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Real-time evaluation of a myoelectric control method for high-level upper limb amputees based on homologous leg movements

机译:基于同源腿部运动的高级上肢截肢者肌电控制方法的实时评估

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Electromyography-based gesture classification methods for control of advanced upper limb prostheses are limited either to individuals with amputations distal to the elbow or to those willing to undergo targeted muscle reinnervation surgery. Based on the natural similarity between gestures of the lower leg and the arm and on established methods in electromyography-based gesture classification, we propose a noninvasive system with which users control an upper limb prosthesis via homologous movements of the leg and foot. Eight inexperienced able-bodied subjects controlled a simulated robotic arm in a target achievement control (TAC) task with command of up to four degrees of freedom toward targets requiring one motion class. All subjects performed the task with analogous electromyography recording configurations on both the leg and the arm (as a benchmark), achieving slightly better performance with leg control overall. Only a brief demonstration of the arm-leg gesture mapping was necessary for subjects to perform the task, establishing the minimal training time required to begin using the control scheme. Our findings indicate that electromyography-based recognition of leg gestures may be a viable noninvasive prosthesis control option for high-level amputees.
机译:用于控制上肢假肢的基于肌电图的手势分类方法仅限于截肢远端的人或愿意进行有针对性的肌肉再支配手术的人。基于小腿和手臂的手势之间的自然相似性以及基于肌电图的手势分类中已建立的方法,我们提出了一种非侵入性系统,用户可以通过该系统通过腿和脚的同源运动来控制上肢假体。八名缺乏经验的健全主体在目标成就控制(TAC)任务中控制着模拟机械臂,对要求一种运动等级的目标具有高达四个自由度的命令。所有受试者均在腿部和手臂上都进行了类似的肌电图记录配置(作为基准),从而在总体上控制腿部时获得了更好的表现。对于受试者执行任务而言,只需简单地演示手臂-腿部手势映射,即可确定开始使用控制方案所需的最短训练时间。我们的研究结果表明,基于肌电图的腿部手势识别可能是高水平截肢者可行的无创假体控制选择。

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