Objective. Unsupervised myocontrol methods aim to create control models for myoelectric prostheses while avoiding the complications of acquiring reliable, regular, and sufficient labeled training data. A limitation of current unsupervised methods is that they fix the number of controlled prosthetic functions a priori, thus requiring an initial assessment of the user's motor skills and neglecting the development of novel motor skills over time. Approach. We developed a progressive unsupervised myocontrol (PUM) paradigm in which the user and the control model coadaptively identify distinct muscle synergies, which are then used to control arbitrarily associated myocontrol functions, each corresponding to a hand or wrist movement. The interaction starts with learning a single function and the user may request additional functions after mastering the available ones, which aligns the evolution of their motor skills with an increment in system complexity. We conducted a multi-session user study to evaluate PUM and compare it against a state-of-the-art non-progressive unsupervised alternative. Two participants with congenital upper-limb differences tested PUM, while ten non-disabled control participants tested either PUM or the non-progressive baseline. All participants engaged in myoelectric control of a virtual hand and wrist. Main results. PUM enabled autonomous learning of three myocontrol functions for participants with limb differences, and of all four available functions for non-disabled subjects, using both existing or newly identified muscle synergies. Participants with limb differences achieved similar success rates to non-disabled ones on myocontrol tests, but faced greater difficulties in internalizing new motor skills and exhibited slightly inferior movement quality. The performance was comparable with either PUM or the non-progressive baseline for the group of non-disabled participants. Significance. The PUM paradigm enables users to autonomously learn to operate the myocontrol system, adapts to the users' varied preexisting motor skills, and supports the further development of those skills throughout practice.
机构:
UNSJ CONICET, Inst Automt, Av San Martin Oeste 1109,J5400ARL, San Juan, ArgentinaUNSJ CONICET, Inst Automt, Av San Martin Oeste 1109,J5400ARL, San Juan, Argentina
Orosco, Eugenio C.
di Sciascio, Fernando
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UNSJ CONICET, Inst Automt, Av San Martin Oeste 1109,J5400ARL, San Juan, ArgentinaUNSJ CONICET, Inst Automt, Av San Martin Oeste 1109,J5400ARL, San Juan, Argentina
机构:
Arizona State Univ, Sch Engn Matter Transport & Energy, Tempe, AZ 85287 USAArizona State Univ, Sch Engn Matter Transport & Energy, Tempe, AZ 85287 USA
Ison, Mark
Artemiadis, Panagiotis
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Arizona State Univ, Sch Engn Matter Transport & Energy, Tempe, AZ 85287 USAArizona State Univ, Sch Engn Matter Transport & Energy, Tempe, AZ 85287 USA
机构:
Univ Fed Rio de Janeiro, Grad Program Phys Educ, Rio De Janeiro, Brazil
Univ Fed Rio de Janeiro, Inst Neurol Deolindo Couto, Rio De Janeiro, BrazilUniv Fed Rio de Janeiro, Grad Program Phys Educ, Rio De Janeiro, Brazil
Correa, Thais Vianna
Rocha Paz, Thiago da Silva
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Univ Fed Rio de Janeiro, Grad Program Phys Educ, Rio De Janeiro, Brazil
Univ Fed Rio de Janeiro, Inst Neurol Deolindo Couto, Rio De Janeiro, BrazilUniv Fed Rio de Janeiro, Grad Program Phys Educ, Rio De Janeiro, Brazil
Rocha Paz, Thiago da Silva
Allodi, Silvana
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Univ Fed Rio de Janeiro, Grad Program Physiol, Rio De Janeiro, BrazilUniv Fed Rio de Janeiro, Grad Program Phys Educ, Rio De Janeiro, Brazil
Allodi, Silvana
Santos de Britto, Vera Lucia
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Univ Fed Rio de Janeiro, Grad Program Phys Educ, Rio De Janeiro, Brazil
Univ Fed Rio de Janeiro, Inst Neurol Deolindo Couto, Rio De Janeiro, BrazilUniv Fed Rio de Janeiro, Grad Program Phys Educ, Rio De Janeiro, Brazil
Santos de Britto, Vera Lucia
Correa, Clynton Lourenco
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Univ Fed Rio de Janeiro, Grad Program Phys Educ, Rio De Janeiro, Brazil
Univ Fed Rio de Janeiro, Inst Neurol Deolindo Couto, Rio De Janeiro, BrazilUniv Fed Rio de Janeiro, Grad Program Phys Educ, Rio De Janeiro, Brazil