A Back Propagation Network (BPN) achieved the highest emotion recognition accuracy of 97.77%, surpassing LVQ (82.35%) and CART, with ECG correlation features outperforming traditional ones.
Does an embedded neural network architecture using HRV features accurately detect stress and emotions in healthy adults?
An embedded neural network architecture analyzing HRV from wearable sensors can accurately recognize emotions and detect stress, with the BPN algorithm achieving up to 97.77% accuracy.
Absolute Event Rate: 97.77% vs 82.35%
This study quantifies individual stress levels through real-time analysis of wearable sensor data. An embedded setup utilizes artificial neural networks to analyze R-R intervals and Heart Rate Variability (HRV). Emotion recognition of happiness, sadness, surprise, fear, and anger is explored using seven normalized HRV features. Statistical analysis and classification with a neural network model are performed on approximately 20,700 segments,with participants within the age ranged from 23 to 40, mixed gender, and normal health status, along with other pertinent demographics included. Findings show stress observation’s potential for mental well-being and early detection of stress-related disorders. Three classification algorithms (LVQ, BPN, CART) are evaluated, comparing ECG signal correlation features with traditional ones. BPN achieves the highest emotional recognition accuracy, surpassing LVQ by 5.9% – 8.5% and CART by 2% – 6.5%. Maximum accuracy is 82.35% for LVQ and 97.77% for BPN, but does not exceed 95%. Using only 72 feature sets yields the highest accuracy, surpassing S1 by 17.9% – 20.5% and combined S1/S2 by 11% – 12.7%. ECG signal correlation features outperform traditional features, potentially increasing emotion recognition accuracy by 25%. This study contributes to stress quantification and emotion recognition, promoting mental well-being and early stress disorder detection. The proposed embedded setup and analysis framework offer real-time monitoring and assessment of stress levels, enhancing health and wellness.
Murty et al. (Fri,) conducted a other in Stress and emotion recognition. Back Propagation Network (BPN) and ECG signal correlation features vs. LVQ, CART, and traditional features was evaluated on Emotion recognition accuracy. A Back Propagation Network (BPN) achieved the highest emotion recognition accuracy of 97.77%, surpassing LVQ (82.35%) and CART, with ECG correlation features outperforming traditional ones.
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