A Novel Domain Adaptation Framework for Wearable Human Activity Recognition Using Multi-Sensor Feature Alignment

Wearable Human Activity Recognition (HAR) models often degrade across users and sensor placements due to domain shifts. This paper presents the Multi-Sensor Adaptive Feature Alignment Network (MSAFAN), integrating Sensor-Specific Normalization Layer (SSNL), Hybrid Polynomial Feature Transformation (HPFT), Conditional Alignment Loss (CAL), and Entropy-Guided Pseudo-Labeling (EGPL) for class-wise adaptation and robust cross-sensor generalization. Evaluated on four benchmark datasets: BAR, DSADS, PAMAP2, and MM-DOS, the MSAFAN improves macro-F1 by 8.4% and accuracy by 10.3% while reducing computational cost by 26% over state-of-the-art UDA models. The framework achieves stable convergence, efficient adaptation, and scalable performance, confirming its suitability for real-time deployment in edge AI and wearable computing applications.