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Device-free near-field human sensing using WiFi signals

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Abstract

Wireless device-free human sensing is an emerging technique of Internet of Things, which holds great potential for ubiquitous location-based services and human-interaction applications. Although existing studies can detect human appearance, they still neglect to further identify whether a user is approaching a sensor or not, which is critical for fine-grained recognition of human behaviors. In this paper, we first conduct comprehensive experiments to measure relationships between signal fading and human positions. The experimental results show that signal fading stepwise changes with different distances of the human to a sensor. Moreover, the signal fading is worse when the human is located closer to an antenna of the sensor. Motivated by these observations, we propose NSee, a novel system for device-free near-field human sensing without site-survey fingerprints. Specifically, we cluster signal fading features of different antennas by a Gaussian mixture model, and further propose a cluster identification algorithm to identify clusters in correspondence to different near-field subareas of human appearance. Based on cluster characteristics, NSee can recognize near-field human presence with online sensing. We implement a prototype of NSee system based on a commercial WiFi card with multiple antennas. Extensive experimental results illustrate that the proposed system can achieve an averaged accuracy of 90% in device-free near-field human recognition.

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Funding

This research is supported by the Young Scientists Fund of the National Natural Science Foundation of China No. 61902211, the National Natural Science Foundation of China No. 61872447, the Natural Science Foundation of Tianjin City (CN) No. 18JCQNJC69900, and the Natural Science Foundation of Chongqing: No.CSTC2018JCYJA1879

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Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, China

    Liangyi Gong

  2. Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, Chongqing, China

    Chaocan Xiang & Chao Chen

  3. The College of Computer Science, Chongqing University, Chongqing, 400044, China

    Chaocan Xiang & Chao Chen

  4. School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Xiaochen Fan

  5. National University of Defense Technology, Changsha, China

    Tao Wu

  6. Institute of Information Engineering, Beijing, China

    Miao Yu

  7. College of Computer Science and Technology, Harbin Engineering University, Harbin, China

    Wu Yang

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  1. Liangyi Gong
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  3. Xiaochen Fan
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  4. Tao Wu
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Correspondence to Chaocan Xiang.

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Gong, L., Xiang, C., Fan, X. et al. Device-free near-field human sensing using WiFi signals. Pers Ubiquit Comput 26, 461–474 (2022). https://doi.org/10.1007/s00779-020-01385-4

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  • DOI: https://doi.org/10.1007/s00779-020-01385-4

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