Abstract
Access to early myoelectric training can be a crucial step in mastering prosthesis control. Controlling a prothesis is a cognitively demanding task with high rejection rates. Serious games not only provide patients with an opportunity to train their myoelectric control, but also help maintain their engagement throughout the extensive rehabilitation process. This work proposes a novel serious game design to train machine learning based myoelectric control, implemented in the form of a music-based app. The prototype of the game was evaluated by seven able-bodied participants and three clinical professionals with regard to system usability and motivation. Results showed positive outcomes in motivation, and a need for specific system usability improvements.
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Bessa, D., Rodrigues, N.F., Oliveira, E., Kolbenschlag, J., Prahm, C. (2022). Designing a Music-Based Game for Training Pattern Recognition Control of a Myoelectric Prosthesis. In: Torricelli, D., Akay, M., Pons, J.L. (eds) Converging Clinical and Engineering Research on Neurorehabilitation IV. ICNR 2020. Biosystems & Biorobotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-030-70316-5_42
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DOI: https://doi.org/10.1007/978-3-030-70316-5_42
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