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Learning Gestures From WiFi: A Siamese Recurrent Convolutional Architecture

2019; Institute of Electrical and Electronics Engineers; Volume: 6; Issue: 6 Linguagem: Inglês

10.1109/jiot.2019.2941527

2372-2541

Jianfei Yang, Han Zou, Yuxun Zhou, Lihua Xie,

Wireless Networks and Protocols

We propose a gesture recognition system that leverages existing WiFi infrastructures and learns gestures from channel state information (CSI) measurements. Having developed an innovative OpenWrt-based platform for commercial WiFi devices to extract CSI data, we propose a novel deep Siamese representation learning architecture for one-shot gesture recognition. Technically, our model extends the capacity of spatio-temporal patterns learning for the standard Siamese structure by incorporating convolutional and bidirectional recurrent neural networks. More importantly, the representation learning is ameliorated by our Siamese framework and transferable pairwise loss which helps to remove structured noise, such as individual heterogeneity and various measurement conditions during domain-different training. Meanwhile, our Siamese model also enables one-shot learning for higher availability in reality. We prototype our system on commercial WiFi routers. The experiments demonstrate that our model outperforms state-of-the-art solutions for temporal-spatial representation learning and achieves satisfactory results under one-shot conditions.