首页> 外文期刊>Neurorehabilitation and neural repair >Assessment of upper-limb sensorimotor function of subacute stroke patients using visually guided reaching.
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Assessment of upper-limb sensorimotor function of subacute stroke patients using visually guided reaching.

机译:使用视觉引导到达评估亚急性中风患者的上肢感觉运动功能。

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OBJECTIVE: Using robotic technology, we examined the ability of a visually guided reaching task to assess the sensorimotor function of patients with stroke. METHODS: Ninety-one healthy participants and 52 with subacute stroke of mild to moderate severity (26 with left- and 26 with right-affected body sides) performed an unassisted reaching task using the KINARM robot. Each participant was assessed using 12 movement parameters that were grouped into 5 attributes of sensorimotor control. RESULTS: A number of movement parameters individually identified a large number of stroke participants as being different from 95% of the controls-most notably initial direction error, which identified 81% of left-affected patients. We also found interlimb differences in performance between the arms of those with stroke compared with controls. For example, whereas only 31% of left-affected participants showed differences in reaction time with their affected arm, 54% showed abnormal interlimb differences in reaction time. Good interrater reliability (r > 0.7) was observed for 9 of the 12 movement parameters. Finally, many stroke patients deemed impaired on the reaching task had been scored 6 or less on the arm portion of the Chedoke-McMaster Stroke Assessment Scale, but some who scored a normal 7 were also deemed impaired in reaching. CONCLUSIONS: Robotic technology using a visually guided reaching task can provide reliable information with greater sensitivity about a patient's sensorimotor impairments following stroke than a standard clinical assessment scale.
机译:目的:使用机器人技术,我们检查了视觉引导下到达任务评估中风患者感觉运动功能的能力。方法:九十一名健康参与者和52名轻度至中度亚急性卒中患者(26例左侧患侧,26例右侧患侧)使用KINARM机器人进行了无辅助的伸手任务。使用12个运动参数对每个参与者进行评估,这些运动参数被分为5个感觉运动控制属性。结果:许多运动参数分别识别出大量卒中参与者不同于95%的对照者-最值得注意的是初始方向错误,后者识别了81%的左患患者。我们还发现与对照组相比,卒中患者的手臂之间的肢体表现存在差异。例如,虽然只有31%的左侧受影响参与者显示出其患病手臂的反应时间不同,但54%显示出异常的肢体反应时间差异。对于12个运动参数中的9个,观察到了良好的间隔可靠性(r> 0.7)。最后,在Chedoke-McMaster中风评估量表的手臂部分,许多被认为在到达任务上受损的中风患者的得分为6分或以下,但也有一些得分在正常范围内的得分为7分。结论:与标准临床评估量表相比,使用视觉引导下到达任务的机器人技术可以提供可靠的信息,并且对中风后患者的感觉运动障碍的敏感性更高。

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