基于 RGB-Depth 相机的运动能力评估：六项标准化运动任务的常模数据。
RGB-Depth Camera-Based Assessment of Motor Capacity: Normative Data for Six Standardized Motor Tasks.
机构信息
Experimental and Clinical Research Center, a Cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité-Universitätsmedizin Berlin, 13125 Berlin, Germany.
Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13125 Berlin, Germany.
出版信息
Int J Environ Res Public Health. 2022 Dec 17;19(24):16989. doi: 10.3390/ijerph192416989.
BACKGROUND
Instrumental motion analysis constitutes a promising development in the assessment of motor function in clinical populations affected by movement disorders. To foster implementation and facilitate interpretation of respective outcomes, we aimed to establish normative data of healthy subjects for a markerless RGB-Depth camera-based motion analysis system and to illustrate their use.
METHODS
We recorded 133 healthy adults (56% female) aged 20 to 60 years with an RGB-Depth camera-based motion analysis system. Forty-three spatiotemporal parameters were extracted from six short, standardized motor tasks-including three gait tasks, stepping in place, standing-up and sitting down, and a postural control task. Associations with confounding factors, height, weight, age, and sex were modelled using a predictive linear regression approach. A z-score normalization approach was provided to improve usability of the data.
RESULTS
We reported descriptive statistics for each spatiotemporal parameter (mean, standard deviation, coefficient of variation, quartiles). Robust confounding associations emerged for step length and step width in comfortable speed gait only. Accessible normative data usage was lastly exemplified with recordings from one randomly selected individual with multiple sclerosis.
CONCLUSION
We provided normative data for an RGB depth camera-based motion analysis system covering broad aspects of motor capacity.
背景
仪器运动分析是评估运动障碍患者运动功能的一种很有前途的发展。为了促进实施并便于解释各自的结果，我们旨在为基于无标记 RGB-Depth 摄像机的运动分析系统建立健康受试者的正常数据，并举例说明其使用方法。
方法
我们使用基于 RGB-Depth 摄像机的运动分析系统记录了 133 名年龄在 20 至 60 岁之间的健康成年人（女性占 56%）。从六个简短的标准化运动任务中提取了 43 个时空参数-包括三个步态任务、原地踏步、站立和坐下，以及一个姿势控制任务。使用预测线性回归方法对与混杂因素（身高、体重、年龄和性别）的相关性进行建模。提供了 z 分数归一化方法来提高数据的可用性。
结果
我们报告了每个时空参数（平均值、标准差、变异系数、四分位数）的描述性统计数据。仅在舒适速度步态中，步长和步宽显示出稳健的混杂关联。最后，我们用一个随机选择的多发性硬化症患者的记录来说明可访问的正常数据使用情况。
结论
我们为基于 RGB 深度摄像机的运动分析系统提供了正常数据，涵盖了运动能力的广泛方面。
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