Fall Feature Simulation and Wi-Fi Sensing Dataset Construction Based on Time-domain Digital Coding Metasurface
With the widespread application of Wi-Fi sensing technology in intelligent health monitoring, constructing high-quality perception datasets has become a key challenge. Particularly in monitoring abnormal behaviors, such as falls, traditional methods rely on repeated human experiments, which not only...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Science Publishing & Media Ltd. (CSPM)
2025-08-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR24247 |
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| Summary: | With the widespread application of Wi-Fi sensing technology in intelligent health monitoring, constructing high-quality perception datasets has become a key challenge. Particularly in monitoring abnormal behaviors, such as falls, traditional methods rely on repeated human experiments, which not only poses safety risks but also raises ethical concerns. To address these issues, this paper proposes a time-domain digital coding metasurface-assisted data acquisition method. By simulating the Doppler effect and micro-Doppler characteristics of the human body, the time-domain digital coding metasurface can effectively replace human experiments and assist in constructing Wi-Fi sensing datasets. To verify the feasibility of this method, we develop a time-domain digital coding metasurface with 0°–360° full-phase modulation capability. Experimental results show that the signals generated by the metasurface retain the motion characteristics of the human body, complement real samples, reduce the complexity of data collection, and finally improve the monitoring accuracy of the classification model significantly. This method provides an innovative and feasible solution for data acquisition for Wi-Fi sensing technology. |
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| ISSN: | 2095-283X |