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.