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通过质心加速度得出的稳定区域能更好地识别起身站立动作有困难的个体。

Region of stability derived by center of mass acceleration better identifies individuals with difficulty in sit-to-stand movement.

作者信息

Fujimoto Masahiro, Chou Li-Shan

机构信息

Department of Human Physiology, 1240 University of Oregon, Eugene, OR, 97403, USA.

出版信息

Ann Biomed Eng. 2014 Apr;42(4):733-41. doi: 10.1007/s10439-013-0945-9. Epub 2013 Nov 21.

DOI:10.1007/s10439-013-0945-9
PMID:24259008
Abstract

Poor performance of sit-to-stand (STS) has been identified as one of the predictors of fall risk among elderly adults. This study examined differences in the whole body center of mass (COM) kinematic variables in relation to the regions of stability between elderly adults with difficulty in STS and healthy individuals. Whole body motion data while performing STS were collected from 10 young, 10 elderly and 10 elderly subjects with difficulty in STS. Young subjects were also asked to stand up with their trunk purposely bent forward. The regions of stability were defined with COM position at seat-off and its instantaneous velocity (ROSv) or peak acceleration (ROSa), using a single-link-plus-foot inverted pendulum model. Peak COM accelerations prior to seat-off differed significantly among groups; however, no significant differences were detected in its velocities at seat-off. The ROSa demonstrated a better ability to discriminate elderly adults with difficulty from healthy individuals. Although a similar COM momentum was observed at seat-off, how the momentum was controlled differed between healthy individuals and individuals with difficulty in STS. ROSa could provide insight into how the COM momentum is controlled prior to seat-off, which could be used to differentiate individuals with functional limitations from healthy individuals.

摘要

从坐到站(STS)表现不佳已被确定为老年人跌倒风险的预测因素之一。本研究调查了在STS方面有困难的老年人与健康个体之间,全身质心(COM)运动学变量与稳定区域的差异。从10名年轻人、10名老年人和10名在STS方面有困难的老年受试者收集了进行STS时的全身运动数据。还要求年轻受试者故意弯腰站立起来。使用单连杆加足部倒立摆模型,根据离座时的COM位置及其瞬时速度(ROSv)或峰值加速度(ROSa)来定义稳定区域。离座前的COM峰值加速度在各组之间有显著差异;然而,离座时其速度没有检测到显著差异。ROSa显示出更好的区分有困难的老年人与健康个体的能力。尽管在离座时观察到了类似的COM动量,但健康个体和在STS方面有困难的个体之间控制动量的方式有所不同。ROSa可以深入了解离座前COM动量是如何控制的,这可用于区分有功能限制的个体与健康个体。

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