DEVICE FOR PREDICTING CHILD MENTAL STRESS USING AI WITH MULTI-SENSOR INTEGRATION

Abstract

The present invention relates to a device for predicting mental stress in children using artificial intelligence (AI) and multi-sensor integration. The device incorporates body-worn sensors, smart clothing, a facial recognition system, and an AI-enabled processing unit for real-time monitoring and prediction of stress levels. Physiological data such as heart rate, skin conductance, and body temperature are collected via sensors, while facial recognition detects emotional states. The AI-based processing unit fuses these data streams to accurately predict mental stress. A mobile application provides real-time feedback, alerts, and recommendations to caregivers, allowing for timely intervention and personalized stress management. The invention aims to enhance child well-being by providing a comprehensive and adaptive solution for stress detection and intervention.

Specification

Description:[013] The following sections of this article will provide various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention. Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[014] Referring now to the drawings, these are illustrated in FIG. 1, the body-worn sensors, including a heart rate monitor, skin conductance sensor, and body temperature sensor, are placed on different parts of the child's body. The heart rate monitor is typically worn on the wrist or chest, the skin conductance sensor on the palm or fingers, and the body temperature sensor on the arm or chest. These sensors are in communication with the AI processing unit via a wireless protocol, such as Bluetooth or Wi-Fi, allowing for continuous real-time data collection. The sensors are designed to be lightweight, non-intrusive, and comfortable for children to wear for extended periods.
[015] In accordance with another embodiment of the present invention, the processing unit runs on a portable microcontroller system that includes an AI model trained using physiological data collected from children under varying stress conditions. The AI uses machine learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), or a combination of both, to accurately determine the level of stress based on the physiological data collected. The processing unit is equipped with sufficient computational power to perform real-time analysis and is designed to be energy-efficient for prolonged use.
[016] In accordance with another embodiment of the present invention, the real-time monitoring system continuously collects data from the body-worn sensors and smart clothing. The monitoring system includes data preprocessing steps such as noise filtering, normalization, and feature extraction to ensure the data is of high quality before analysis. The system compares the collected data against predefined baseline values, which are specific to each child, to detect deviations that may indicate mental stress. The baselines are established through an initial calibration phase, during which the child's physiological parameters are measured under non-stressful conditions.
[017] In accordance with another embodiment of the present invention, The facial recognition camera is integrated into the system and placed in the child's vicinity, such as on a desk or mounted on a wall. The camera captures images of the child's face at regular intervals or when triggered by specific physiological changes. The images are sent to the AI-enabled processing unit, where an emotion detection algorithm processes these images to determine the emotional state of the child. The facial recognition system utilizes deep learning algorithms, such as convolutional neural networks (CNNs), to analyze facial expressions and identify emotions such as anxiety, sadness, frustration, happiness, or calmness. The emotion detection module is designed to work under various lighting conditions and angles to ensure accurate emotion recognition.
[018] Smart clothing integrates textile-based sensors designed to be comfortable for children. These sensors are seamlessly embedded into everyday clothing items such as shirts, vests, or socks. The textile-based sensors measure physiological data such as heart rate variability, skin conductance, and respiratory rate. The smart clothing is linked to the portable AI processor via Bluetooth or other wireless communication protocols. The clothing is made of breathable, hypoallergenic materials to ensure comfort and is washable without damaging the embedded sensors. The smart clothing is designed to provide continuous monitoring without causing discomfort, making it suitable for long-term use.
[019] The AI interface is responsible for fusing multiple sensor data streams, including physiological, facial, and textile-based sensor data. The data fusion process allows the system to consider the interactions between different physiological parameters, thereby providing a more accurate prediction of mental stress levels. For example, an increase in heart rate combined with a negative facial expression may indicate a higher likelihood of stress. The fused data is analyzed using a predictive model, which determines whether the child is experiencing mental stress based on established thresholds and correlations. The AI system is designed to learn and adapt over time, refining its predictive capabilities based on the child's individual responses to stress.
[020] The device can be linked to a mobile application that displays the predicted stress levels and provides caregivers with notifications and recommendations. The mobile application provides real-time visualizations of the child's physiological parameters and emotional state. Alerts are generated when stress levels exceed predefined limits, enabling prompt intervention. The application also includes a history log that allows caregivers to track stress patterns over time and identify potential triggers. Recommendations for stress management techniques, such as breathing exercises or calming activities, are also provided to help caregivers support the child effectively.
[021] The present invention provides a comprehensive solution for predicting mental stress in children using advanced AI technologies and multi-sensor integration. By combining body-worn sensors, facial recognition, smart clothing, and an AI-enabled processing unit, the device offers a reliable and accurate way to monitor and predict stress levels in real time. The integration of multiple physiological parameters, along with emotion detection, ensures a holistic approach to stress assessment, allowing caregivers to take timely action and provide appropriate interventions. The adaptability of the AI system, which learns from the child's individual responses, further enhances the accuracy and reliability of the stress prediction. This invention has significant potential to improve child mental health outcomes by facilitating early detection and proactive management of stress, ultimately promoting better emotional well-being and quality of life for children.
[022] The benefits and advantages that the present invention may offer have been discussed above with reference to particular embodiments. These benefits and advantages are not to be interpreted as critical, necessary, or essential features of any or all of the embodiments, nor are they to be read as any elements or constraints that might contribute to their occurring or becoming more evident.
[023] Although specific embodiments have been used to describe the current invention, it should be recognized that these embodiments are merely illustrative and that the invention is not limited to them. The aforementioned embodiments are open to numerous alterations, additions, and improvements. These adaptations, changes, additions, and enhancements are considered to be within the purview of the invention. , Claims:1. A device for predicting child mental stress, comprising:
a plurality of body-worn sensors including a heart rate monitor, skin conductance sensor, and a body temperature sensor;
an AI-enabled processing unit configured to receive data from said body-worn sensors and analyze said data to predict mental stress levels;
a real-time monitoring system that continuously collects physiological data and processes it using machine learning interface;
a facial recognition system configured to capture images of the child and detect emotions using an AI-based interface;
Smart clothing integrated with textile-based sensors to measure physiological parameters, said sensors in communication with said AI-enabled processing unit;
an AI interface for fusing multiple sensor data streams for enhanced prediction accuracy.
2. The device as claimed in claim 1, wherein the AI-enabled processing unit utilizes machine learning interface including convolutional neural networks (CNNs), recurrent neural networks (RNNs), or a combination thereof to predict mental stress levels.
3. The device as claimed in claim 1, wherein the facial recognition system includes a camera positioned to capture the child's facial expressions, and an emotion detection module to process the captured images using deep learning algorithms.
4. The device as claimed in claim 1, wherein the smart clothing comprises textile-based sensors configured to communicate with the AI-enabled processing unit via a wireless communication protocol, said smart clothing being made of breathable, hypoallergenic materials suitable for long-term use.
5. The device as claimed in claim 1, wherein the AI interface fuses physiological data, emotion data, and textile-based sensor data to improve the accuracy of the stress prediction model, said AI interface being capable of learning and adapting over time based on the child's individual responses to stress.
6. The device as claimed in claim 1, further comprising a user interface for providing alerts, visualizations, and recommendations to caregivers based on the predicted stress levels, said user interface being accessible via a mobile application.
7. The device as claimed in claim 1, wherein the real-time monitoring system includes data preprocessing steps, such as noise filtering, normalization, and feature extraction, to ensure high-quality data for analysis.
8. The device as claimed in claim 1, wherein the AI-enabled processing unit is configured to compare collected physiological data against predefined baseline values specific to each child, said baseline values being established during an initial calibration phase under non-stressful conditions.
9. The device as claimed in claim 1, wherein the smart clothing includes textile-based sensors capable of measuring heart rate variability, skin conductance, and respiratory rate, said sensors being embedded into everyday clothing items such as shirts, vests, or socks.
10. The device as claimed in claim 1, further comprising a mobile application for caregivers, wherein the application provides real-time visualizations, history logs, alerts, and recommendations for stress management techniques, such as breathing exercises or calming activities, based on the predicted stress levels of the child.

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