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Digital Analysis of Sit-to-Stand in Masters Athletes, Healthy Old People, and Young Adults Using a Depth Sensor

1
King’s Centre for Military Health Research, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London WC2R 2LS, UK
2
School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M15 6BH, UK
*
Author to whom correspondence should be addressed.
Healthcare 2018, 6(1), 21; https://doi.org/10.3390/healthcare6010021
Received: 8 January 2018 / Revised: 12 February 2018 / Accepted: 28 February 2018 / Published: 2 March 2018
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Abstract

The aim of this study was to compare the performance between young adults (n = 15), healthy old people (n = 10), and masters athletes (n = 15) using a depth sensor and automated digital assessment framework. Participants were asked to complete a clinically validated assessment of the sit-to-stand technique (five repetitions), which was recorded using a depth sensor. A feature encoding and evaluation framework to assess balance, core, and limb performance using time- and speed-related measurements was applied to markerless motion capture data. The associations between the measurements and participant groups were examined and used to evaluate the assessment framework suitability. The proposed framework could identify phases of sit-to-stand, stability, transition style, and performance between participant groups with a high degree of accuracy. In summary, we found that a depth sensor coupled with the proposed framework could identify performance subtleties between groups. View Full-Text
Keywords: kinect; depth sensor; motion capture; sit-to-stand; automated assessment; short physical performance battery kinect; depth sensor; motion capture; sit-to-stand; automated assessment; short physical performance battery
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Leightley, D.; Yap, M.H. Digital Analysis of Sit-to-Stand in Masters Athletes, Healthy Old People, and Young Adults Using a Depth Sensor. Healthcare 2018, 6, 21.

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