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.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028175PMC
http://dx.doi.org/10.1109/TNSRE.2020.3016909DOI Listing

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