Literature DB >> 30010580

Evaluation of Myoelectric Control Learning Using Multi-Session Game-Based Training.

Aaron Tabor, Scott Bateman, Erik Scheme.   

Abstract

While training is critical for ensuring initial success as well as continued adoption of a myoelectric powered prosthesis, relatively little is known about the amount of training that is necessary. In previous studies, participants have completed only a small number of sessions, leaving doubt about whether the findings necessarily generalize to a longer-term clinical training program. Furthermore, a heavy emphasis has been placed on a functional prosthesis use when assessing the effectiveness of myoelectric training. Although well-motivated, this all-inclusive approach may obscure more subtle improvements made in underlying muscle control that could lead to tangible benefits. In this paper, a deeper exploration of the effects of myoelectric training was performed by following the progress of 30 participants as they completed a series of ten 30-min training sessions over multiple days. The progress was assessed using a newly developed set of metrics that was specifically designed to quantify the aspects of muscle control that are foundational to the strong myoelectric prosthesis use. It was determined that, while myoelectric training can lead to improvements in muscle control, these improvements may take longer than previously considered, even occurring after improvements in the training game itself. These results suggest the need to reconsider how and when transfer from training activities is assessed.

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Year:  2018        PMID: 30010580     DOI: 10.1109/TNSRE.2018.2855561

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  3 in total

1.  EMG-Force and EMG-Target Models During Force-Varying Bilateral Hand-Wrist Contraction in Able-Bodied and Limb-Absent Subjects.

Authors:  Ziling Zhu; Carlos Martinez-Luna; Jianan Li; Benjamin E McDonald; Chenyun Dai; Xinming Huang; Todd R Farrell; Edward A Clancy
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2021-01-28       Impact factor: 3.802

2.  User training for machine learning controlled upper limb prostheses: a serious game approach.

Authors:  Morten B Kristoffersen; Andreas W Franzke; Raoul M Bongers; Michael Wand; Alessio Murgia; Corry K van der Sluis
Journal:  J Neuroeng Rehabil       Date:  2021-02-12       Impact factor: 4.262

3.  Serious Games Are Not Serious Enough for Myoelectric Prosthetics.

Authors:  Christian Alexander Garske; Matthew Dyson; Sigrid Dupan; Graham Morgan; Kianoush Nazarpour
Journal:  JMIR Serious Games       Date:  2021-11-08       Impact factor: 4.143

  3 in total

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