Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

Abstract The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal information, regarding identification and healthcare. Current wearable electronics of gait analysis a...

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Autores principales:	Zixuan Zhang, Tianyiyi He, Minglu Zhu, Zhongda Sun, Qiongfeng Shi, Jianxiong Zhu, Bowei Dong, Mehmet Rasit Yuce, Chengkuo Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Electronics TK7800-8360 Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/033a34c87a444cc8882f081ab8d169db
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spelling	oai:doaj.org-article:033a34c87a444cc8882f081ab8d169db2021-12-02T13:43:48ZDeep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications10.1038/s41528-020-00092-72397-4621https://doaj.org/article/033a34c87a444cc8882f081ab8d169db2020-10-01T00:00:00Zhttps://doi.org/10.1038/s41528-020-00092-7https://doaj.org/toc/2397-4621Abstract The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal information, regarding identification and healthcare. Current wearable electronics of gait analysis are mainly limited by high fabrication cost, operation energy consumption, or inferior analysis methods, which barely involve machine learning or implement nonoptimal models that require massive datasets for training. Herein, we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data. The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information, regarding the identity, health status, and activity of the users. To further address the issue of ineffective analysis methods, an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed, which produces a 93.54% identification accuracy of 13 participants and detects five different human activities with 96.67% accuracy. Toward practical application, we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring, healthcare, identification, and future smart home applications.Zixuan ZhangTianyiyi HeMinglu ZhuZhongda SunQiongfeng ShiJianxiong ZhuBowei DongMehmet Rasit YuceChengkuo LeeNature PortfolioarticleElectronicsTK7800-8360Materials of engineering and construction. Mechanics of materialsTA401-492ENnpj Flexible Electronics, Vol 4, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronics TK7800-8360 Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	Electronics TK7800-8360 Materials of engineering and construction. Mechanics of materials TA401-492 Zixuan Zhang Tianyiyi He Minglu Zhu Zhongda Sun Qiongfeng Shi Jianxiong Zhu Bowei Dong Mehmet Rasit Yuce Chengkuo Lee Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications
description	Abstract The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal information, regarding identification and healthcare. Current wearable electronics of gait analysis are mainly limited by high fabrication cost, operation energy consumption, or inferior analysis methods, which barely involve machine learning or implement nonoptimal models that require massive datasets for training. Herein, we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data. The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information, regarding the identity, health status, and activity of the users. To further address the issue of ineffective analysis methods, an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed, which produces a 93.54% identification accuracy of 13 participants and detects five different human activities with 96.67% accuracy. Toward practical application, we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring, healthcare, identification, and future smart home applications.
format	article
author	Zixuan Zhang Tianyiyi He Minglu Zhu Zhongda Sun Qiongfeng Shi Jianxiong Zhu Bowei Dong Mehmet Rasit Yuce Chengkuo Lee
author_facet	Zixuan Zhang Tianyiyi He Minglu Zhu Zhongda Sun Qiongfeng Shi Jianxiong Zhu Bowei Dong Mehmet Rasit Yuce Chengkuo Lee
author_sort	Zixuan Zhang
title	Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications
title_short	Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications
title_full	Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications
title_fullStr	Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications
title_full_unstemmed	Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications
title_sort	deep learning-enabled triboelectric smart socks for iot-based gait analysis and vr applications
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/033a34c87a444cc8882f081ab8d169db
work_keys_str_mv	AT zixuanzhang deeplearningenabledtriboelectricsmartsocksforiotbasedgaitanalysisandvrapplications AT tianyiyihe deeplearningenabledtriboelectricsmartsocksforiotbasedgaitanalysisandvrapplications AT mingluzhu deeplearningenabledtriboelectricsmartsocksforiotbasedgaitanalysisandvrapplications AT zhongdasun deeplearningenabledtriboelectricsmartsocksforiotbasedgaitanalysisandvrapplications AT qiongfengshi deeplearningenabledtriboelectricsmartsocksforiotbasedgaitanalysisandvrapplications AT jianxiongzhu deeplearningenabledtriboelectricsmartsocksforiotbasedgaitanalysisandvrapplications AT boweidong deeplearningenabledtriboelectricsmartsocksforiotbasedgaitanalysisandvrapplications AT mehmetrasityuce deeplearningenabledtriboelectricsmartsocksforiotbasedgaitanalysisandvrapplications AT chengkuolee deeplearningenabledtriboelectricsmartsocksforiotbasedgaitanalysisandvrapplications
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Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

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