Motor Learning for Control of Prosthetic Limbs

Agamemnon Krasoulis, Matthew Dyson, Sigrid Dupan, Kianoush Nazarpour

Research output: Contribution to conferencePaperpeer-review


In this paper we present results from five real-time myoelectric control experiments with able-bodied and amputee participants comprising three distinct paradigms, namely, two-dimensional cursor position control on a screen; classification-based grasp control of a prosthetic hand; and prosthetic control of individual fingers. We observe a consistent trend across this spectrum of myoelectric interfaces, that is, regardless of the level of intuitiveness of the interface, users can improve their performance in biofeedback myoelectric control tasks with training. We argue that progress in prosthetic limb control can be best achieved through a strong synergy of signal processing algorithms, motor learning, and feedback.
Original languageEnglish
Publication statusPublished - 20 Mar 2019
Event9th International IEEE EMBS Conference on Neural Engineering - San Francisco, United States
Duration: 20 Mar 201923 Mar 2019


Conference9th International IEEE EMBS Conference on Neural Engineering
Abbreviated titleNER 2019
Country/TerritoryUnited States
CitySan Francisco
Internet address


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