Gait Analysis Using Wearable Sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable s...

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Main Authors:	Tao, Weijun, Liu, Tao, Zheng, Rencheng, Feng, Hutian
Format:	Online
Language:	English
Published:	Molecular Diversity Preservation International (MDPI) 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3304165/

id	pubmed-3304165
recordtype	oai_dc
spelling	pubmed-33041652012-03-21 Gait Analysis Using Wearable Sensors Tao, Weijun Liu, Tao Zheng, Rencheng Feng, Hutian Review Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications. Molecular Diversity Preservation International (MDPI) 2012-02-16 /pmc/articles/PMC3304165/ /pubmed/22438763 http://dx.doi.org/10.3390/s120202255 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Tao, Weijun Liu, Tao Zheng, Rencheng Feng, Hutian
spellingShingle	Tao, Weijun Liu, Tao Zheng, Rencheng Feng, Hutian Gait Analysis Using Wearable Sensors
author_facet	Tao, Weijun Liu, Tao Zheng, Rencheng Feng, Hutian
author_sort	Tao, Weijun
title	Gait Analysis Using Wearable Sensors
title_short	Gait Analysis Using Wearable Sensors
title_full	Gait Analysis Using Wearable Sensors
title_fullStr	Gait Analysis Using Wearable Sensors
title_full_unstemmed	Gait Analysis Using Wearable Sensors
title_sort	gait analysis using wearable sensors
description	Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.
publisher	Molecular Diversity Preservation International (MDPI)
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3304165/
_version_	1611514120179286016

Gait Analysis Using Wearable Sensors

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