Key result
Integrated IoT and machine learning system classifies stress states with ~93% accuracy and rapid relaxation feedback.
Why the study?
Stress contributes significantly to mental and physical health disorders, making continuous monitoring and early detection essential to prevent long-term complications.
An integrated IoT system using EMG and temperature sensors with an SVM classifier can accurately and rapidly detect stress to provide real-time personalized relaxation feedback.
Hypothesis-generating for real-time EMG-IoT stress detection; leaves open cardiovascular outcome benefits pending clinical validation.
Stress is a major contributor to mental and physical health disorders in modern society. Continuous monitoring and early detection of stress are essential to prevent long-term complications. This paper presents a Real-Time Stress Detection and Relaxation Feedback System that integrates EMG sensors, temperature sensing, IoT, Machine Learning, and relaxation guidance. Physiological signals are acquired using EMG and temperature sensors and processed by an ESP32 microcontroller. The collected data is transmitted to a web-based platform and analyzed using machine learning models to classify stress levels. A web application developed using HTML, CSS, and JavaScript provides real-time visualization and personalized relaxation feedback. The proposed system offers an affordable, portable, and intelligent solution for real-time stress monitoring and management.
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Vishwakar et al. (2026) studied Stress. Real-Time Stress Detection and Relaxation Feedback System was evaluated on Stress state prediction accuracy. The integrated IoT and machine learning system utilizing EMG and temperature sensors classified stress states with 93.1% accuracy and delivered relaxation feedback with under 0.5 seconds latency.
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