Personalized Stress Detection via Physiological Signal Analysis
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Solution Overview
Problem
Current methods for detecting stress in subjects are subjective, time-consuming, and lack objective, continuous, and quantitative measures, failing to account for individual physiological responses, which vary significantly between individuals.
Innovation Solution
An electronic system that uses physiological signals such as ECG, HR, HRV, BVP, SC, and ST to determine subject-specific stress responses by identifying correlating features and normalization parameters during stress tests, selecting personalized models from a pool trained on diverse features, and employing reinforcement learning for adaptive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If questionnaires are used to detect stress, then stress detection can be performed, but the method is subjective, time-consuming, and only provides spot-check basis data
Solution Approach 1:
The patent replaces the mechanical/questionnaire-based stress detection system with a physiological signal-based system. Instead of using subjective questionnaires that require manual completion, the system automatically collects and analyzes physiological signals (ECG, HRV, SC, ST) to objectively determine stress levels, thereby eliminating time consumption while improving measurement precision
Solution Approach 2:
The system enables continuous automatic stress monitoring without requiring subject participation or input. The physiological sensors continuously collect data and the processing unit automatically analyzes the signals to determine stress levels, providing continuous monitoring without time loss while maintaining high measurement precision
2Adaptability or versatility
If generic stress models are used, then stress detection can be performed, but individual physiological response variations between subjects are not accounted for
Solution Approach 1:
The patent segments the stress detection approach by creating subject-specific stress models tailored to each individual's physiological response patterns. Instead of using a single generic model, the system divides the detection process into individualized models that account for personal variations in physiological responses to stress
Solution Approach 2:
The system applies local quality by customizing the stress detection parameters and models for each subject based on their specific physiological characteristics. Each subject receives a personalized stress model that reflects their unique physiological response patterns, thereby improving measurement precision while enhancing adaptability
3Reliability
If continuous monitoring is implemented, then objective and quantitative stress data can be obtained, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional integrated system where a single processing unit handles multiple physiological signals (ECG, HRV, SC, ST) and performs various functions including data collection, analysis, stress level determination, and model updating. This universal approach enables continuous monitoring for reliable stress detection while managing system complexity through functional integration
Data Source
AI summary
Disclosed herein is a system for determining a subject's stress condition. The system includes a stress test unit configured for: receiving features defining the subject and physiological signals sensed from the subject when performing a relaxation and a stressful test task; extracting normalization parameters from the physiological signals; and identifying stress-responsive physiological features. The system also includes a storage unit configured for: storing a plurality of stress models; and storing the subject's features, normalization parameters, and the stress-responsive physiological features. The system also includes a stress detection unit configured for: selecting a stress model from the plurality of stress models based on the subject's features and the stress responsive physiological features; estimating a specific stress condition based on the stress model, stored subject's features, normalization parameters, and physiological signals that apply to the selected stress model; and providing a stress value representative of the subject's stress condition.