Domain Adaptation with Contrastive Group Construction for Human Activity Recognition in Multi-Sensor

Multi-sensor-based human activity recognition (HAR) models trained with deep learning often exhibit limited generalization when applied to data collected under conditions different from those seen during training. To alleviate this issue, we present an adversarial domain adaptation framework that incorporates contrastive group construction to promote class-aware feature alignment. Specifically, augmented and perturbed sample groups are generated in both source and target domains and optimized through contrastive learning objectives, allowing the feature extractor to compact semantically similar representations while separating dissimilar ones without relying on target-domain annotations. This joint design preserves semantic structure while reducing cross-domain distribution discrepancies, resulting in representations that are both domain-invariant and discriminative. Experiments conducted on the Opportunity dataset validate the effectiveness of the proposed approach, demonstrating consistent performance gains over representative unsupervised domain adaptation methods.