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System training and assessment in simultaneous proportional myoelectric prosthesis control

机译:同时比例肌电假体控制中的系统训练和评估

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摘要

BackgroundudPattern recognition control of prosthetic hands take inputs from one or more myoelectric sensors and controls one or more degrees of freedom. However, most systems created allow only sequential control of one motion class at a time. Additionally, only recently have researchers demonstrated proportional myoelectric control in such systems, an option that is believed to make fine control easier for the user. Recent developments suggest improved reliability if the user follows a so-called prosthesis guided training (PGT) scheme.ududMethodsudIn this study, a system for simultaneous proportional myoelectric control has been developed for a hand prosthesis with two motor functions (hand open/close, and wrist pro-/supination). The prosthesis has been used with a prosthesis socket equivalent designed for normally-limbed subjects. An extended version of PGT was developed for use with proportional control. The control system’s performance was tested for two subjects in the Clothespin Relocation Task and the Southampton Hand Assessment Procedure (SHAP). Simultaneous proportional control was compared with three other control strategies implemented on the same prosthesis: mutex proportional control (the same system but with simultaneous control disabled), mutex on-off control, and a more traditional, sequential proportional control system with co-contractions for state switching.ududResultsudThe practical tests indicate that the simultaneous proportional control strategy and the two mutex-based pattern recognition strategies performed equally well, and superiorly to the more traditional sequential strategy according to the chosen outcome measures.ududConclusionsudThis is the first simultaneous proportional myoelectric control system demonstrated on a prosthesis affixed to the forearm of a subject. The study illustrates that PGT is a promising system training method for proportional control. Due to the limited number of subjects in this study, no definite conclusions can be drawn.
机译:背景 ud假手的模式识别控制从一个或多个肌电传感器获取输入,并控制一个或多个自由度。但是,大多数创建的系统一次只允许顺序控制一个运动类别。另外,直到最近,研究人员才在这种系统中证明了比例肌电控制,据信这种选择使用户更容易进行精细控制。最近的发展表明,如果用户遵循所谓的假体引导训练(PGT)方案,则可靠性得到改善。 ud udMethods ud在本研究中,已经开发了一种用于同时具有比例肌电控制的系统,用于具有两个运动功能(手打开/关闭和手腕向前/向后旋转)。假体已与为正常肢体对象设计的假体插座等效物一起使用。开发了PGT的扩展版本以与比例控制一起使用。该控制系统的性能在“晾衣夹重定位任务”和“南安普敦手评估程序”(SHAP)中测试了两个对象。将同时比例控制​​与在相同假体上实现的其他三种控制策略进行了比较:互斥比例控制(相同的系统,但禁用了同时控制),互斥开-关控制,以及一种更传统的,具有共同收缩的顺序比例控制系统,用于 ud udResults ud实际测试表明,同时比例控制​​策略和两个基于互斥量的模式识别策略均表现良好,根据选择的结果度量,其效果优于传统的顺序策略。 ud ud ud这是第一个同时成比例的肌电控制系统,在附着于受试者前臂的假体上展示。研究表明,PGT是一种有前途的比例控制系统训练方法。由于这项研究的对象数量有限,因此无法得出明确的结论。

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