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利用运动学和参数信息来突出日常生活活动中上肢缺乏运动和代偿的情况。

The use of kinematic and parametric information to highlight lack of movement and compensation in the upper extremities during activities of daily living.

机构信息

School of Systems Engineering, University of Reading, Whiteknights, Reading, United Kingdom.

出版信息

Gait Posture. 2010 Mar;31(3):300-6. doi: 10.1016/j.gaitpost.2009.11.007. Epub 2010 Jan 19.

DOI:10.1016/j.gaitpost.2009.11.007
PMID:20034798
Abstract

A problem that is common to the study of upper limb kinematics and gait analysis is the translation of the evidence from kinematic measurements into easily interpretable information on the status of the patient, such as the amount of compensation or lack of motion. In this study parameters that can be helpful in the rapid and clear identification of limited wrist motion and compensation were derived from kinematic data. A group of six subjects (group A) with no hand impairment, average age 32.5 ys SD 10.7 ys, and another group of five subjects (group B), average age 34.2 ys SD 16.8 ys, having suffered from distal radius fracture were tested during a cyclic activity of daily living. The activity simulated page turning. Thorax, shoulder, elbow and wrist angles were measured during this task using a motion capture system. Corresponding angle ranges were also calculated. The active range of motion (AROM) found for Group B was generally lower than that of Group A, particularly for elbow supination and wrist movements, with wrist flexion/extension statistically smaller for group B (P=0.02). Additional parameters that took into account lack of movements at the wrist and compensation from shoulder elevation, rotation and elbow pronation/supination proved to be more useful at identifying those subjects of group B outside the normative range and can provide clinicians with a rapid and efficient tool that can shorten the analysis process and help make more informed decisions on therapeutic treatments.

摘要

上肢运动学和步态分析研究中普遍存在的一个问题是,如何将运动测量的证据转化为患者状况的易于解释的信息,例如补偿量或运动不足。在这项研究中,从运动学数据中得出了一些参数,这些参数有助于快速、清晰地识别腕部运动受限和补偿的情况。一组 6 名(A 组)无手部损伤的受试者,平均年龄 32.5 岁,标准差 10.7 岁,另一组 5 名(B 组)患有桡骨远端骨折的受试者,在日常生活的周期性活动中接受了测试。活动模拟翻页。使用运动捕捉系统测量了这项任务中的胸部、肩部、肘部和手腕角度。还计算了相应的角度范围。B 组的主动活动范围(AROM)普遍低于 A 组,尤其是肘部旋后和手腕运动,B 组的手腕屈伸统计学上更小(P=0.02)。考虑到腕部运动不足和肩部抬高、旋转以及肘部旋前/旋后的补偿的其他参数,对于确定 B 组中超出正常范围的受试者更为有用,并且可以为临床医生提供一种快速、有效的工具,缩短分析过程并帮助更明智地做出治疗决策。

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