Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162593
Title: Learning gestures from WiFi: a Siamese recurrent convolutional architecture
Authors: Yang, Jianfei
Zou, Han
Zhou, Yuxun
Xie, Lihua
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Yang, J., Zou, H., Zhou, Y. & Xie, L. (2019). Learning gestures from WiFi: a Siamese recurrent convolutional architecture. IEEE Internet of Things Journal, 6(6), 10763-10772. https://dx.doi.org/10.1109/JIOT.2019.2941527
Project: RG72/19
Journal: IEEE Internet of Things Journal 
Abstract: 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.
URI: https://hdl.handle.net/10356/162593
ISSN: 2327-4662
DOI: 10.1109/JIOT.2019.2941527
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JIOT.2019.2941527.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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