Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching
Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching
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DOI：
10.1177/0309364615605373
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发表时间：
2016-10-01
影响因子：
1.5
通讯作者：
Pilarski, Patrick M.
中科院分区：
文献类型：
--
作者：
Edwards, Ann L.;Dawson, Michael R.;Pilarski, Patrick M.
背景：截肢者目前使用的肌电假体可能很难控制。机器学习，特别是对用户意图的学习预测，可以帮助截肢者在操作假肢时减少所需的时间和认知负荷。目的：本研究的目标是比较两种基于开关的肌电手臂控制方法：非自适应(或常规)控制和自适应控制(包括实时预测学习)。研究设计：案例系列研究。方法：我们在两个不同的实验中比较了非自适应控制和自适应控制。在第一组中，一名截肢者和一名非截肢者控制机械臂执行简单的任务；在第二组中，三名健全的受试者控制机械臂执行更复杂的任务。结果：与传统控制方法相比，自适应控制显著减少了两种任务的切换次数和总切换时间。结论：实时预测学习被成功地用于改善截肢者和健全人在不间断使用时的肌电机械臂的控制界面。基于实时预测学习的临床相关性自适应控制有助于减少截肢者在使用肌电假体时所需的时间和认知负荷。
Background: Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device.Objectives: The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning).Study design: Case series study.Methods: We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials.Results: Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method.Conclusion: Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects.Clinical relevance Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses.