Abstract
AbstractThe analysis of human gait is of fundamental importance for the monitoring and enhancement of athletes’ performances. The kinematics and kinetics of human gait are mostly investigated with optical motion capture systems and force plates that require specialised laboratories and limit the possible test conditions. On the contrary, body-attached sensor networks provide an opportunity for long-term acquisitions in unsupervised, naturalistic scenarios. In this study, a wearable sensor network consisting of two wireless dataloggers and two instrumented insoles with eight pressure sensors each is used. Custom algorithms for the automatic detection of hike events and the estimation of the related temporal parameters based on sensors data are presented. The proposed algorithms were tested against laboratory measurements performed on an instrumented treadmill and showed relative errors of less than 2.5% in the estimation of stride time, step time and cadence. Higher relative errors were found in the estimation of stance and swing phases. The developed algorithms were also applied in a field study. In this paper data from one subject are considered. The aim of this research work is to provide an effective sensor-based methodology for the evaluation of gait parameters in naturalistic settings.
Funder
Deutsche Forschungsgemeinschaft
Sächsischer Landtag
Technische Universität Chemnitz
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials,Physical Therapy, Sports Therapy and Rehabilitation,Orthopedics and Sports Medicine,Modeling and Simulation,Biomedical Engineering
Reference13 articles.
1. Prakash C, Kumar R, Mittal N (2018) Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges. Artif Intell Rev 49(1):1–40. https://doi.org/10.1007/s10462-016-9514-6
2. Dunn M, Kelley J (2015) Non-invasive, spatio-temporal gait analysis for sprint running using a single camera. Procedia Eng 112:528–533. https://doi.org/10.1016/j.proeng.2015.07.237
3. Tao W, Liu T, Zheng R et al (2012) Gait analysis using wearable sensors. Sensors (Basel) 12(2):2255–2283. https://doi.org/10.3390/s120202255
4. Paulich M, Schepers M, Rudigkeit N et al. (2018) Xsens MTw Awinda: miniature wireless inertial-magnetic motion tracker for highly accurate 3D kinematic applications. https://www.xsens.com/hubfs/3446270/Downloads/Manuals/MTwAwinda_WhitePaper.pdf
5. Chew DK, Ngoh KJH, Gouwanda D, Gopalai AA (2018) Estimating running spatial and temporal parameters using an inertial sensor. Sports Eng 21(2):115–122. https://doi.org/10.1007/s12283-017-0255-9
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献