悲伤
计算机科学
心率变异性
愤怒
人工智能
可穿戴计算机
相关性
幸福
特征(语言学)
人工神经网络
模式识别(心理学)
机器学习
语音识别
心理学
心率
医学
数学
临床心理学
放射科
哲学
嵌入式系统
语言学
几何学
血压
社会心理学
作者
Patnala S.R. Chandra Murty,Chinta Anuradha,P. Appala Naidu,C. Balaswamy,Rajeswaran Nagalingam,Senthil Kumar Jagatheesaperumal,Muruganantham Ponnusamy
摘要
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.
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