Embedded System for ECG Signal Monitoring and Fatigue Detection in Elderly Individuals Using Machine Learning Models

Ascertaining fatigue in elderly people is crucial both for preventing future health complications and for enhancing their quality of life. In this paper, we present an embedded system for real-time fatigue detection and monitoring based on electrocardiogram (ECG) signals, leveraging cost-effective sensors and advanced deep learning architectures. The proposed framework integrates an AD8232 ECG sensor with an ESP32/Raspberry Pi platform for continuous signal acquisition, followed by preprocessing through a 4th-order Butterworth bandpass filter, feature extraction, dimensionality reduction with PCA, and classification using recurrent neural network models. Unlike previous studies relying on multi-sensor or image-based approaches, our solution demonstrates high efficiency, scalability, and affordability by employing a single low-cost ECG sensor. Three neural architectures were evaluated: standard Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Among them, the GRU model achieved the highest accuracy (98.86%), followed by LSTM (97.73%), whereas standard RNNs lagged behind (82.76%). Experimental results confirm the robustness of GRU in capturing temporal dependencies in ECG data, outperforming other models in both accuracy and computational efficiency. This study highlights the feasibility of deploying lightweight yet powerful AI models in embedded healthcare systems for elderly individuals. By enabling early detection of fatigue as a critical risk factor for falls, cardiovascular incidents, and reduced autonomy. Our approach offers significant societal benefits, including preventive care, reduced hospitalization costs, and improved independence. Future work will extend the dataset and validate system robustness in real-world environments to enhance clinical applicability.