{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Piazza C"],"funding":["NICHD NIH HHS","Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institute of Health","ERC Programme","European Union through the Horizon 2020 Research and Innovation Program"],"pagination":["2286-2295"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9028175"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["28(10)"],"pubmed_abstract":["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."],"journal":["IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society"],"pubmed_title":["Evaluation of a Simultaneous Myoelectric Control Strategy for a Multi-DoF Transradial Prosthesis."],"pmcid":["PMC9028175"],"funding_grant_id":["810346 (Natural Bionics)","688857 (SoftPro)","R01 HD094861","5R01HD094861-02"],"pubmed_authors":["Catalano MG","Hargrove LJ","Bicchi A","Rossi M","Piazza C"],"additional_accession":[]},"is_claimable":false,"name":"Evaluation of a Simultaneous Myoelectric Control Strategy for a Multi-DoF Transradial Prosthesis.","description":"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.","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Oct","modification":"2025-05-18T12:06:58.199Z","creation":"2025-04-05T21:34:43.025Z"},"accession":"S-EPMC9028175","cross_references":{"pubmed":["32804650"],"doi":["10.1109/TNSRE.2020.3016909","10.1109/tnsre.2020.3016909"]}}