BioKin: an ambulatory platform for gait kinematic and feature assessment

Ekanayake, Samitha W., Morris, Andrew J., Forrester, Mike and Pathirana, Pubudu N. 2015, BioKin: an ambulatory platform for gait kinematic and feature assessment, Healthcare technology letters, vol. 2, no. 1, pp. 40-45, doi: 10.1049/htl.2014.0094.

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Title BioKin: an ambulatory platform for gait kinematic and feature assessment
Author(s) Ekanayake, Samitha W.
Morris, Andrew J.
Forrester, Mike
Pathirana, Pubudu N.ORCID iD for Pathirana, Pubudu N.
Journal name Healthcare technology letters
Volume number 2
Issue number 1
Start page 40
End page 45
Total pages 6
Publisher Institution of Engineering and Technology
Place of publication Stevenage, Eng.
Publication date 2015-02
ISSN 2053-3713
Keyword(s) BioKin system
ambulatory platform
correlation coefficient
data collection
feature assessment
gait analysis
gait kinematic
image motion analysis
instrumented treadmill
limb orientation estimation algorithm
low-cost wearable wireless motion capture sensor
medical image processing
motion analysis platform
motion estimation
motion measurement
patient rehabilitation
root-mean-square error
storage engine
visualisation platform
wireless sensor networks
Summary A platform to move gait analysis, which is normally restricted to a clinical environment in a well-equipped gait laboratory, into an ambulatory system, potentially in non-clinical settings is introduced. This novel system can provide functional measurements to guide therapeutic interventions for people requiring rehabilitation with limited access to such gait laboratories. BioKin system consists of three layers: a low-cost wearable wireless motion capture sensor, data collection and storage engine, and the motion analysis and visualisation platform. Moreover, a novel limb orientation estimation algorithm is implemented in the motion analysis platform. The performance of the orientation estimation algorithm is validated against the orientation results from a commercial optical motion analysis system and an instrumented treadmill. The study results demonstrate a root-mean-square error less than 4° and a correlation coefficient more than 0.95 when compared with the industry standard system. These results indicate that the proposed motion analysis platform is a potential addition to existing gait laboratories in order to facilitate gait analysis in remote locations.
Language eng
DOI 10.1049/htl.2014.0094
Field of Research 090305 Rehabilitation Engineering
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IET
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