April 23, 2026Open Access

Real time stress detection and relaxation feedback using EMG sensor and ML

Q: What is the clinical evidence from this study?

Study design: Other. Population: Stress. Intervention: Real-Time Stress Detection and Relaxation Feedback System. Primary outcome: Stress state prediction accuracy.

Key Result

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.

Key Points

To develop a system for real-time stress detection and relaxation feedback using EMG sensors and machine learning.
Use EMG and temperature sensors to acquire physiological signals.
Process data with an ESP32 microcontroller and transmit it to a web platform.
Analyze data with machine learning models for stress classification.
The system accurately classifies stress levels based on physiological signals.
Users receive real-time visualization of their stress status and relaxation guidance.
The solution is affordable, portable, and practical for stress management.

Structured PICO

Intervention

Real-Time Stress Detection and Relaxation Feedback System integrating EMG sensors, temperature sensing, ESP32 microcontroller, and Support Vector Machine (SVM) classifier

Outcome

Stress classification accuracy and system latency

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.

Limitations

Lack of longitudinal studies to quantify the sustained impact of the real-time feedback mechanism on user stress resilience
Current system relies on peripheral data and lacks integration of additional physiological sensor modalities like EEG or Electrodermal Activity

Abstract

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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