Deep Neural Network for Real-Time Stress Analysis
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Solution Overview
Problem
Conventional stress analysis methods struggle to analyze stress in real-time due to the need for extensive preprocessing and noise elimination from electrocardiogram signals, making it difficult to accurately detect R peaks and calculate heart rate variability, which is essential for monitoring stress levels effectively.
Innovation Solution
A deep neural network algorithm, specifically a convolutional neural network, is used to process raw bio-signals such as electrocardiogram, electroencephalogram, and skin conductivity data without separate preprocessing, allowing for real-time stress analysis by calculating the probability of stress levels and updating the model based on user feedback to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional stress analysis methods use heart rate variability extraction from electrocardiogram signals, then stress monitoring can be performed, but real-time analysis is difficult due to required preprocessing and noise elimination steps
Solution Approach 1:
The patent merges the preprocessing steps and feature extraction into a single integrated deep neural network model that processes raw electrocardiogram signals directly, eliminating the need for separate preprocessing stages and achieving real-time stress analysis
Solution Approach 2:
The patent replaces the conventional mechanical signal processing pipeline (filtering, R peak detection, HRV calculation) with a deep neural network algorithm that learns direct stress indicators from raw signals, enabling faster real-time analysis
2Measurement precision
If conventional methods extract R peaks from electrocardiogram signals, then heart rate variability can be calculated, but the analysis result depends largely on R peak detection performance which is vulnerable to external noise
Solution Approach 1:
The patent converts the vulnerability to noise into a benefit by training the deep neural network to recognize stress patterns despite the presence of noise and interference, allowing accurate stress analysis even in noisy everyday life conditions
Solution Approach 2:
The patent changes the approach from extracting specific R peak intervals to analyzing overall signal patterns through deep neural network, transforming the measurement from being sensitive to noise to being robust against noise by learning invariant stress features
3Measurement precision
If conventional stress analysis requires at least 5 minutes of electrocardiogram data, then meaningful analysis results can be obtained, but real-time stress analysis in everyday life becomes practically difficult
Solution Approach 1:
The patent implements dynamic analysis by continuously processing electrocardiogram signals in real-time through the deep neural network, allowing stress monitoring to adapt to changing conditions and provide immediate feedback without requiring fixed 5-minute intervals
Solution Approach 2:
The patent enables continuous stress monitoring by processing electrocardiogram signals in real-time as they are acquired, maintaining continuous analysis rather than periodic 5-minute sampling, thus providing uninterrupted stress monitoring in everyday life
Data Source
AI summary
The present invention relates to a stress analysis method including: acquiring bio-signals from a test subject; calculating a probability of each of a plurality of stress level values by processing the bio-signals using a deep neural network algorithm; estimating a stress level value with the maximum probability of the plurality of stress level values as a stress level value of the test subject; determining usefulness of the estimated stress level value; and outputting the estimated stress level value determined to be useful through the determination of usefulness, as the final stress level.