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一种新型机器人系统,用于量化手臂运动学和动力学:在治疗师辅助下脑卒中后被动手臂运动中的描述和评估。

A novel robotic system for quantifying arm kinematics and kinetics: description and evaluation in therapist-assisted passive arm movements post-stroke.

机构信息

School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, West Yorks, UK.

出版信息

J Neurosci Methods. 2011 Apr 30;197(2):259-69. doi: 10.1016/j.jneumeth.2011.03.004. Epub 2011 Mar 21.

Abstract

We developed a system for quantitatively measuring arm movement. Our approach provides a method to simultaneously capture upper limb kinetic and kinematic data during assisted passive arm movements. Data are analysed with respect to Cartesian and upper limb coordinate systems to obtain upper limb joint angles and torques. We undertook an evaluation of the system in participants with stroke to show the feasibility of this approach. During rehabilitation after stroke, one aspect of treatment includes the physiotherapist applying assistive forces to move the impaired arm of the patient who remains passive. There is a dearth of published data on the relationship between upper limb kinematics and the underlying forces (kinetics) in this mode of physiotherapy treatment. Such quantitative data are crucial in facilitating research into therapy practice, for example by measuring variation in practice and determining dosage. An experienced therapist prescribed passive movements tailored to the needs of 16 participants with stroke (41-81 years) with a range of anthropometric sizes and motor impairments. Our novel measurement tool recorded kinematic and kinetic data at 100 Hz for 6-11 movements per participant. The kinetic data show that the majority of movements fall within upper limits of 36.7 N in shoulder elevation, 22.4N in shoulder protraction, 4.6 Nm in shoulder abduction, 12.8 Nm in shoulder flexion, 2.4 Nm in shoulder rotation and 5.5 Nm in elbow flexion. These data show the potential of this system to better understand arm movement, in particular to objectively evaluate physical therapy treatments and support development of robotic devices to facilitate upper limb rehabilitation.

摘要

我们开发了一种定量测量手臂运动的系统。我们的方法提供了一种在辅助被动手臂运动中同时捕获上肢运动学和运动学数据的方法。数据是相对于笛卡尔坐标系和上肢坐标系进行分析的,以获得上肢关节角度和扭矩。我们在中风患者中评估了该系统,以证明这种方法的可行性。中风后康复期间,治疗的一个方面包括治疗师施加辅助力来移动患者的受损手臂,而患者保持被动。在这种物理治疗治疗模式下,上肢运动学与基础力(运动学)之间的关系缺乏已发表的数据。这种定量数据对于促进治疗实践的研究至关重要,例如通过测量实践中的变化和确定剂量。一位经验丰富的治疗师根据 16 名中风患者(41-81 岁)的需求,为每位患者量身定制了被动运动,这些患者的体型和运动障碍各不相同。我们的新型测量工具以 100Hz 的频率记录了 6-11 次运动的运动学和动力学数据。动力学数据显示,大多数运动的肩部抬高力在 36.7N 以内,肩部伸展力在 22.4N 以内,肩部外展力在 4.6Nm 以内,肩部弯曲力在 12.8Nm 以内,肩部旋转力在 2.4Nm 以内,肘部弯曲力在 5.5Nm 以内。这些数据表明该系统有潜力更好地理解手臂运动,特别是客观评估物理治疗治疗,并支持开发机器人设备以促进上肢康复。

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