Cross-Domain Multi-Person Human Activity Recognition via Near-Field Wi-Fi Sensing

• URL: http://arxiv.org/abs/2510.17816v1
• Date: Sat, 27 Sep 2025 03:22:15 GMT
• Title: Cross-Domain Multi-Person Human Activity Recognition via Near-Field Wi-Fi Sensing
• Authors: Xin Li, Jingzhi Hu, Yinghui He, Hongbo Wang, Jin Gan, Jun Luo,
• Abstract summary: We propose WiAnchor, a novel training framework for efficient cross-domain adaptation in the presence of incomplete activity categories.<n>We construct a comprehensive dataset to thoroughly evaluate WiAnchor, achieving over 90% cross-domain accuracy with absent activity categories.
• Score: 20.1340684071988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wi-Fi-based human activity recognition (HAR) provides substantial convenience and has emerged as a thriving research field, yet the coarse spatial resolution inherent to Wi-Fi significantly hinders its ability to distinguish multiple subjects. By exploiting the near-field domination effect, establishing a dedicated sensing link for each subject through their personal Wi-Fi device offers a promising solution for multi-person HAR under native traffic. However, due to the subject-specific characteristics and irregular patterns of near-field signals, HAR neural network models require fine-tuning (FT) for cross-domain adaptation, which becomes particularly challenging with certain categories unavailable. In this paper, we propose WiAnchor, a novel training framework for efficient cross-domain adaptation in the presence of incomplete activity categories. This framework processes Wi-Fi signals embedded with irregular time information in three steps: during pre-training, we enlarge inter-class feature margins to enhance the separability of activities; in the FT stage, we innovate an anchor matching mechanism for cross-domain adaptation, filtering subject-specific interference informed by incomplete activity categories, rather than attempting to extract complete features from them; finally, the recognition of input samples is further improved based on their feature-level similarity with anchors. We construct a comprehensive dataset to thoroughly evaluate WiAnchor, achieving over 90% cross-domain accuracy with absent activity categories.

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