Mental Stress Detection Using Wearable Sensors and Machine Learning

Abstract - This project proposes a real-time stress level detection system using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) layers. It collects biometric data—heart rate (BPM) and SpO₂ levels— from a pulse sensor connected to an ESP8266 microcontroller. The data is transmitted via Wi-Fi to a Python-based backend for processing. The collected signals are normalized usingMinMaxScaler and reshaped to preserve their sequential nature. The preprocessed data is fed into a trained RNN model that classifies stress levels into four categories: No Stress,Medium, High, and Very High. The model uses a softmax output layer and categorical cross-entropy loss foraccurate multi-class classification. Predictions are generated in real time and displayed for immediate feedback. The model was previously trained on a synthetically generated dataset reflecting stressrelated biometric thresholds. The system is scalable and can be integrated into mobile applications using TensorFlow Lite, offering continuous and portable stress monitoring. This approach enables early detection and intervention for stress-related health issues. This innovative system bridges wearable technology with deep learning for efficient mental health monitoring. Its real-time feedback mechanism empowers users to take timely actions to manage stress effectively. Key Words: Stress Detection, RNN, LSTM, Heart Rate, SpO₂, ESP8266, Real-Time Monitoring, Deep Learning