NDLI: Repeatability of an off-the-shelf, full body inertial motion capture system during clinical gait analysis

Content Provider	IEEE Xplore Digital Library
Author	Cloete, T. Scheffer, C.
Copyright Year	2010
Description	Author affiliation: Biomedical Engineering Research Group, Department of Mechanical and Mechatronic Engineering, Stellenbosch University, South Africa (Cloete, T.; Scheffer, C.)
Abstract	Background and aims: To establish gait analysis as part of routine clinical diagnoses, physicians demand accurate and flexible motion capture (Mocap). Currently, popular optical, mechanical and acoustic Mocap systems offer acceptable repeatability, but fall short of the spatial and economic benefits accompanying inertial motion capture (IMC). However, IMC is considered adolescent due to limited testing in gait analysis. This paper aims to address this hindrance through within-day and between test day repeatability studies. Methods: To determine the repeatability of IMC, routine gait studies were done on 30 able-bodied males. Repeatability was quantified using the coefficient of multiple determination (CMD) and the coefficient of multiple correlation (CMC). Results: IMC-recorded kinematics were highly repeatable for within-day (CMD: 0.786–0.984 or CMC: 0.881–0.992) and between-day trials (CMD: 0.771–0.991 or CMC: 0.872–0.995). The results compare well to those from similar repeatability studies in the literature based on optical Mocap systems.
Starting Page	5125
Ending Page	5128
File Size	1523159
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5626196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Joints Magnetic sensors Kinematics Knee Humans Sensor systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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