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Article Dans Une Revue IEEE Transactions on Mobile Computing Année : 2022

Wi-PIGR: path independent gait recognition with commodity Wi-Fi

Résumé

Wi-Fi based gait recognition has many potential applications. However, the gait information derived from Wi-Fi changes with the walking path. This makes the human identification through gait really challenging, the existing Wi-Fi based gait recognition systems require the subject walking along a predetermined path. This path dependence restriction impedes Wi-Fi based gait recognition from being widely used. In this paper, a path independent gait recognition system for a single subject, Wi-PIGR, is proposed. In Wi-PIGR, the subject is identified through the gait regardless of the walking path. Specifically, an extra receiver is introduced to get CSI data in orthogonal directions. A series of signal processing techniques are proposed to eliminate the differences among signals introduced by walking along the arbitrary paths and generate a high quality path independent signal spectrogram. Furthermore, a deep learning approach is integrated into the feature extraction. The experiment results in typical indoor environment demonstrate the superior performance of Wi-PIGR, with the average recognition accuracy of 77.15%, when the number of subjects is 50.
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Dates et versions

hal-03363404 , version 1 (03-10-2021)

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Lei Zhang, Cong Wang, Daqing Zhang. Wi-PIGR: path independent gait recognition with commodity Wi-Fi. IEEE Transactions on Mobile Computing, 2022, 21 (9), pp.3414 - 3427. ⟨10.1109/TMC.2021.3052314⟩. ⟨hal-03363404⟩
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