Validation of an ambient system for the measurement of gait parameters
- Dubois, Amandine Department of Medicine, University of Fribourg, Fribourg, Switzerland
- Bresciani, Jean-Pierre Department of Medicine, University of Fribourg, Fribourg, Switzerland
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01.03.2018
Published in:
- Journal of Biomechanics. - 2018, vol. 69, p. 175–180
English
Fall risk in elderly people is usually assessed using clinical tests. These tests consist in a subjective evaluation of gait performed by healthcare professionals, most of the time shortly after the first fall occurrence. We propose to complement this one-time, subjective evaluation, by a more quantitative analysis of the gait pattern using a Microsoft Kinect. To evaluate the potential of the Kinect sensor for such a quantitative gait analysis, we benchmarked its performance against that of a gold-standard motion capture system, namely the OptiTrack. The “Kinect” analysis relied on a home-made algorithm specifically developed for this sensor, whereas the OptiTrack analysis relied on the “built-in” OptiTrack algorithm. We measured different gait parameters as step length, step duration, cadence, and gait speed in twenty-five subjects, and compared the results respectively provided by the Kinect and OptiTrack systems. These comparisons were performed using Bland-Altman plot (95% bias and limits of agreement), percentage error, Spearman’s correlation coefficient, concordance correlation coefficient and intra-class correlation. The agreement between the measurements made with the two motion capture systems was very high, demonstrating that associated with the right algorithm, the Kinect is a very reliable and valuable tool to analyze gait. Importantly, the measured spatio-temporal parameters varied significantly between age groups, step length and gait speed proving the most effective discriminating parameters. Kinect-monitoring and quantitative gait pattern analysis could therefore be routinely used to complete subjective clinical evaluation in order to improve fall risk assessment during rehabilitation.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département de Médecine
- Language
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- English
- Classification
- Biological sciences
- License
- License undefined
- Identifiers
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- RERO DOC 309039
- DOI 10.1016/j.jbiomech.2018.01.024
- Persistent URL
- https://folia.unifr.ch/unifr/documents/306486
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