Leightley Daniel, Yap Moi Hoon
King's Centre for Military Health Research, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London WC2R 2LS, UK.
School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M15 6BH, UK.
Healthcare (Basel). 2018 Mar 2;6(1):21. doi: 10.3390/healthcare6010021.
The aim of this study was to compare the performance between young adults ( = 15), healthy old people ( = 10), and masters athletes ( = 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.
本研究的目的是使用深度传感器和自动数字评估框架，比较年轻人（n = 15）、健康老年人（n = 10）和大师级运动员（n = 15）之间的表现。参与者被要求完成一项经过临床验证的坐立技术评估（五次重复），该评估使用深度传感器进行记录。一个使用与时间和速度相关的测量来评估平衡、核心和肢体表现的特征编码和评估框架被应用于无标记运动捕捉数据。研究了测量结果与参与者组之间的关联，并用于评估评估框架的适用性。所提出的框架能够高度准确地识别坐立阶段、稳定性、过渡方式以及参与者组之间的表现。总之，我们发现深度传感器与所提出的框架相结合能够识别组间表现的细微差别。
