Description:FIELD OF THE INVENTION
This invention relates to AI-Powered Wearable System for Predicting Stress, Anxiety, and Depression Using Physiological and Behavioural Data
BACKGROUND OF THE INVENTION
Mental health disorders, including stress, anxiety, and depression, affect millions globally, often going undetected due to the limitations of traditional diagnostic methods. Existing mental health assessment techniques rely heavily on self-reported questionnaires and clinical evaluations, which are subjective, time-consuming, and inaccessible to many individuals. Furthermore, there is a lack of real-time, automated, and continuous monitoring systems capable of detecting early signs of mental health deterioration. Current wearable devices provide basic health metrics such as heart rate and sleep tracking, but they do not leverage Artificial Intelligence (AI) and Machine Learning (ML) to analyse, predict, and intervene in mental health conditions effectively.
KR20240133794A Embodiments include therapeutic small molecules that are used to treat addiction and neurological disorders by stimulating neuroplasticity. The therapeutic small molecules can increase neuroplasticity and improve neuronal function by affecting upstream regulators of FOS, JUN, BDNF, CDC42, and CCL2. The therapeutic small molecules can also bind to amyloid beta (a4) and help reduce amyloid plaques.
RESEARCH GAP: An AI-powered mental health monitoring system could be built that could offers non-invasive, real-time tracking of stress, anxiety, and depression, enabling early detection and prevention.
The system could utilize AI-driven biosensors, behavioural analysis, and NLP to provide personalized interventions without the side effects of pharmaceutical treatments.
Real-time alerts and mental health recommendations, such as guided meditation, CBT exercises, and therapist referrals, could be sent based on detected risk levels.
The system could utilize continuous monitoring and adaptive learning, to offer improved accuracy over time through AI model updates.
All collected data could be securely stored in the cloud with end-to-end encryption and GDPR/HIPAA compliance, ensuring user privacy and data protection.
US20210378581A1 A neural analysis and treatment system includes a computing device with a memory for storing an application that is executable on a processor to receive amplitude-integrated electroencephalography (aEEG) and range-EEG (rEEG) measurements associated with a patient. The systems determine a spectral edge frequency (SEF) measurement from the received EEG measurements, and determine one or more neural characteristics of the patient according to the determined SEF, aEEG, and rEEG measurements. These neural characteristics may then be used to identify and implement an appropriate therapeutic treatment
RESEARCH GAP: The AI-powered mental health monitoring system could be used to offer a non-invasive, real-time solution compared to the existing EEG-based neural analysis system, which requires specialized equipment and clinical settings.
Continuously tracks physiological and behavioural patterns could be achieved using wearable biosensors for early detection and prevention of stress, anxiety, and depression.
AI and machine learning algorithms could be utilized to analyse HRV, GSR, sleep patterns, and NLP-based speech analysis, providing personalized mental health interventions without the need for complex EEG data interpretation.
Real-time alerts and therapy recommendations could be offered to make it more accessible and scalable for both individual users and healthcare providers.
All collected data could be securely stored in the cloud with end-to-end encryption and GDPR/HIPAA compliance, ensuring user privacy and data protection.
US20070270665A1 The invention provides a physiological function monitoring system for monitoring a first physiological function of a user. The physiological function monitoring system includes a first detecting device, N second detecting devices, a signal processing device, and a communicating device, N is a nature number. Furthermore, the physiological function monitoring system is applied in remote monitoring. In addition, the monitoring system is capable of detecting N second physiological functions of the user by the N detecting devices, to help making correct judgment on the first physiological function of the user.
RESEARCH GAP: The AI-powered mental health monitoring system could be used to offer a more advanced and specialized solution compared to the existing physiological function monitoring system, which focuses on basic remote monitoring of physiological functions.
AI-driven biosensors and NLP models could be utilized to specifically analyse stress, anxiety, and depression indicators, providing mental health insights rather than just physical metrics.
Multi-modal data sources (biosensors, behavioral analysis, and NLP) cpoul dbe integrated for holistic mental health assessment, making it more comprehensive and accurate.
US20230395235A1 A computational personalized cognitive therapeutic system for treating patients with Mild Cognitive Impairment, Alzheimer's Disease, dementia and related conditions is described. The system includes a patient clinical database, a data aggregation layer and data pre-processor module, a digital cognitive therapy delivery module, a cognitive analytics engine, and a personalised cognitive platform configured to personalize a personalised cognitive digital therapy model. The personalised cognitive digital therapy model defines specific digital treatments to be delivered to the patient using the digital cognitive therapy delivery module each with a different mechanism of action. A range of digital cognitive biomarkers are collected along with behavioural and physiological biomarkers from wearable and medical devices which are processed by the cognitive analytics engine and uses AI/ML methods which are configured to estimate metrics and generate alerts. The metrics are used to assess treatment progress and then personalize the personalised cognitive digital therapy model for the patient including adjustment of digital therapies and medication. Alerts may be generated if adverse side effects are observed. This process is iteratively repeated to provide improved treatment over time.
RESEARCH GAP: A real-time, non-invasive mental health monitoring system for predicting stress, anxiety, and depression, making it suitable for both clinical and everyday use.
The system could use multi-modal biosensor data, behavioural analysis, and NLP-based speech sentiment detection to detect early warning signs of mental health deterioration, while the existing system primarily focuses on cognitive therapy adjustments.
The system could also offer instant alerts and personalized interventions, such as guided meditation, CBT exercises, and therapist referrals, to provide preventive care rather than only therapeutic adjustments.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to AI-Powered Wearable System for Predicting Stress, Anxiety, and Depression Using Physiological and Behavioural Data.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
In this invention, we've suggested a system which uses an AI-powered wearable system designed for real-time prediction and monitoring of stress, anxiety, and depression using physiological and behavioural data. The system consists of three key components: a wearable sensor device (103), an AI-driven computing unit (104), and a user interface (mobile app and cloud dashboard). The wearable device (103) continuously collects biometric signals such as heart rate variability (HRV), electrodermal activity (EDA/GSR), respiration rate, sleep patterns, and oxygen saturation (SpO2). These signals, combined with behavioural inputs from social media sentiment, voice tone, and activity levels, are processed using machine learning (ML) and deep learning algorithms to detect mental health conditions. The system provides real-time alerts and personalized interventions, making it a scalable, non-invasive, and proactive solution for mental health monitoring.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1. The hardware setup of the invention
Figure 2. Functional workflow of the system
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, 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 should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used 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.
In this invention, we've suggested a system which uses an AI-powered wearable system designed for real-time prediction and monitoring of stress, anxiety, and depression using physiological and behavioural data. The system consists of three key components: a wearable sensor device (103), an AI-driven computing unit (104), and a user interface (mobile app and cloud dashboard). The wearable device (103) continuously collects biometric signals such as heart rate variability (HRV), electrodermal activity (EDA/GSR), respiration rate, sleep patterns, and oxygen saturation (SpO2). These signals, combined with behavioural inputs from social media sentiment, voice tone, and activity levels, are processed using machine learning (ML) and deep learning algorithms to detect mental health conditions. The system provides real-time alerts and personalized interventions, making it a scalable, non-invasive, and proactive solution for mental health monitoring.
Figure 1 illustrates the hardware setup of the invention. The wearable device is designed to be lightweight, non-invasive, and capable of real-time physiological monitoring. It includes multiple biosensors that track vital parameters linked to mental health disorders. The HRV sensor (101) measures the time variation between heartbeats, which is a key indicator of autonomic nervous system activity and stress response. The electrodermal activity (EDA/GSR) sensor (102) monitors skin conductance to detect emotional distress. The respiration sensor (105) tracks breathing rate and depth, identifying irregularities associated with anxiety and panic attacks. Additionally, the sleep monitoring module (106) analyzes sleep cycles, including deep sleep and disturbances, which are crucial indicators of mental well-being. The oxygen saturation (SpO2) sensor (107) detects breathing irregularities linked to anxiety, while the accelerometer and motion sensor (108) track daily activity levels to assess behavioral changes. The collected data is transmitted in real-time to the AI-powered computing unit (104) for further analysis.
The AI-driven computing unit serves as the central processing system, leveraging advanced artificial intelligence algorithms to analyse biometric and behavioural data. The first stage involves data preprocessing and feature extraction, where raw sensor data is filtered, normalized, and structured to ensure accurate predictions. AI models process time-series data, sentiment analysis, and behavioural trends to identify risk factors for stress, anxiety, and depression. The computing unit employs multiple machine learning and deep learning techniques to enhance prediction accuracy. Support Vector Machines (SVMs) classify mental health risk levels, while Random Forest and Decision Tree models detect patterns in sleep disturbances and physical activity. Convolutional Neural Networks (CNNs) are used for facial emotion recognition, and Long Short-Term Memory (LSTM) networks analyse time-dependent variations in HRV and respiration. Additionally, Natural Language Processing (NLP) models such as BERT and GPT evaluate speech patterns, social media interactions, and text-based inputs to detect emotional distress. The AI system continuously learns from user data, allowing for adaptive and personalized mental health monitoring.
The user interface is designed to provide real-time mental health insights, alerts, and personalized intervention strategies. The mobile application dashboard displays stress, anxiety, and depression scores derived from AI-driven analysis. Users receive instant notifications when their mental health conditions exceed predefined risk thresholds. The app provides recommendations based on AI predictions, including guided breathing exercises, meditation techniques, cognitive behavioural therapy (CBT) exercises, and professional therapist referrals. It also allows users to track long-term mental health trends through data visualization tools, enabling self-assessment and progress monitoring. Additionally, the system integrates with telehealth platforms, allowing users to schedule virtual therapy sessions if required. The cloud integration ensures secure data storage and processing, using end-to-end encryption and compliance with global health regulations (GDPR, HIPAA) to protect user privacy. The AI model is continuously updated through federated learning, allowing it to learn from multiple devices without compromising sensitive user data.
Figure 2 illustrates the system workflow. The system follows a structured workflow for data collection, processing, risk classification, and intervention. In the first step, the wearable device collects biosensor data, including HRV, GSR, sleep patterns, and activity levels. Simultaneously, the mobile app records self-reported symptoms and behavioural inputs, and the NLP module analyses text and voice inputs for emotional cues. In the second step, data is cleaned and processed using AI models that detect deviations from normal mental health trends. In the third step, the AI classifies the user’s mental health condition into low, moderate, or high risk categories. If the detected risk surpasses a predefined threshold, the system sends real-time alerts and intervention recommendations. In the fourth step, the app provides personalized suggestions, including lifestyle modifications, relaxation techniques, and therapy referrals. If mental health deterioration is detected over time, the system recommends professional intervention via telemedicine.
ADVANTAGES OF THE INVENTION:
1. Unlike traditional diagnosis methods, the system provides continuous tracking of stress, anxiety, and depression.
2. Predicts mental health conditions before symptoms escalate, enabling timely intervention.
3. Combines biosensors, behavioral analysis, and NLP to provide a holistic mental health assessment.
4. Offers customized mental health strategies, including meditation, therapy connections, and lifestyle changes.
5. Displays real-time mental health insights, trends, and interventions for users and healthcare professionals.
6. Implements end-to-end encryption, anonymization, and strict regulatory compliance (GDPR, HIPAA) to protect sensitive user data.
7. Can be integrated with telemedicine platforms to facilitate remote mental health assessments.
8. Unlike traditional diagnostic methods, which require in-person evaluations, the wearable device enables uninterrupted monitoring without discomfort.
9. Uses SHAP and LIME algorithms to provide interpretable AI-based mental health predictions for healthcare professionals.
, Claims:1. An AI-Powered Wearable System for Predicting Mental health conditions, comprising: heart rate variability (HRV) sensor, electrodermal activity (EDA) sensor, respiration sensor, sleep monitoring module, oxygen saturation (SpO2) sensor, motion sensor, AI-driven computing unit and user interface.
2. The system as claimed in claim 1, wherein the system continuously updates the AI models using federated learning, enabling adaptive personalization without transmitting raw user data.
3. The system as claimed in claim 1, wherein the system provides real-time alerts and personalized interventions, making it a scalable, non-invasive, and proactive solution for mental health monitoring.
4. The system as claimed in claim 1, wherein the AI-driven computing unit serves as the central processing system, leveraging advanced artificial intelligence algorithms to analyse biometric and behavioural data.
5. The system as claimed in claim 1, wherein the computing unit employs multiple machine learning and deep learning techniques to enhance prediction accuracy. Support Vector Machines (SVMs) classify mental health risk levels, while Random Forest and Decision Tree models detect patterns in sleep disturbances and physical activity.
6. The system as claimed in claim 1, wherein the system integrates with telehealth platforms, allowing users to schedule virtual therapy sessions if required.