ADVANCE AI-POWERED WEARABLE SYSTEM FOR CONTINUOUS DETECTION AND MONITORING OF ATTENTION DEFICIT HYPERACTIVITY DISORDER (ADHD) SYMPTOMS USING MULTI-SENSOR DATA INTEGRATION

Abstract

This invention discloses a novel AI-powered wearable system for continuous monitoring of ADHD symptoms. Utilizing multi-sensor data and advanced machine learning, the system provides real-time detection, personalized insights, and interventions, improving ADHD management and enhancing quality of life.

Specification

Description:FIELD OF THE INVENTION
This invention relates to the field of medical technology and wearable computing, specifically concerning the diagnosis, monitoring, and management of Attention Deficit Hyperactivity Disorder (ADHD). It leverages artificial intelligence, machine learning, and multi-sensor data integration to provide a novel, continuous, and personalized approach to ADHD management.
BACKGROUND OF THE INVENTION
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects millions worldwide, posing significant challenges for individuals, families, and healthcare systems. Current diagnostic and management approaches for ADHD rely heavily on subjective assessments, such as questionnaires and behavioral observations, often conducted in clinical settings. These methods have significant limitations. They are time-consuming, expensive, and prone to biases related to the subjective nature of the assessments, potentially leading to misdiagnosis or inadequate treatment plans. Moreover, these assessments provide only a snapshot of an individual's behavior at a specific point in time, failing to capture the dynamic and fluctuating nature of ADHD symptoms, which vary across different contexts and throughout the day. Traditional methods often fail to consider the complex interplay of neurological, physiological, and behavioral factors that contribute to ADHD. The lack of continuous, real-time monitoring in real-world settings makes it challenging to understand the triggers and patterns of ADHD symptoms, hindering the development of effective personalized interventions. Existing technological interventions for ADHD have also fallen short. While some digital tools and apps aim to support ADHD management, they typically rely on self-reporting or infrequent assessments, lacking the continuous monitoring and real-time feedback needed for effective management. Many lack integration with various data sources and fail to provide personalized insights based on comprehensive data analysis. This invention aims to address the limitations of current approaches by providing an AI-powered wearable system capable of continuously monitoring ADHD symptoms in real-world settings, using multi-sensor data integration and advanced machine learning to enhance accuracy, provide personalized insights, and facilitate effective treatment strategies.
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 invention discloses a novel AI-powered wearable system for continuous detection and monitoring of ADHD symptoms. The system uses a combination of EEG, accelerometer, heart rate, and electrodermal activity sensors to collect comprehensive physiological and behavioral data. Advanced machine learning algorithms (convolutional and recurrent neural networks) process this data in real-time, identifying and classifying ADHD symptoms based on pre-defined patterns. The system generates personalized reports, providing valuable insights into symptom patterns and triggers. It also delivers real-time feedback and suggests personalized interventions (e.g., deep breathing exercises, attentional control prompts) via a mobile application. The system's design prioritizes user comfort and ease of use while enabling continuous data collection and feedback, promoting effective management of ADHD and enhancing overall quality of life.
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: PHASES OF THE AI-POWERED WEARABLE SYSTEM FOR ADHD DETECTION AND MANAGEMENT
FIGURE 2: ARCHITECTURAL DIAGRAM OF THE AI-POWERED WEARABLE SYSTEM FOR CONTINUOUS ADHD MONITORING
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.
The AI-powered wearable system comprises several key components: a wearable device, a data acquisition and processing module, a machine learning algorithm, and a mobile application. The wearable device integrates multiple sensors including EEG, accelerometer, heart rate, and electrodermal activity (EDA) sensors. These sensors collect continuous real-time data related to brain activity, movement, physiological arousal, and stress levels. The data acquisition and processing module collects, filters, and preprocesses the raw data from the sensors, preparing it for analysis by the machine learning algorithm. This module also handles data transmission to the cloud via Bluetooth and ensures data security and privacy. The machine learning algorithm, specifically employing convolutional and recurrent neural networks, analyzes the processed data in real-time to identify and classify various ADHD symptoms (inattention, hyperactivity, impulsivity) according to predefined patterns and thresholds. The algorithm learns and adapts continuously, enhancing its accuracy over time through a feedback mechanism that incorporates real-world data and user input. The mobile application provides a user-friendly interface for real-time monitoring of symptoms, generating personalized reports and insights, and delivering tailored feedback and interventions. The application allows users to review their data, receive suggestions for managing symptoms, and share their data with healthcare professionals, fostering collaborative management and informed decision-making. The system incorporates a feedback loop mechanism to continuously refine its algorithms and adapt to individual needs, ensuring its effectiveness and providing users with a valuable tool for managing their ADHD symptoms.
, Claims:1. A wearable system for monitoring ADHD symptoms, as claimed in Claim 1, comprising a wearable device with integrated EEG, accelerometer, heart rate, and electrodermal activity sensors.
2. The system, as claimed in Claim 1, further comprising a data acquisition and processing module for collecting, preprocessing, and transmitting sensor data.
3. The system, as claimed in Claim 2, further comprising a machine learning algorithm for real-time analysis of sensor data to identify and classify ADHD symptoms.
4. The system, as claimed in Claim 3, wherein said machine learning algorithm utilizes convolutional and recurrent neural networks.
5. The system, as claimed in Claim 4, further comprising a mobile application for providing real-time feedback, personalized insights, and interventions.
6. The system, as claimed in Claim 5, wherein said system incorporates a feedback mechanism to continuously learn and adapt based on user data and environmental factors.
7. The system, as claimed in Claim 6, wherein said system generates personalized reports that provide insights into symptom patterns and triggers.

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