Machine learning based ECG analysis with data augmentation for fatigue status detection in elderly individuals
Chokri Baccouch, Chaima Bahar, Ahmed Zouinkhi, Aymen Flah, Claude Ziad El-bayeh, Shaaban M. Shaaban et al. · Scientific Reports · 2026
Abstract Fatigue detection in elderly individuals is critical for preventing health complications and enhancing quality of life. Electrocardiogram (ECG) signals offer a non-invasive way to capture fatigue-related physiological changes, but few solutions are lightweight, interpretable, and deployable in real-world settings. This study proposes a portable, low-cost ECG-based system for real-time fatigue detection using an extended AD8232 module and ESP32 microcontroller. A dataset of 6304 ECG segments was collected from 100 elderly participants. Data augmentation (SMOTE and Gaussian noise) was applied. GRU, LSTM, and RNN models were trained and validated with subject-wise splitting. The GRU model achieved 97.96% accuracy, 0.99 AUC, and 0.98 F1-score, significantly outperforming LSTM, RNN, Random Forest, and Logistic Regression baselines. This work demonstrates the feasibility of low-cost, embedded ECG-based fatigue detection and provides interpretable, generalizable
Results: suitable for elderly care applications. Further clinical validation is planned.