Location independent human activity recognition using self-training CSI-based techniques for wireless sensor networks

Wong, Yan Chiew and Saad Abuhoureyah, Fahd (2025) Location independent human activity recognition using self-training CSI-based techniques for wireless sensor networks. IEEE Internet of Things Journal, 12 (14). pp. 27419-27434. ISSN 2327-4662

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Abstract

Human activity recognition (HAR) using WiFi is applied across various domains ranging from smart environments, the Internet of Things (IoT) and immersive virtual gaming. The environmental effects of WiFi sensing lie in its susceptibility to variations in physical surroundings, which influence signal strength and accuracy in detecting human activity. Innovative solutions are needed to meet these demands, such as activity-adapted learning for seamless feature transfer and recognition across various locations, reducing the reliance on extensive training datasets. This work proposes a framework incorporating a confidence threshold to filter unreliable samples, a progressive self-training strategy to integrate unlabeled data, and a weighted self-training approach to counter class imbalance. The proposed model explores HAR and its improved performance by integrating self-training techniques. This work enhances HAR by reconciling self-training’s potential with challenges and offering practical insights for reliable activity recognition within wireless sensor networks. The results of experiments show that the self-training method, which uses channel state information based features to train the model with unlabeled data, is up to 97.5% accurate. Additionally, experiments using HAR datasets validate the proposed method and displays performance improvements over baselines.

Item Type:	Article
Uncontrolled Keywords:	Channel state information (CSI), Human activity recognition (HAR), Self-adaptive algorithms, Self-training and dynamic environments, WiFi sensing.
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	30 Dec 2025 03:00
Last Modified:	30 Dec 2025 03:00
URI:	http://eprints.utem.edu.my/id/eprint/29306
Statistic Details:	View Download Statistic

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