Description:FIELD OF THE INVENTION
This invention relates to AI-Driven Personalized Mental Health Monitoring Using Passive Data
BACKGROUND OF THE INVENTION
The self-reported data used by current mental health monitoring systems can be biased, sporadic, and subjective. Current AI-driven solutions are ineffective for ongoing monitoring because they necessitate active user engagement, such as surveys or explicit feedback. A non-intrusive, AI-powered system that protects user privacy and uses behavioral and physiological data to passively evaluate mental health conditions is required.
Current ways of monitoring mental health, like wearables that track physiological signs of stress (Apple Watch, Fitbit Sense) or chatbots for AI-powered apps (Replika, Woebot, Wysa), depend largely on user-initiated participation. Though lacking multimodal data synthesis and overall real-time analysis, some AI-driven systems, such as MindStrong Health and Ellipsis Health, attempt passive monitoring through voice tone analysis or mobile phone use patterns. By failing to capitalize on a combination of behavior and physiological indicators towards comprehensive appreciation of mental well-being, current commercial practice is either engagement-based or restricted to certain forms of information. Your AI-powered solution is unique in that it provides actually passive, multimodal tracking of mental well-being by using AI to track movement, voice tone, phone behavior, sleep activity, and typing activity—all without invading privacy through federated learning and on-device computation.
All the latest mental health fixes just don't quite get it right. Chatbots such as Woebot and Wysa need users to be actively involved, which isn't always convenient—particularly when a person is in distress. Wearables such as Fitbit and Apple Watch can monitor stress via heart rate but overlook important behavioral indicators such as shifts in typing speed, voice tone, or phone behavior. Certain AI-powered apps, such as MindStrong and Ellipsis Health, attempt to monitor mental health passively but only examine a single source of data—the omission of the larger picture. Additionally, a majority of such systems are based on cloud-based processing, creating privacy issues. What is needed is a fluid, passive, and personalized AI system that will learn from numerous behavioral and physiological cues—without the need for users to change a thing.
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.
This mental health monitoring system via artificial intelligence acquires and analyzes various behavioral and physiological cues passively without active user engagement. It acquires behavioral data such as typing pace, phone use, and screen usage and physiological signals such as tone of voice, facial reactions, and heart rate variability. Along with these, environmental data like sleep patterns and activity level are used to make an estimate as a whole. The system employs AI-driven multimodal data fusion to identify behavioral abnormalities of mental health conditions, with over time personalized learning for enhanced accuracy. In contrast to current solutions based on cloud processing, the system is privacy-focused through on-device AI and federated learning, where sensitive information is kept safe. The AI continuously tracks mood changes, anxiety, or depression patterns, offering real-time feedback and personalized well-being advice and optional integration of mental health experts. It is indeed a passive, multimodal, adaptive, and privacy-based method, and it circumvents the shortcomings of existing mental health surveillance systems.
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: SYSTEM ARCHITECTURE
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.
This mental health monitoring system via artificial intelligence acquires and analyzes various behavioral and physiological cues passively without active user engagement. It acquires behavioral data such as typing pace, phone use, and screen usage and physiological signals such as tone of voice, facial reactions, and heart rate variability. Along with these, environmental data like sleep patterns and activity level are used to make an estimate as a whole. The system employs AI-driven multimodal data fusion to identify behavioral abnormalities of mental health conditions, with over time personalized learning for enhanced accuracy. In contrast to current solutions based on cloud processing, the system is privacy-focused through on-device AI and federated learning, where sensitive information is kept safe. The AI continuously tracks mood changes, anxiety, or depression patterns, offering real-time feedback and personalized well-being advice and optional integration of mental health experts. It is indeed a passive, multimodal, adaptive, and privacy-based method, and it circumvents the shortcomings of existing mental health surveillance systems.
The proposed AI-based mental health monitoring system provides a completely passive, multimodal, and adaptive solution addressing some of the most critical challenges with available solutions. In contrast to chatbot-based applications or questionnaires involving self-reported answers that demand user engagement, this system passively observes behavioral, physiological, and environmental data like typing speed, speech volume, facial expression, phone usage, movement, and sleeping patterns without any manual input. As compared to existing solutions with single-source data, multimodal fusion with AI in this scenario is more accurate in assessing mental health as a whole. The system also gets smarter over time depending on the behavior of the individual and offers precise and personalized monitoring compared to existing solutions relying on one-size-fits-all AI. One of the key benefits is privacy, as it makes use of on-device AI and federated learning to minimize the risk of data exposure to cloud servers. Another benefit, aside from detection, is that such a system offers real-time insights and personalized recommendations to support proactive mental well-being. It therefore becomes a more precise, friendly, and privacy-preserving alternative compared to existing commercial products.
NOVELTY:
This AI-based system provides genuinely passive, multimodal, and adaptive mental health monitoring, in contrast to current solutions that involve active input or single-source data. It specifically combines behavioral, physiological, and environmental cues (e.g., typing habits, speech tone, facial expressions, phone usage) for comprehensive assessment. The AI learns over time to individual behavior, providing greater accuracy than one-size-fits-all models. In contrast to cloud solutions, it also values privacy through on-device AI and federated learning. It also actively captures mood changes and gives real-time suggestions and is therefore an effortless, secure, and smart way of monitoring mental health.
 
, Claims:1. An AI based mental health monitoring system, comprising: machine learning model, a personalization module, a processing unit and sensors.
2. The system as claimed as claim 1, wherein the system providing genuinely passive, multimodal, and adaptive mental health monitoring, in contrast to current solutions that involve active input or single-source data.
3. The system as claimed as claim 1, wherein the system passively observes behavioural, physiological, and environmental data like typing speed, speech volume, facial expression, phone usage, movement, and sleeping patterns without any manual input.
4. The system as claimed as claim 1, wherein the personalization module that continuously updates the artificial intelligence model based on the individual user’s behavioral patterns over time to improve prediction accuracy.
5. The system as claimed as claim 1, wherein the processing unit configured to perform multimodal data fusion on the acquired data using an artificial intelligence model to detect anomalies associated with mental health conditions.
6. The system as claimed as claim 1, wherein the system offers real-time insights and personalized recommendations to support proactive mental well-being.