Physical activity monitoring using wearable devices based on machine learning algorithms

0.6032). The method effectively captures temporal activity patterns, enabling sleep-stage transition identification and sedentary-event detection (MET < 1.6 for 30 min). The proposed framework provides an interpretable mapping from accelerometer signals to behavioral-health indicators, offering a practical approach for long-term wearable-based monitoring and personalized health management.