In the case of a hand amputation, the affected person can use a myoelectric prosthesis to substitute the missing limb and regain motor functions. Unfortunately, commercial methods for myoelectric control, although robust and simple, are unintuitive and cognitively taxing when applied to an advanced multi-functional prosthesis. The state-of-the-art methods developed in academia are based on machine learning and therefore require long training and suffer from a lack of robustness. This work presents a novel closed-loop multi-level amplitude controller (CMAC), which aims at overcoming these drawbacks. The CMAC implements three degrees-of-freedom (DoF) control by thresholding the muscle contraction intensity during wrist flexion and extension movements. Unique features of the controller are the vibrotactile feedback that communicates the state of the controller to the user and a scheme for proportional control. These components allow exploiting the full dexterity of the prosthesis using a simple two-channel myoelectric interface. The CMAC was compared to a commonly implemented pattern-recognition method (linear discriminant analysis – LDA) using clinically relevant tests in 12 able-bodied and 2 amputee subjects. The experimental assessment demonstrated that CMAC was similarly fast as LDA in dexterous tests (clothespin and cube manipulation), while it was somewhat slower than LDA during a simple, single DoF task (box and blocks). In addition, in all the tasks, LDA and CMAC resulted in a similarly low error rate. On the other hand, to an amputee that could not generate six distinguishable classes using LDA, the CMAC still enabled the control of all the prosthesis DoFs. Importantly, the overall setup and training time in CMAC were significantly lower compared to LDA. In conclusion, the novel method is convenient for clinical applications, and allows substantially higher control dexterity compared to what can be normally achieved using conventional two channel EMG. Therefore, CMAC provides performance comparable to advanced machine-learning algorithms and the robustness and ease of use that is characteristic for the simple two-channel myoelectric interface.

中文翻译：

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闭环多振幅控制，实现肌电假体的鲁棒性和灵巧性

在手截肢的情况下，受影响的人可以使用肌电假体来代替失去的肢体并恢复运动功能。不幸的是，用于肌电控制的商业方法虽然健壮和简单，但是当应用于先进的多功能假体时却不直观且认知繁重。学术界开发的最新方法是基于机器学习的，因此需要长期培训，并且缺乏鲁棒性。这项工作提出了一种新颖的闭环多级幅度控制器（CMAC），旨在克服这些缺点。CMAC通过限制腕部屈伸运动中的肌肉收缩强度来实现三个自由度（DoF）控制。控制器的独特功能是振动触觉反馈，该反馈将控制器的状态传达给用户以及比例控制方案。这些组件允许使用简单的两通道肌电接口来充分利用假体的灵活性。使用临床相关测试，将CMAC与12种身体健全和2名被截肢者的临床相关测试与常用的模式识别方法（线性判别分析– LDA）进行了比较。实验评估表明，在灵巧测试（衣夹和立方体操作）中，CMAC的速度与LDA相似，而在简单的单个DoF任务（方框和方块）中，CMAC的速度却比LDA慢。此外，在所有任务中，LDA和CMAC导致的错误率同样较低。另一方面，对于无法使用LDA生成六种可区分类别的截肢者，CMAC仍然可以控制所有假体DoF。重要的是，与LDA相比，CMAC的总体设置和培训时间明显更低。总之，与使用常规的两通道肌电图通常可以实现的方法相比，该新方法便于临床应用，并允许更高的控制灵活性。因此，CMAC提供的性能可与高级机器学习算法相媲美，而鲁棒性和易用性是简单的两通道肌电接口所特有的。与使用常规的两通道肌电图通常可以实现的控制方法相比，该新方法便于临床应用，并允许更高的控制灵活性。因此，CMAC提供的性能可与高级机器学习算法相媲美，而鲁棒性和易用性是简单的两通道肌电接口所特有的。与使用常规的两通道肌电图通常可以实现的控制方法相比，该新方法便于临床应用，并允许更高的控制灵活性。因此，CMAC提供的性能可与高级机器学习算法相媲美，而鲁棒性和易用性是简单的两通道肌电接口所特有的。