and executable instructions can be read by the at least one processor 102. The at least one memory 104 can be at least one of a random access memory (RAM), read-only memory (ROM), flash memory, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, a hard disk, a cache, a removable disk, tape, compact disk read-only memory (CD-ROM), digital versatile disk (DVD), floppy disk, Blu-ray disk, or any other form of storage medium known in the art. The at least one memory 104 can be volatile or non-volatile, secure and / or encrypted, unsecure and / or unencrypted. As regards the present disclosure, the system 100 may comprise any combination of aforementioned memory or a single storage memory.

[0049] The system 100 further includes at least one display unit 106, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid-state display, a cathode ray tube (CRT), a plasma display, or any other type of display, examples of which are well known to skilled persons. The at least one display unit 106 is further associated with the atleast one processor 102 through a graphical user interface (GUI) that may display / render a set of details generated by the at least one processor 102.

[0050] The system 100 further comprises at least one input unit 108, such as a touch-sensitive input screen or pad, a speech input, a mouse, a remote -control device having a wireless keypad, a microphone coupled to a speech recognition engine, a camera such as a video camera or still camera, a cursor control device, or any combination thereof. Those skilled in the art appreciate that various embodiments of the system 100 may include multiple input units. Moreover, those skilled in the art further appreciate that the above-listed, exemplary input units are not meant to be exhaustive and that the system 100 may include any additional, or alternative, input units 108. A keyboard, touch-sensitive input screen or pad, a mouse, a microphone connected to a speech recognition engine, a remote-control device with a wireless keypad, a camera (either still or video), or any combination of these may also be included in the system 100. Person skilled in the art will appreciate that different iterations of the system 100 can have more than one input unit 108 and that the system 100 may have any additional or different input units and that the exemplary input units mentioned above are not intended to be all-inclusive.

[0051] The system further comprises at least one actuation unit 110 to perform one or more actions based on commands provided by the at least one processor 102. The actuation unit 110 is further connected to one or more devices that can be controlled based on the command provided the at least one processor 102. The actuation unit 110 in the system 100 is configured to execute commands from the at least one processor 102 to modify or adjust various external devices and settings in real time. The actuation unit 110 can be integrated with a range of controllable systems, such as HVAC units, lighting systems, or any other environmental and home health devices that can receive operational instructions digitally. Upon receiving data-driven triggers or threshold breaches from the at least one processor 102, the actuation unit 110 initiates predetermined responses. For example, lowering room temperature, activating humidifiers, adjusting ambient lighting, or enabling air purifiers. By incorporating actuators capable of precise mechanical or electrical control (e.g., motors, solenoids, or servo mechanisms), the actuation unit 110 can promptly respond to changes in either physiological or environmental parameters. This responsiveness enables that a user’s environment remains optimized and that corrective actions are taken seamlessly and automatically, according to the personalized sensitivity profiles generated by the at least one processor 102.

[0052] FIG. 2 illustrates an exemplary process flow diagram 200 for generating personalized environmental sensitivity profile, in accordance with one or more exemplary embodiments of the present disclosure. With reference to FIG. 2, there is shown a process flow diagram 200. Theprocess flow diagram 200 includes the at least one processor 102, the at least one memory 104, the display unit 106, the input unit 108, the actuation unit 110, a first device 202, a second device 204, and a trained model 206.

[0053] As illustrated, the at least one processor 102 receives a set of physiological parameters associated with at least one user. Herein the at least one processor 102 may be configured with the first device 202 capable of sensing the at least one physiological parameter from the set of physiological parameters associated with the at least one user. The first device 202 is further configured to collect, monitor, and track physiological parameters associated with the at least one user based on the set of psychological parameters obtained by one or more sensors associated with the first device 202.

[0054] The first device 202 may include at least one of wearable device or similar electronic devices that include but are not limited to a smart ring, a smart watch, a fitness tracker band, a smart jewellery, a smart glasses, a smart shoes and alike that would be known to a person skilled in the art. Further, the wearable devices may function solely or can be operationally coupled with any other wearable device to intake the required physiological parameters as per requirement of the at least one process.

[0055] In an exemplary aspect, the wearable devices are meant to be worn over different body portions of the user, which may include but is not limited to a finger portion, a wrist portion, a head portion, an arm portion, a hand portion, a leg portion, and alike that would be well known to a person skilled in the art. For example, in an event, the wearable device is the smart ring and alike, then the smart ring is meant to be worn on the finger portion (preferably index finger, ring finger, and middle finger) of the user. Likewise, if the wearable device is the smart watch and alike, then the smart watch is preferably meant to be worn on the wrist portion of the user. Further, in another aspect, the mentioned wearable devices can associate with an apparel of the user. For example, the mentioned wearable device can be placed within a pocket and / or pouches of apparel or can be clipped and / or pinned with the apparel.

[0056] The first device 202 may include any sleep monitoring device such as sleep trackers or smart mattresses associated with the user. The sleep monitoring devices are capable of monitoring sleep patterns, breathing, and physical movement of the user. The first device 202 may further include a smart phone associated with the user. The smart phone viatheir built-in sensors is capable of measuring physical movement of the user for a desired time period (for example, hourly, daily, weekly, monthly and alike).

[0057] Further, examples of the set of physiological parameters may include at least one of sleep quality, heart rate (HR), resting heart rate (rHR), heart rate variability (HRV), physical movement, and skin temperature, associated with the at least one user. It would be appreciated by the person skilled in the art that a physiological parameter or a combination of physiological parameters from the set of physiological parameters is able to provide useful insights about the health and overall well-being of the user.

[0058] In an exemplary implementation, the physiological parameter is the sleep quality parameter. The sleep quality parameter includes factors such as duration of sleep, sleep stages (light, deep, and REM sleep), and sleep interruptions. The first device 202 may comprise one or more motion sensors (e.g., accelerometers) to detect movements of the user during sleep. The first device 202 may further include one or more optical sensors to monitor heart rate changes, and one or more temperature sensors (particularly skin temperature sensors) to detect skin temperature variations that correspond to sleep cycles.

[0059] For example: if the first device 202 detects a frequent movement or an elevated heart rate of the user during their sleep, then it may correspond to a fragmented sleep or light sleep. Conversely, if the first device detects less motion and a stable heart rate of the user during their sleep, then it may correspond to a deep or restorative sleep.

[0060] In another exemplary implementation, the physiological parameter is the heart rate parameter. The heart rate parameter may correspond to a number of heart beats per minute of the user to gain insights of cardiovascular health and activity levels of the user. The first device 202 may comprise photoplethysmography (PPG) sensors to measure changes in blood volume under the skin of the user. Herein, the PPG sensor emits light into the skin of the user, and the reflected light is analysed to determine the heart rate of the user. The heart rate parameter may further vary based on the physical movement of the user. For example: in an event, the user may perform an exercise or any other athletic activity, then the first device 202 may detect an increased heart rate, such as 150 bpm. In another event, the user is at rest / stable then the first device may detect a normal heart rate, such as 70 bpm.

[0061] In yet another exemplary implementation, the physiological parameter is the resting health rate parameter to measure the heart rate of the user, when the user is at complete rest such as during sleep of any prolonged resting break. For example: if the first device detects the resting health rate parameter of 60 bpm, indicating good cardiovascular health of the user. However, in an event, the resting health parameter increases over 70 bpm, then this may indicate fatigue, illness, or stress experienced by the user.

[0062] In yet another exemplary implementation, the physiological parameter is the heart rate variability parameter. The heart rate variability parameter may refer to a variation in time intervals between consecutive heartbeats of the user. The heart rate variability parameter may further indicate the balance between the sympathetic and parasympathetic nervous systems of the user.

[0063] In yet another exemplary implementation, the physiological parameter is the physical movement parameter. The physical movement parameter is generally used for tracking activity levels, step count, and movement patterns of the user. The first device 202 may comprise an accelerometer and a gyroscope to detect the physical movement parameter of the user.

[0064] In yet another exemplary implementation, the physiological parameter is the skin temperature parameter. The skin temperature parameter may indicate the thermoregulation, stress, and overall health of the user. The first device 202 may comprise a skin temperature sensor to measure changes in skin temperature of the user. For example: the skin temperature of the user may slightly drop during sleep. However, a sudden increase in the skin temperature during sleep may indicate fever or illness experienced by the user.

[0065] It is to be noted that the first device 202 may comprise any other sensor that is capable of detecting the aforementioned set of physiological parameters, which would be known to a person skilled in the art.

[0066] Further, the first device 202 is connected to the at least one processor 102 via a wired / wireless connection such as Bluetooth, Wi-Fi, cloud servers, USB or proprietary cables and similar that would be known to a person skilled in the art.

[0067] Herein, each parameter from the set physiological parameters is measured in real time or at scheduled time intervals via the one or more sensors embedded within the first device 202. The first device 202 may continuously transmit raw data (e.g., heart rate beats per minute, temperature readings, movement patterns) received from the one or more sensors to the at least one processor 102. The at least one processor 102 post receiving the raw data processes the raw data to obtain the set of physiological parameters, such as daily activity summaries, stress levels, or sleep efficiency of the user.

[0068] The at least one processor 102 post obtaining the set of physiological parameters may further store the set of physiological parameters to the at least one memory 104 or a cloud storage for further analysis, pattern recognition, or generating health recommendations as per requirement.

[0069] The system 100 further receives a set of environmental parameters associated with the at least one user, the set of environmental parameters are sensed in close proximity to the user. Thesystem 100 herein may incorporate the second device 204 to measure the set of environmental parameters around the user.

[0070] The second device 204 may include a home security system (HSS) to monitor the set of environmental parameters around the user. The second device 204 can further include any other devices capable of measuring an environmental parameter or a combination of environmental parameters from the set of environmental parameters. Herein, the other devices may include at least one of a thermostats, air purifiers, wearable devices that are integrated with environmental sensors, or any other heating ventilation air conditioning (HVAC) system that are cable of measuring the set of environmental parameters. Herein, the set of environmental parameters may include, but not limited only to, at least one of a room temperature, humidity, noise levels, CO2 level, lighting conditions, radon level, pollen level, formaldehyde (HCHO) level, and electromagnetic fields (EMF) level associated with the at least one user.

[0071] In an exemplary implementation, the environmental parameter is the room temperature parameter. The room temperature parameter is utilized to measure the ambient temperature of a surrounding environment that is in close proximity to the user. The second device 204 may include temperature sensors such as thermistors or thermocouples that continuously monitor the air temperature and send the raw data to the at least one processor.

[0072] In another exemplary implementation, the environmental parameter is the humidity parameter. The humidity parameter refers to the amount of moisture in the air around the user, which may influence comfort and respiratory health of the user. The second device 204 may include hygrometers or capacitive humidity sensors to measure relative humidity levels around the user.

[0073] In yet another exemplary implementation, the environmental parameter is the noise level parameter. The noise level parameter is utilized to measure an ambient sound intensity around the user. The second device 204 may include microphones or sound level meters to capture sound waves, which are further labelled as decibel (dB) levels.

[0074] In yet another exemplary implementation, the environmental parameter is the CO2 level parameter. The CO2 level parameter is utilized to measure carbon di-oxide levels in the surrounding air of the user. The second device 204 may include NDIR (Non-Dispersive Infrared) CO2 sensors or any other CO2 sensors to measure the CO2 concentration in the surrounding air of the user. Herein, the CO2 concentration is preferably measured in parts per million (ppm).

[0075] In yet another exemplary implementation, the environmental parameter is the lighting conditions. The lighting conditions around the user may affect vision, mood, and circadian rhythmof the user. The second device 204 may include ambient light sensors or photodiodes to measure the light intensity in lux and evaluate spectrum in order to analyse the lighting conditions around the user.

[0076] In yet another exemplary implementation, the environmental parameter is the radon level parameter. The radon level parameter is utilized to detect the presence of radon gas around the user. Radon gas is a radioactive gas that can be detected in a close environment and may pose health risks to the user. The second device 204 may comprise radon detectors to detect the presence of radon gas around the user.

[0077] In yet another exemplary implementation, the environmental parameter is the pollen level parameter. The pollen level parameter is utilized to measure the concentration of allergenic particles in the air around the user. High pollen parameters may further affect the users that are sensitive to allergies or are suffering from conditions such as asthma. The second device 204 may comprise optical particle counters or laser-based pollen detectors to detect the concentration of allergenic particles in the air around the user.

[0078] In yet another exemplary implementation, the environmental parameter is the HCHO level parameter. The HCHO level parameter is utilized to measure the level of Formaldehyde present around the user. Formaldehyde is a volatile organic compound (VOC) found in indoor air which is majorly emitted by furniture and construction materials. The concentration of Formaldehyde is measured in parts per billion (ppb). The concentration of Formaldehyde above a threshold level may further pose respiratory health problems to the user. The second device 204 may comprise an electrochemical sensors or photoionization sensors to measure the concentration of Formaldehyde around the user.

[0079] In yet another exemplary implementation, the environmental parameter is the EMF level parameter. The EMF level parameter is utilized to measure the exposure of electromagnetic radiation around the user. A high exposure of electromagnetic radiation around the user may potentially affect sleep, health, or mood of the user. The second device 204 may comprise an EMF meter or Gauss meter to detect the exposure of electromagnetic radiation around the user.

[0080] It is to be noted that the second device 204 may comprise any other units that are capable of detecting the aforementioned set of environmental parameters, which would be known to a person skilled in the art.

[0081] Further, the second device 204 is connected to the at least one processor 102 via a wired / wireless connection such as Bluetooth, Wi-Fi, cloud servers, USB or proprietary cables and similar that would be known to a person skilled in the art.

[0082] Herein, each parameter from the set of environmental parameters is measured in real time or at scheduled time intervals via the one or more units embedded within the second device 204. The second device 204 may continuously transmit raw data (e.g., temperature in degrees, CO2 levels in ppm, or noise in decibels) received from the one or more units to the at least one processor 102. The at least one processor 102 post receiving the raw data processes the raw data to obtain the set of environmental parameters.

[0083] The at least one processor 102 post obtaining the set of environmental parameters may further store the set of environmental parameters to the at least one memory 104 or a cloud storage for further analysis, pattern recognition, or generating health recommendations as per requirement.

[0084] Further, the at least one processor 102 is communicatively coupled with at least one trained model 206 in order to analyse the set of physiological parameters (e.g., heart rate, sleep quality) and the set of environmental parameters (e.g., temperature, CO2 levels) to identify one or more correlations between the set of physiological parameters and the set of environmental parameters. The at least one processor 102 may further use a unique analysis to correlate the set of physiological parameters and the set of environmental parameters. The trained model 206 is configured to interpret and learn from both historical and real-time data streams. By continuously ingesting physiological metrics, such as heart rate or sleep patterns, and correlating them with various environmental indicators, like room temperature, humidity, or noise levels, the trained model 206 refines its understanding of the user’s unique sensitivities over time. During an initial training phase, large volumes of data, often collected from both user-specific inputs and broader population datasets, are processed to identify patterns and thresholds that accurately predict how changes in environmental parameters affect physiological responses. Subsequently, the trained model 206 updates itself incrementally through periodic retraining sessions, incorporating new observations to enhance predictive accuracy. The trained model 206 not only establishes cause- and-effect relationships between environmental conditions and personal wellbeing but also guides the system 100 in generating or adjusting personalized recommendations, triggering automated interventions via the actuation unit 110, and ultimately contributing to a dynamically evolving, user-centric sensitivity profile.

[0085] In one example, a sudden increase in real-time heart rate may correlate with exposure to high noise levels during the night, further indicating a disrupted sleep pattern. In another example, a prolonged exposure to elevated CO2 levels or inconsistent room temperatures may lead to a poor sleep pattern. In yet another example, a frequent high pollen exposure correlating with increased heart rate and reduced HRV may indicate an allergic reaction to the user. In yet another example, in an event of workout, the user may experience a slight fluctuation in resting heart rateduring different parts of the day (e.g., morning, noon, lunch), or the user may get tired easily in hot and humid conditions.

[0086] The at least one processor 102 cross-references the data received from the set of physiological parameters and the set of environmental parameters to find one or more correlations indicating change in the set of physiological parameters corresponding to change in the set of environmental parameters. For example, the at least one processor 102 may correlate that the user may face problems in sleeping if the room temperature exceeds 20°C or the noise levels around the user increases 40 dB.

[0087] The trained model 206 utilizes historical data associated with the set environmental parameters, and the set of physiological parameters. The trained model 206 may firstly obtain historical data such as heart rate, HRV, and other physiological parameters to detect any similar trends in the physiological behaviour of the user. The trained model 206 may then obtain the historical data such as temperature, humidity, pollen levels, and other environmental parameters to monitor environment conditions around the user. The trained model 206 may then use a machine learning technique to identify any consistent correlation between any one or a combination of physiological and environmental parameters, respectively. For example, the at least one processor 102 may monitor that the heart rate parameter of the user rises by 5% when the C02 level parameter exceeds 800 ppm, especially during nighttime.

[0088] The at least one processor 102 may further assign a score to each psychological parameter from the set of psychological parameters, and each environmental parameter from the set of environmental parameters associated with the at least one user. The assignment of score by the at least one processor 102 may assist the trained model 206 in recognizing one or more patterns and simplify the correlation between the set of psychological parameters and the set of environmental parameters. For example, a heart rate variability parameter score (such as 100) may drop down to a heart rate variability parameter score (such as 70) based on the influence of the room temperature parameters score (60). Further, a sleep quality parameter score (such as 100) may drop down to a sleep quality parameter score (such as 40) based on the influence of noise level parameter score (such as 50) and the CO2 level score (such as 60).

[0089] The at least one processor 102 determines a threshold limit for at least one of the set of environmental parameters that causes breach in a predefined threshold associated with the set of physiological parameters. For example, in an event, the heart rate variability parameter of a user drops when the noise level parameter exceeds 55dB. In another event, the sleep quality parameter of the user decreases when the temperature of the room exceeds 22°C.

[0090] It is to be noted that the set of physiological parameters of a user may behave differently from another user under the same set of environmental parameters. For example, a user A is highly sensitive to noise level parameter (threshold limit is 40 dB) but is not affected by any minute change in the room temperature parameter (threshold limit is 35°C). Further, a user B is sensitive to the humidity parameter (threshold limit is 70%) but may not be affected by the noise level parameter (threshold limit is 70dB). It is to be further noted that the above stated value used while explaining the parameters are just exemplary to explain the statement and may differ in real life.

[0091] The predefined threshold is determined at least based on one of manual input from the at least one user, and on the trained model. The user may utilise the at least one of an input unit 108 to provide their preferred set of environmental parameters, tolerable set of environmental parameters, and sensitive set of environmental parameters. The at least one processor 102 may further account the factors stated by the user during the correlation of the set of psychological parameters and the set of environmental parameters.

[0092] The at least one processor 102, further generates at least one personalized sensitivity profile for the at least one user based on the determined threshold limit. The at least one personalized sensitivity profile herein displays the set of psychological parameters and the set of environmental parameters along with the scores of each parameter. Further, the at least one personalized sensitivity profile further comprises the threshold limit. Furthermore, the at least one personalized sensitivity profile may further comprise a combination of a set of environmental parameters that influences the set of psychological parameters for each user. For example, the personalized sensitivity profile of a user XYZ may contain the user XYZ may experience a reduction in the sleep quality parameter, when the noise level parameter is above 55dB. Similarly, the user XYZ may experience an increment in the heart rate parameter, when the room temperature parameter increases from 25°C.

[0093] It is to be noted that the at least one processor 102 may further collect additional data apart from the manual input data to determine the threshold limit of a particular user. The additional data mentioned herein may comprise a historical response pattern, a genetic predisposition data, a current health status, and time-of-day.

[0094] The historical response pattern refers to an analysis of outcomes of the set of physiological parameters based on the set of environmental parameters for a particular user. Further, the genetic predisposition data may refer to genetic data associated with stress, allergens, or sleep disturbances of the particular user. For example, a user with genetic predisposition to respiratory conditions may clearly have a lower pollen level parameter in comparison to a healthyuser. Furthermore, the current health status is a real-time set of physiological parameters, and the real time set of environmental parameters. The requirement of real-time parameters is important to notify that any set of physiological parameters at a particular instant may not affect the overall threshold limit of the user. For example, in such an event the user is currently suffering from fever, then in such event the user may have a lower tolerance to room temperature parameters than usual days. Therefore, in such events, the at least one processor 102 may ignore the real time parameters while evaluating the threshold limit of the user. Furthermore, the time-of-day variations may affect a physiological parameter of the user. For example, a user is more sensitive to the noise level parameter at bedtime than the morning.

[0095] Further, the set of physiological parameters and the set of environmental parameters are monitored for at least one of a short-term, a medium-term, and a long-term physiological term. Herein, the short-term may refer to a change in an immediate or close to real-time set of physiological parameters due to at least one set of environmental parameters. For example, in an event the user may experience a sudden heart rate variability drop due to an increase in the noise level parameter (suppose 30dB to 60dB). The analysis of the set of physiological parameters and environmental parameters is performed over different time scales, short-term, medium-term, and long-term physiological terms, to capture a comprehensive understanding of how environmental conditions impact the user’s health and wellbeing.

[0096] In the short-term, the analysis focuses on immediate or near-real-time correlations. For example, the system might detect how a sudden increase in noise levels impacts the user's heart rate variability or stress levels within minutes or hours. This short-term analysis is particularly useful for triggering real-time interventions, such as lowering noise or adjusting lighting conditions, to address immediate discomfort or physiological stress. In the medium-term, the analysis examines trends that develop over days or weeks. For example, the system might observe how changes in room temperature or humidity over a week affect the user's sleep quality or resting heart rate. This timeframe is valuable for identifying patterns that may not be apparent in shortterm analysis, such as how consistent exposure to poor air quality affects daily recovery or energy levels. In the long-term, the system analyses physiological and environmental interactions over months or years. The analysis facilitates gradual shifts in the user's sensitivity to environmental factors, such as increasing sensitivity to temperature extremes or a decline in tolerance to high CO2 levels. Long-term analysis also provides insights into how lifestyle changes, aging, or chronic exposure to specific environmental conditions contribute to shifts in physiological baselines.

[0097] Further, the medium -term responses may refer to a psychological adaptation of a user over a period of time (such as weeks, months), on the set of physiological parameters due tochanges in the set of the environmental parameters. For example, increment in tolerance of the sleep quality parameter due to changes in humidity parameter over a week. Furthermore, the longterm responses may refer to an identification of evolving patterns in physiological and environmental tolerance over months or years.

[0098] The at least one processor 102 is further communicatively coupled with the display unit 106. Herein, the display unit 106 is configured to render the set of details associated with the generated at least one personalized sensitivity profile. In an implementation, the display unit 106 may render a clear correlation and threshold limit of the user to their corresponding set of environmental parameters. In another implementation, the display unit 106 may render suggestions or warnings regarding the set of environmental parameters that majorly affects the set of physiological parameters of the particular user. In yet another implementation, the display unit 106 further renders real-time tracking of the set of physiological parameters and the set of environmental parameters. In yet another implementation, the display unit 106 renders the historic data of the user based on different sets of environmental parameters over a period of time.

[0099] The at least one processor 102 is configured to analyse historical and real-time data from both environmental sensors and physiological metrics, enabling it to forecast how a user’s physiological parameters, such as heart rate, sleep quality, or stress levels, might change under future environmental conditions. By identifying patterns in how specific parameters (e.g., temperature, humidity, noise levels) correlate with the user’s physiological responses, the at least one processor 102 can recognize emerging trends and anticipate upcoming changes in health status. For example, if historical data indicates that a user’s heart rate consistently rises and sleep quality diminishes when the bedroom temperature exceeds 26 degrees Celsius, then, upon detecting that the temperature will reach or exceed this threshold, the at least one processor 102 can predict a potential decline in sleep quality. The system can, thus, proactively recommend or trigger environmental adjustments, such as lowering the thermostat or turning on a fan, to help the user maintain optimal physiological conditions before adverse symptoms occur.

[0100] The system 100 comprises the actuation unit 110 operatively coupled to the at least one processor 102, enabling immediate and automated responses to predicted adverse outcomes. Specifically, once the at least one processor 102 anticipates a likely change in a user’s physiological parameters, based on current or upcoming environmental conditions, it communicates a signal to the actuation unit 110. In turn, the actuation unit 110 may carry out corrective actions, such as adjusting room temperature, humidity, or lighting to prevent the forecasted decline in the user’s wellbeing. For example, if the system 100 predicts that excessivebedroom humidity will negatively impact sleep quality, the actuation unit can activate a dehumidifier or modify ventilation settings before the humidity threshold is reached.

[0101] The at least one processor 102 can be configured to monitor and manage sleep apnoea and snore detection to observe sleep quality parameter of the user. The at least one processor 102 can leverage data from the first device 202 and the second device 204 to monitor and manage sleep apnoea and snore detection. Sleep apnoea is a condition characterized by repeated interruptions in breathing during sleep, often resulting in poor sleep quality and potential long-term health complications. Snore detection, a common indicator of sleep apnoea, can be performed using the raw microphone data from the second device 204 in conjunction with sleep data from the first device 202. The second device 204 may include high-sensitivity microphones capable of capturing raw audio data during the user's sleep. The raw audio data is processed to identify patterns consistent with snoring, such as prolonged low-frequency sound waves with intermittent pauses or spikes. Advanced signal processing techniques, including spectral analysis and the machine learning techniques may be employed to distinguish snoring from other ambient noises.

[0102] In parallel, the first device 202, equipped with motion sensors, optical heart rate sensors, and skin temperature sensors, continuously monitors sleep stages and physiological responses such as heart rate variability (HRV) and breathing patterns. The system 100 cross- references snore patterns detected by the second device 204 with physiological anomalies recorded by the first device 202. For example, prolonged snoring episodes, coupled with irregular breathing patterns or sudden changes in heart rate detected by the first device 202, may indicate potential sleep apnoea events. Furthermore, fluctuations in oxygen saturation levels, as captured by photoplethysmography (PPG) sensors in the first device 202, may provide additional corroboration of sleep apnoea occurrences. The at least one processor 102 processes the synchronized data streams from the first device 202 and the second device 204 to establish correlations between snoring, physiological changes, and environmental factors. For example, the system might identify that snoring intensity and frequency increase when the room's humidity level is high or when the CO2 concentration exceeds a certain threshold. The trained model 206 is further optimized to learn from historical data and user-specific inputs to refine its ability to detect and predict sleep apnoea and snoring -related disruptions. The display unit 106 renders actionable insights for the user, such as a detailed sleep apnoea and snoring report highlighting the frequency, duration, and intensity of snoring episodes, as well as their impact on physiological parameters like heart rate and sleep quality. It may also provide personalized recommendations, such as adjusting room temperature, humidity, or sleeping posture, to mitigate snoring and improve overall sleep health. Additionally,the system 100 may proactively trigger corrective actions through the actuation unit 110, such as adjusting the bedroom environment or activating anti-snoring interventions.

[0103] It would be appreciated by the person skilled in the art that by integrating snore detection and sleep apnoea analysis into its comprehensive health monitoring framework, the system 100 offers a robust solution for enhancing sleep quality and identifying early signs of potential sleep disorders.

[0104] FIG. 3 illustrates an exemplary method flow diagram for generating personalized environmental sensitivity profile, in accordance with one or more exemplary embodiments of the present disclosure. With reference to FIG. 3 there is shown a flow diagram 300. The operations of the flow diagram 300 starts at 302 and proceed to 304.

[0105] At 304, the method includes receiving, by at least one processor 102, the set of physiological parameters associated with the at least one user. At 304, the method includes obtaining, by the at least one processor 102, relevant health metrics reflecting the user’s physical state. The set of physiological parameters may include, for example, heart rate, sleep duration, resting heart rate, or skin temperature, which can be gathered in real time via wearable devices or periodically uploaded from other sources. Once acquired, the set of physiological parameters enable the at least one processor 102 to establish a baseline profde of the user’s current health status, forming the foundation for subsequent analyses of correlations with environmental data and the generation of personalized health insights.

[0106] At 306, the method includes receiving, by the at least one processor 102, the set of environmental parameters associated with the at least one user, wherein the set of environmental parameters are sensed in close proximity of the at least one user. At 306, the method involves collecting a range of environmental data points from sensors located near the user, such that the measurements accurately reflect the conditions most likely to affect them. These sensors may measure factors such as temperature, humidity, noise levels, or air quality in immediate proximity to the user’s living or working space. By gathering these environmental parameters, the system can create a granular, context-specific view of the user’s surroundings, which, when analysed alongside the user’s physiological parameters, helps identify how changes in the environment correlate with fluctuations in the user’s health metrics.

[0107] At 308, the method comprises analysing, by the at least one processor 102 using the trained model 206, the set of physiological parameters and the set of environmental parameters to identify one or more correlations between the set of physiological parameters and the set of environmental parameters. The one or more correlations indicate change in the set of physiologicalparameters corresponding to change in the set of environmental parameters. At 308, the method involves applying the trained model 206 to concurrently examine both the collected physiological parameters and the environmental parameters, thereby identifying potential cause-and-effect relationships. The at least one processor 102 leverages historical patterns and real-time data to determine how specific shifts in environmental conditions, such as rising room temperature or elevated CO2 levels, might trigger or exacerbate certain physiological changes (e.g., increased heart rate or reduced sleep quality). The trained model 206, thus, moves beyond merely cataloguing static readings, providing a deeper understanding of how different variables interact over time. The insight helps the system recognize that, for example, a drop in humidity might correspond to an improvement in sleep quality, or that prolonged exposure to high noise levels can lead to elevated stress indicators.

[0108] At 310, the method includes determining, by the at least one processor 102, a threshold limit for at least one of the set of environmental parameters that causes breach in a predefined threshold associated with the set of physiological parameters. At 310, the method includes calculating a threshold value for one or more environmental variables, such as temperature, humidity, or noise, beyond which a user’s physiological parameters would likely surpass their predetermined safe range. By comparing historical and real-time data, the at least one processor 102 determines the exact level at which an environmental parameter, if exceeded, can result in a breach of the user’s physiological threshold. For example, should the system detect that a room temperature above 28 degrees Celsius consistently elevates the user’s resting heart rate to unhealthy levels, it will designate 28 degrees Celsius as the threshold limit.

[0109] At 312, the method includes generating, by the at least one processor 102, at least one personalized sensitivity profile for the at least one user based on the determined threshold limit. At 312, the method includes creating at least one profile for the user, reflecting the specific environmental conditions that may lead to adverse physiological responses based on the threshold limits identified in the previous step. By integrating information on how each environmental variable can breach an individual’s safe range of biological parameters, the at least one processor 102 facilitates in compiling a “sensitivity map” that highlights the user’s unique vulnerabilities, such as heightened sensitivity to temperature, humidity, or certain air pollutants. The personalized sensitivity profile not only encapsulates current data but can also be updated over time, capturing evolving patterns such that the user’s environment remains aligned with their ongoing health and wellbeing needs.

[0110] The method further includes assigning a quantitative score to each of the set of psychological parameters and the set of environmental parameters associated with the user. Thescoring system is designed to provide a structured, comparable framework for assessing the relative importance or impact of the set of parameters on the user’s overall health and wellbeing. For example, psychological parameters such as sleep quality, stress levels, or heart rate variability might receive scores based on their deviation from the user’s baseline or optimal range. Similarly, environmental parameters like temperature, humidity, noise levels, or air quality are scored based on their alignment with the user’s identified sensitivity thresholds.[oni] These scores serve multiple purposes. First, they allow the at least one processor 102 to prioritize parameters that have the most significant influence on the user’s physiological state. For example, if the score for high noise levels indicates a strong negative impact on the user’s sleep quality, this parameter will be flagged as a high-priority factor for intervention. Second, these scores help in creating a more personalized and actionable sensitivity profile by quantifying the interplay between various physiological and environmental factors. The scores are also dynamic, meaning they can adjust over time as the system collects more data and refines its understanding of the user’s unique sensitivities.

[0112] This scoring mechanism enhances the system’s ability to generate meaningful insights and recommendations. For example, if a particular combination of high temperature and low humidity consistently correlates with elevated stress levels, the at least one processor 102 assigns higher scores to these environmental conditions and includes them as key factors in personalized recommendations or automated corrective actions. By leveraging these scores, the system enables a targeted, data-driven approach to improving the user’s health and wellbeing.

[0113] In accordance with the present disclosure, crowd-sourced environmental alerts are generated by leveraging data inputs from multiple users, each equipped with environmental sensors or wearable devices that communicate with the system’s processor. The system 100 consolidates and analyses these anonymized data streams to detect acute changes, such as rising pollution levels, heightened noise, or elevated allergen concentrations, in geographically defined areas. Once a threshold is breached, the at least one processor 102 triggers community notifications, alerting users in the vicinity of the affected zone through a user interface . These alerts are enriched by a feedback loop in which recipients can confirm observed conditions or submit additional sensor readings, thus enhancing the accuracy and timeliness of future alerts.

[0114] Beyond individual -level notifications, the invention employs an adaptive analysis module that aggregates regional data to develop community-based mitigation recommendations. For example, it may prompt individuals to avoid certain routes or reschedule outdoor activities when poor air quality or excessive pollen levels are detected. This shared information can be further escalated to local policymakers or community organizations, guiding infrastructuredecisions like creating green corridors, regulating traffic, or installing localized air filtration units. As fresh data continuously flows into the system, the at least one processor 102 refines both the alerts and the recommended mitigation measures, enabling them to remain responsive to evolving environmental patterns. It would be appreciated by the person skilled in the art that by integrating user-generated data, personalized sensitivity profiles, and collective insights, the present disclosure provides an all-encompassing mechanism for communities to respond proactively and collaboratively to environmental health risks.

[0115] FIG. 4 illustrates an exemplary implementation of a system for generating personalized environmental sensitivity profile, in accordance with one or more exemplary embodiments of the present disclosure. With reference to FIG. 4, there is shown a block diagram 400.

[0116] The system integrates data from two primary sources: biological parameters 402 (such as physiological parameters) and environmental parameters 404. Biological parameters 402 (such as physiological parameters), such as heart rate, sleep quality, and resting heart rate, are obtained from wearable devices or other physiological monitoring systems. The environmental parameters 404, such as room temperature, humidity, noise levels, and air quality, are captured by environmental sensors located in close proximity to the user. These data streams are fed into the integration and analysis module 406, where they are processed and analysed using trained machine learning models.

[0117] The integration and analysis module 406 evaluates correlations between the biological parameters 402 (such as physiological parameters) and environmental parameters 404 to identify patterns and threshold limits that define the user’s sensitivity to environmental conditions. Based on this analysis, the integration and analysis module generates a personalized sensitivity profile and transmits this information to a display device 410, where the user can view the insights in an intuitive, user-friendly interface. Additionally, the system synchronizes the processed data with a cloud server 408 for secure storage, real-time updates, and further analysis. The cloud integration enables continuous refinement of the sensitivity profile by incorporating new data over time, such that the profile remains accurate and adaptive to changes in the user’s environment and physiological state. It would be appreciated by the person skilled in the art that the present disclosure utilizes data integration, analysis, and cloud connectivity to deliver highly personalized and actionable health insights to the user.

[0118] FIG. 5 illustrates an exemplary implementation for generating personalized environmental sensitivity profile, in accordance with one or more exemplary embodiments of the present disclosure. With reference to FIG. 5, there is shown a block diagram 500. The block diagram includes at least one of ring device 502a, smart watch 502b, smart band 502c, continuousglucose monitoring (CGM) device 502d, home environment monitoring device 504, cloud server 506, display device 508, and user equipment (UE) 510.

[0119] As illustrated, the system integrates multiple devices and components to gather data, analyse it, and provide actionable insights to users. At the core of the system is the cloud server 506, which serves as the central node where various devices interact. Data from both wearable devices and environmental monitoring devices are collected and processed to create a personalized sensitivity profde.

[0120] As illustrated, the ring device 502a, the smartwatch 502b, and the smart band 502c represent wearable devices that continuously monitor the user's physiological parameters, such as heart rate, sleep quality, movement, and temperature. The wearables devices (502a, 502b, and 502c) and the CGM device 502d provide complementary physiological data, including blood glucose levels or stress-related metrics, further enhancing the depth of the analysis. This multidevice integration allows the system to capture a holistic view of the user's biological state. The wearable devices (502a, 502b, and 502c) and the CGM device 502d transmit data to the cloud server 506 in real time, such that the user's physiological state is constantly updated for analysis. In an embodiment, the data from the wearable devices (502a, 502b, and 502c) and the CGM device 502d is directly transmitted to the cloud server. In another embodiment, the data from the wearable devices (502a, 502b, and 502c) and the CGM device 502d is transmitted to the cloud server 506 via the UE 510.

[0121] The home environment monitoring device 504 collects environmental parameters, such as room temperature, humidity, CO2 levels, noise levels, and lighting conditions. The environmental metrics are analysed alongside the physiological parameters, at the cloud server 506, to identify correlations and thresholds that define the user’s unique sensitivities. The cloud server 506 enables secure data storage and provides additional computational resources for machine learning that refine the sensitivity profiles based on historical data and trends. The cloud connectivity also allows the system to integrate updates and learn from a broader dataset, enabling continuous improvement in predictive accuracy.

[0122] The processed data and personalized sensitivity profiles are made accessible to the user via a display device 508, which could be a smartphone, tablet, or dedicated health monitoring interface. The display device 508 provides users with actionable insights, such as recommendations to adjust environmental conditions or lifestyle habits. The user interface is designed to be intuitive, making it easy for users to interpret the results and implement suggested changes.

[0123] Another aspect of the present disclosure relates to a non-transitory computer-readable storage medium storing instructions for generating personalized environmental sensitivity profde is disclosed. The instructions include executable code which, when executed by a processor, may cause the processor to receive a set of physiological parameters associated with at least one user; receive a set of environmental parameters associated with the at least one user, wherein the set of environmental parameters are sensed in close proximity of the user; analyse, using a trained model, the set of physiological parameters and the set of environmental parameters to identify one or more correlations between the set of physiological parameters and the set of environmental parameters, wherein the one or more correlations indicate change in the set of physiological parameters corresponding to change in the set of environmental parameters; determine a threshold limit for at least one of the set of environmental parameters that causes breach in a predefined threshold associated with the set of physiological parameters; and generate, at least one personalized sensitivity profile for the at least one user based on the determined threshold limit.

[0124] Terms and phrases used in this document, and variations thereof, unless otherwise expressly stated, should be construed as open-ended as opposed to limiting. As examples of the foregoing: the term “including” should be read as mean “including, without limitation” or the like; the term “example” is used to provide exemplary instances of the item in discussion, not an exhaustive or limiting list thereof; and adjectives such as “conventional,” “traditional,” “standard,” “known” and terms of similar meaning should not be construed as limiting the item described to a given time period or to an item available as of a given time, but instead should be read to encompass conventional, traditional, normal, or standard technologies that may be available or known now or at any time in the future. Likewise, a group of items linked with the conjunction “and” should not be read as requiring that each and every one of those items be present in the grouping, but rather should be read as “and / or” unless expressly stated otherwise. Similarly, a group of items linked with the conjunction “or” should not be read as requiring mutual exclusivity among that group, but rather should also be read as “and / or” unless expressly stated otherwise. Furthermore, although item, elements or components of the disclosure may be described or claimed in the singular, the plural is contemplated to be within the scope thereof unless limitation to the singular is explicitly stated. The presence of broadening words and phrases such as “one or more,” “at least,” “but not limited to” or other like phrases in some instances shall not be read to mean that the narrower case is intended or required in instances where such broadening phrases may be absent.

[0125] For the purposes of this specification and appended claims, unless otherwise indicated, all numbers expressing amounts, sizes, dimensions, proportions, shapes, formulations, parameters,percentages, quantities, characteristics, and other numerical values used in the specification and claims, are to be understood as being modified in all instances by the term “about” even though the term “about” may not expressly appear with the value, amount, or range. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are not and need not be exact, but may be approximate and / or larger or smaller as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art depending on the desired properties sought to be obtained by the subject matter of the present invention. For example, the term “about,” when referring to a value can be meant to encompass variations of, in some embodiments ± 100%, in some embodiments ± 50%, in some embodiments ± 20%, in some embodiments ± 10%, in some embodiments ± 5%, in some embodiments ± 1%, in some embodiments ± 0.5%, and in some embodiments ± 0.1% from the specified amount, as such variations are appropriate to perform the disclosed methods or employ the disclosed compositions.

[0126] Further, the term “about” when used in connection with one or more numbers or numerical ranges, should be understood to refer to all such numbers, including all numbers in a range and modifies that range by extending the boundaries above and below the numerical values set forth. The recitation of numerical ranges by endpoints includes all numbers, e.g., whole integers, including fractions thereof, subsumed within that range (for example, the recitation of 1 to 5 includes 1, 2, 3, 4, and 5, as well as fractions thereof, e.g., 1.5, 2.25, 3.75, 4.1, and the like) and any range within that range.

[0127] All publications, patent applications, patents, and other references mentioned in the specification are indicative of the level of those skilled in the art to which the presently disclosed subject matter pertains. All publications, patent applications, patents, and other references are herein incorporated by reference to the same extent as if each individual publication, patent application, patent, and other reference was specifically and individually indicated to be incorporated by reference. It will be understood that, although a number of patent applications, patents, and other references are referred to herein, such reference does not constitute an admission that any of these documents forms part of the common general knowledge in the art. Although the foregoing subject matter has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be understood by those skilled in the art that certain changes and modifications can be practiced within the scope of the appended claims.

Claims

We Claim:

1. A method for generating personalized environmental sensitivity profile, said method comprising: receiving, by at least one processor (102), a set of physiological parameters associated with at least one user; receiving, by the at least one processor (102), a set of environmental parameters associated with the at least one user, wherein the set of environmental parameters are sensed in close proximity of the user; analysing, by the at least one processor (102) using a trained model, the set of physiological parameters and the set of environmental parameters to identify one or more correlations between the set of physiological parameters and the set of environmental parameters, wherein the one or more correlations indicate change in the set of physiological parameters corresponding to change in the set of environmental parameters; determining, by the at least one processor (102), a threshold limit for at least one of the set of environmental parameters that causes breach in a predefined threshold associated with the set of physiological parameters; and generating, by the at least one processor (102), at least one personalized sensitivity profile for the at least one user based on the determined threshold limit.

2. The method as claimed in claim 1, wherein the method further comprises predicting, by the at least one processor (102), future outcomes of the set of physiological parameters based on the set of environmental parameters.

3. The method as claimed in claim 2, wherein the method further comprises triggering, by the at least one processor (102) using an actuation unit, based on the prediction, one or more corrective actions based on the predicted future outcomes.

4. The method as claimed in claim 1, wherein the predefined threshold is determined at least based on one of manual input from the at least one user, and the trained model.

5. The method as claimed in claim 1, wherein analysing the set of physiological parameters and the set of environmental parameters is performed for at least one of a short-term, a medium-term, and a long-term physiological term.

6. The method as claimed in claim 1, wherein the set of psychological parameters comprises at least one of sleep quality, heart rate (HR), resting heart rate (rHR), heart rate variability (HRV), physical movement, and skin temperature, associated with the at least one user.

7. The method as claimed in claim 1, wherein the set of environmental parameters comprises at least one of a room temperature, humidity, noise levels, CO2 level, lighting conditions, radon level, pollen level, formaldehyde (HCHO) level, and electromagnetic fields (EMF) level associated with the at least one user.

8. The method as claimed in claim 1, wherein the method further comprises rendering, by the at least one processor (102) using a display unit, a set of details associated with the generated at least one personalized sensitivity profile.

9. The method as claimed in claim 1, wherein the method further comprises assigning, by the at least one processor (102), a score to each of the set of psychological parameters, and the set of environmental parameters associated with the at least one user.

10. The method as claimed in claim 1, wherein the model is trained based on historical data associated with the set environmental parameters, and the set of physiological parameters.

11. A system (100) for generating personalized environmental sensitivity profile, the system comprising: at least one processor (102) configured to: receive a set of physiological parameters associated with at least one user;receive a set of environmental parameters associated with the at least one user, wherein the set of environmental parameters are sensed in close proximity of the user; analyse, using a trained model (206), the set of physiological parameters and the set of environmental parameters to identify one or more correlations between the set of physiological parameters and the set of environmental parameters, wherein the one or more correlations indicate change in the set of physiological parameters corresponding to change in the set of environmental parameters; determine a threshold limit for at least one of the set of environmental parameters that causes breach in a predefined threshold associated with the set of physiological parameters; and generate, at least one personalized sensitivity profile for the at least one user based on the determined threshold limit.

12. The system (100) as claimed in claim 11, wherein the at least one processor (102) is configured to predict future outcomes of the set of physiological parameters based on the set of environmental parameters.

13. The system (100) as claimed in claim 12, wherein an actuation unit communicatively coupled with the at least one processor (102), to trigger, based on the prediction, one or more corrective actions based on the predicted future outcomes.

14. The system (100) as claimed in claim 11, wherein the predefined threshold is determined at least based on one of manual input from the at least one user, and the trained model.

15. The system (100) as claimed in claim 11, wherein analysing the set of physiological parameters and the set of environmental parameters is performed for at least one of a shortterm, a medium -term, and a long-term physiological term.

16. The system (100) as claimed in claim 11, wherein the set of psychological parameters comprises at least one of sleep quality, heart rate (HR), resting heart rate (rHR), heart rate variability (HRV), physical movement, and skin temperature, associated with the at least one user.

17. The system (100) as claimed in claim 11, wherein the set of environmental parameters comprises at least one of a room temperature, humidity, noise levels, CO2 level, lighting conditions, radon level, pollen level, formaldehyde (HCHO) level, and electromagnetic fields (EMF) level associated with the at least one user.

18. The system (100) as claimed in claim 11, wherein a display unit communicatively coupled with the at least one processor (102), to render a set of details associated with the generated at least one personalized sensitivity profile.

19. The system (100) as claimed in claim 11, wherein the at least one processor (102) is configured to assign a score to each of the set of psychological parameters and, the set of environmental parameters.

20. The system (100) as claimed in claim 11, wherein the model (206) is trained based on historical data associated with the set environmental parameters, and the set of physiological parameters.

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