Optimization of Wearable Biosensor Data for Stress Classification Using Machine Learning and Explainable AI

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Abstract
This work utilizes wearable devices for real-time stress detection and investigates the effectiveness of meditation audio in reducing stress levels after academic exposure. Physiological data, including Interbeat Interval (IBI)-derived Heart Rate Variability (HRV), Blood Volume Pulse (BVP), and electrodermal activity (EDA), are collected during the Montreal Imaging Stress Task (MIST). The stress classification methodology employs an integrated approach using Genetic Algorithm and Mutual Information to reduce feature set redundancy. It further uses Bayesian optimization to fine-tune machine learning hyperparameters. The results indicate that the combination of EDA, BVP, and HRV achieves the highest classification accuracy of 98.28% and 97.02% using the Gradient Boosting (GB) algorithm for 2-level and 3-level stress classification. In contrast, EDA and HRV alone achieve a comparable accuracy of 97.07% and 95.23% for 2-level and 3-level stress classification, respectively. Furthermore, the SHAP Explainable AI (XAI) analysis confirms that HRV and EDA are the most significant features for stress classification. The study also finds evidence that listening to meditation audio reduces stress levels. These findings highlight the potential of wearable technology combined with machine learning for real-time stress monitoring and management in academic environments.

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