Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Electronics

DOI: 10.3390/electronics15061171

Authors: Yongtu Tan, Shikang Lian

Institution: Guangdong University of Technology

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province