Abstract
A wide range of applications can benefit from the human motion recognition techniques that utilize the fluctuation of time series wireless signals to infer human gestures. Among which, device-free gesture recognition becomes more attractive because it does not need human to carry or wear sensing devices. Existing device-free solutions, though yielding good performance, require heavy crafting on data preprocessing and feature extraction. In this paper, we propose RF-Mnet, a deep-learning based device-free gesture recognition framework, which explores the possibility of directly utilizing time series RFID tag signal to recognize static and dynamic gestures. We conduct extensive experiments in three different environments. The results demonstrate the superior effectiveness of the proposed RF-Mnet framework.
This work is supported by NSFC Grants No. 61802299, 61772413, 61672424, Project funded by China Postdoctoral Science Foundation No. 2018M643663.
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Ding, H., Guo, L., Zhao, C., Li, X., Shi, W., Zhao, J. (2019). Device-Free Gesture Recognition Using Time Series RFID Signals. In: Li, Q., Song, S., Li, R., Xu, Y., Xi, W., Gao, H. (eds) Broadband Communications, Networks, and Systems. Broadnets 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-030-36442-7_10
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