Research Article
Device-Free Gesture Recognition Using Time Series RFID Signals
@INPROCEEDINGS{10.1007/978-3-030-36442-7_10, author={Han Ding and Lei Guo and Cui Zhao and Xiao Li and Wei Shi and Jizhong Zhao}, title={Device-Free Gesture Recognition Using Time Series RFID Signals}, proceedings={Broadband Communications, Networks, and Systems. 10th EAI International Conference, Broadnets 2019, Xi’an, China, October 27-28, 2019, Proceedings}, proceedings_a={BROADNETS}, year={2019}, month={12}, keywords={Gesture recognition RFID Device free}, doi={10.1007/978-3-030-36442-7_10} }- Han Ding
Lei Guo
Cui Zhao
Xiao Li
Wei Shi
Jizhong Zhao
Year: 2019
Device-Free Gesture Recognition Using Time Series RFID Signals
BROADNETS
Springer
DOI: 10.1007/978-3-030-36442-7_10
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.