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

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...