Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing

Gestures serve an important role in enabling natural interactions with computing devices, and they form an important part of everyday nonverbal communication. In increasingly many application scenarios of gesture interaction, such as gesture-based authentication, calligraphy, sketching, and even art...

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Autores principales:	Weiyuan Tong, Rong Li, Xiaoqing Gong, Shuangjiao Zhai, Xia Zheng, Guixin Ye
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/750794ef0a1641768cf50f3384afae07
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spelling	oai:doaj.org-article:750794ef0a1641768cf50f3384afae072021-11-22T01:11:12ZExploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing1875-905X10.1155/2021/4770143https://doaj.org/article/750794ef0a1641768cf50f3384afae072021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4770143https://doaj.org/toc/1875-905XGestures serve an important role in enabling natural interactions with computing devices, and they form an important part of everyday nonverbal communication. In increasingly many application scenarios of gesture interaction, such as gesture-based authentication, calligraphy, sketching, and even artistic expression, not only are the underlying gestures complex and consist of multiple strokes but also the correctness of the gestures depends on the order at which the strokes are performed. In this paper, we present WiCG, an innovative and novel WiFi sensing approach for capturing and providing feedback on stroke order. Our approach tracks the user’s hand movement during writing and exploits this information in combination with statistical methods and machine learning techniques to infer what characters have been written and at which stroke order. We consider Chinese calligraphy as our use case as the resulting gestures are highly complex, and their assessment depends on the correct stroke order. We develop a set of analyses and algorithms to overcome many issues of this challenging task. We have conducted extensive experiments and user studies to evaluate our approach. Experimental results show that our approach is highly effective in identifying the written characters and their written stroke order. We show that our approach can adapt to different deployment environments and user patterns.Weiyuan TongRong LiXiaoqing GongShuangjiao ZhaiXia ZhengGuixin YeHindawi LimitedarticleTelecommunicationTK5101-6720ENMobile Information Systems, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Weiyuan Tong Rong Li Xiaoqing Gong Shuangjiao Zhai Xia Zheng Guixin Ye Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing
description	Gestures serve an important role in enabling natural interactions with computing devices, and they form an important part of everyday nonverbal communication. In increasingly many application scenarios of gesture interaction, such as gesture-based authentication, calligraphy, sketching, and even artistic expression, not only are the underlying gestures complex and consist of multiple strokes but also the correctness of the gestures depends on the order at which the strokes are performed. In this paper, we present WiCG, an innovative and novel WiFi sensing approach for capturing and providing feedback on stroke order. Our approach tracks the user’s hand movement during writing and exploits this information in combination with statistical methods and machine learning techniques to infer what characters have been written and at which stroke order. We consider Chinese calligraphy as our use case as the resulting gestures are highly complex, and their assessment depends on the correct stroke order. We develop a set of analyses and algorithms to overcome many issues of this challenging task. We have conducted extensive experiments and user studies to evaluate our approach. Experimental results show that our approach is highly effective in identifying the written characters and their written stroke order. We show that our approach can adapt to different deployment environments and user patterns.
format	article
author	Weiyuan Tong Rong Li Xiaoqing Gong Shuangjiao Zhai Xia Zheng Guixin Ye
author_facet	Weiyuan Tong Rong Li Xiaoqing Gong Shuangjiao Zhai Xia Zheng Guixin Ye
author_sort	Weiyuan Tong
title	Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing
title_short	Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing
title_full	Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing
title_fullStr	Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing
title_full_unstemmed	Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing
title_sort	exploiting serialized fine-grained action recognition using wifi sensing
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/750794ef0a1641768cf50f3384afae07
work_keys_str_mv	AT weiyuantong exploitingserializedfinegrainedactionrecognitionusingwifisensing AT rongli exploitingserializedfinegrainedactionrecognitionusingwifisensing AT xiaoqinggong exploitingserializedfinegrainedactionrecognitionusingwifisensing AT shuangjiaozhai exploitingserializedfinegrainedactionrecognitionusingwifisensing AT xiazheng exploitingserializedfinegrainedactionrecognitionusingwifisensing AT guixinye exploitingserializedfinegrainedactionrecognitionusingwifisensing
_version_	1718418287623143424

Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing

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