Recognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers

Human gestures have been considered as one of the important human-computer interaction modes. With the fast development of wireless technology in urban Internet of Things (IoT) environment, Wi-Fi can not only provide the function of high-speed network communication but also has great development pot...

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Autores principales:	Haixia Yang, Zhaohui Ji, Jun Sun, Fanan Xing, Yixian Shen, Wei Zhuang, Weigong Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/42a1ac796c4844f984475b8fa5ec4ca6
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id	oai:doaj.org-article:42a1ac796c4844f984475b8fa5ec4ca6
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spelling	oai:doaj.org-article:42a1ac796c4844f984475b8fa5ec4ca62021-11-15T01:19:04ZRecognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers1530-867710.1155/2021/7821241https://doaj.org/article/42a1ac796c4844f984475b8fa5ec4ca62021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7821241https://doaj.org/toc/1530-8677Human gestures have been considered as one of the important human-computer interaction modes. With the fast development of wireless technology in urban Internet of Things (IoT) environment, Wi-Fi can not only provide the function of high-speed network communication but also has great development potential in the field of environmental perception. This paper proposes a gesture recognition system based on the channel state information (CSI) within the physical layer of Wi-Fi transmission. To solve the problems of noise interference and phase offset in the CSI, we adopt a model based on CSI quotient. Then, the amplitude and phase curves of CSI are smoothed using Savitzky-Golay filter, and the one-dimensional convolutional neural network (1D-CNN) is used to extract the gesture features. Then, the support vector machine (SVM) classifier is adopted to recognize the gestures. The experimental results have shown that our system can achieve a recognition rate of about 90% for three common gestures, including pushing forward, left stroke, and waving. Meanwhile, the effects of different human orientation and model parameters on the recognition results are analyzed as well.Haixia YangZhaohui JiJun SunFanan XingYixian ShenWei ZhuangWeigong ZhangHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Haixia Yang Zhaohui Ji Jun Sun Fanan Xing Yixian Shen Wei Zhuang Weigong Zhang Recognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers
description	Human gestures have been considered as one of the important human-computer interaction modes. With the fast development of wireless technology in urban Internet of Things (IoT) environment, Wi-Fi can not only provide the function of high-speed network communication but also has great development potential in the field of environmental perception. This paper proposes a gesture recognition system based on the channel state information (CSI) within the physical layer of Wi-Fi transmission. To solve the problems of noise interference and phase offset in the CSI, we adopt a model based on CSI quotient. Then, the amplitude and phase curves of CSI are smoothed using Savitzky-Golay filter, and the one-dimensional convolutional neural network (1D-CNN) is used to extract the gesture features. Then, the support vector machine (SVM) classifier is adopted to recognize the gestures. The experimental results have shown that our system can achieve a recognition rate of about 90% for three common gestures, including pushing forward, left stroke, and waving. Meanwhile, the effects of different human orientation and model parameters on the recognition results are analyzed as well.
format	article
author	Haixia Yang Zhaohui Ji Jun Sun Fanan Xing Yixian Shen Wei Zhuang Weigong Zhang
author_facet	Haixia Yang Zhaohui Ji Jun Sun Fanan Xing Yixian Shen Wei Zhuang Weigong Zhang
author_sort	Haixia Yang
title	Recognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers
title_short	Recognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers
title_full	Recognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers
title_fullStr	Recognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers
title_full_unstemmed	Recognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers
title_sort	recognition for human gestures based on convolutional neural network using the off-the-shelf wi-fi routers
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/42a1ac796c4844f984475b8fa5ec4ca6
work_keys_str_mv	AT haixiayang recognitionforhumangesturesbasedonconvolutionalneuralnetworkusingtheofftheshelfwifirouters AT zhaohuiji recognitionforhumangesturesbasedonconvolutionalneuralnetworkusingtheofftheshelfwifirouters AT junsun recognitionforhumangesturesbasedonconvolutionalneuralnetworkusingtheofftheshelfwifirouters AT fananxing recognitionforhumangesturesbasedonconvolutionalneuralnetworkusingtheofftheshelfwifirouters AT yixianshen recognitionforhumangesturesbasedonconvolutionalneuralnetworkusingtheofftheshelfwifirouters AT weizhuang recognitionforhumangesturesbasedonconvolutionalneuralnetworkusingtheofftheshelfwifirouters AT weigongzhang recognitionforhumangesturesbasedonconvolutionalneuralnetworkusingtheofftheshelfwifirouters
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Recognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers

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