An intelligent wearable embedded architecture for stress detection and psychological behavior monitoring using heart rate variability

Article type: Research Article

Authors: Chandra Murty, Patnala S.R.^a | Anuradha, Chinta^b | Appala Naidu, P.^c | Balaswamy, C.^d | Nagalingam, Rajeswaran^{e; *} | Jagatheesaperumal, Senthil Kumar^f | Ponnusamy, Muruganantham^g

Affiliations: [a] Department of CSE, Malla Reddy Engineering College (Autonomous), Maisammaguda, Secunderabad, India | [b] Department of CSE, V. R. Siddhartha Engineering College, Kanuru, Vijayawada, India | [c] Department of CSE, Raghu Engineering College (Autonomous) Visakhapatnam, India | [d] Department of ECE, Sheshadri Rao Gudlavalleru Engineering College, Gudlavalleru, India | [e] Department of Electrical and Electronics Engineering, Malla Reddy College of Engineering, Maisammaguda, Secunderabad, India | [f] Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India | [g] Deputy Registrar, Indian Institute of Information Technology Kalyani, West Bengal, India

Correspondence: [*] Corresponding author. Rajeswaran Nagalingam, Department of Electrical and Electronics Engineering, Malla Reddy College of Engineering, Maisammaguda, India. E-mail: rajeswarann@gmail.com; 0000-0000/09/$00.00.

Abstract: 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.

Keywords: Psychological behavior, stress monitoring, artificial neural networks, wearable embedded sensors, heart rate variability, ECG

DOI: 10.3233/JIFS-233791

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8203-8216, 2023