MobHAR: Source-free Knowledge Transfer for Human Activity Recognition on Mobile Devices

Human Activity Recognition (HAR) faces significant challenges when deployed in real-world scenarios due to non-independent and identically distributed (non-IID) data distributions. While existing domain adaptation (DA) approaches attempt to address this issue, they either require access to source data or struggle with large domain shifts. This paper presents a novel source-free domain adaptation framework for HAR that effectively handles substantial domain discrepancies across different datasets. Our approach introduces two key innovations: (1) a Discriminative Information Gramian (DIG) method that quantifies the relationship between target-domain samples and the source domain without requiring access to source data, and (2) an unsupervised domain generalization technique that ensures consistent feature extraction across augmented data samples, enhancing the model’s effectiveness in the target domain. We evaluate our framework on five diverse HAR datasets comprising 87 users with varying demographics, devices, and environmental conditions. In single-source scenarios, our method achieves 76.77% accuracy and 67.03% F1-score, surpassing state-of-the-art solutions by 9.77% and 17.43%, respectively. For multi-source scenarios, we attain 85.33% accuracy and 79.55% F1-score, exceeding existing methods by at least 8.8% and 14.8%, respectively. This work represents the first successful attempt at dataset-level HAR domain adaptation without access to source data, marking a significant advancement in practical HAR applications.