While natural movements result from fluid coordination of multiple joints, commercial upper-limb prostheses are still limited to sequential control of multiple degrees of freedom (DoFs), or constrained to move along predefined patterns. To control multiple DoFs simultaneously, a probability-weighted regression (PWR) method has been proposed and has previously shown good performance with intramuscular electromyographic (EMG) sensors. This study aims to evaluate the PWR method for the simultaneous and proportional control of multiple DoFs using surface EMG sensors and compare the performance with a classical direct control strategy. To extract the maximum number of DoFs manageable by a user, a first analysis was conducted in a virtually simulated environment with eight able-bodied and four amputee subjects. Results show that, while using surface EMG degraded the PWR performance for the 3-DoFs control, the algorithm demonstrated excellent achievements in the 2-DoFs case. Finally, the two methods were compared on a physical experiment with amputee subjects using a hand-wrist prosthesis composed of the SoftHand Pro and the RIC Wrist Flexor. Results show comparable outcomes between the two controllers but a significantly higher wrist activation time for the PWR method, suggesting this novel method as a viable direction towards a more natural control of multi-DoFs.

中文翻译：

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多自由度Trans骨假体同时肌电控制策略的评估

尽管自然运动是由多个关节的流体协调引起的，但是商用上肢​​假体仍然仅限于对多个自由度（DoF）的顺序控制，或者只能沿着预定的模式运动。为了同时控制多个DoF，已经提出了一种概率加权回归（PWR）方法，该方法以前在肌内肌电图（EMG）传感器中显示出良好的性能。这项研究旨在评估使用表面肌电传感器同时控制多个自由度的PWR方法，并将其性能与经典的直接控制策略进行比较。为了提取用户可管理的最大数量的DoF，在具有8个身体健全和4个截肢者的虚拟环境中进行了首次分析。结果表明，虽然使用表面肌电图会降低3-DoFs控制的PWR性能，但该算法在2-DoFs情况下表现出优异的性能。最后，在由被截肢者进行的物理实验中，使用由SoftHand Pro和RIC Wrist Flexor组成的手腕假体对这两种方法进行了比较。结果表明两个控制器之间的结果可比，但是PWR方法的腕部激活时间明显更长，这表明该新颖方法是朝着更自然地控制多自由度的可行方向。