ActiveSelfHAR: Incorporating Self-Training Into Active Learning to Improve Cross-Subject Human Activity Recognition

• Published in: IEEE internet of things journal Vol. 11; no. 4; pp. 6833 - 6847
• Main Authors: Wei, Baichun, Yi, Chunzhi, Zhang, Qi, Zhu, Haiqi, Zhu, Jianfei, Jiang, Feng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active learning; Adaptation models; Body area networks; Correlation; Costs; Cross-subject adaptation; Data models; Datasets; Deep learning; deep learning (DL); Human activity recognition; Internet of medical things; Internet of Things; Labeling; Labels; Machine learning; semi-supervised learning; Training; Upper bounds; wearable sensors; Wireless networks; Wireless sensor networks
• DOI: 10.1109/JIOT.2023.3314150
• ISSN: 2327-4662

Deep learning (DL)-based human activity recognition (HAR) methods have shown promise in the applications of health Internet of Things (IoT) and wireless body sensor networks (BSNs). However, adapting these methods to new users in real-world scenarios is challenging due to the cross-subject issue. To solve this issue, we propose ActiveSelfHAR, a framework that combines active learning's benefit of sparsely acquiring informative samples with actual labels and self-training's benefit of effectively utilizing unlabeled data to adapt the HAR model to the target domain, i.e., the new users. ActiveSelfHAR consists of several key steps. First, we utilize the model from the source domain to select and label the domain invariant samples, forming a self-training set. Second, we leverage the distribution information of the self-training set to identify and annotate samples located around the class boundaries, forming a core set. Third, we augment the core set by considering the spatiotemporal relationships among the samples in the nonself-training set. Finally, we combine the self-training set and augmented core set to construct a diverse training set in the target domain and fine-tune the HAR model. Through leave-one-subject-out validation on three IMU-based data sets and one EMG-based data set, our method achieves mean HAR accuracies of 95.20%, 82.06%, 89.52%, and 92.82%, respectively. Our method demonstrates similar HAR accuracies to the upper bound, i.e., fine-tuning framework with approximately 1% labeled data of the target data set, while significantly improving data efficiency and time cost. Our work highlights the potential of implementing user-independent HAR methods into health IoT and BSN.