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Model and experiments to optimize co-adaptation in a simplified myoelectric control system

Abstract : Objective. To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly di↵erent from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls. Approach. We combined a simplified myoelectric control with a perturbation for which human adaptation is well characterized and modeled, in order to explore co-adaptation settings in a principled manner. Results. First, we reproduced results obtained in a classical visuomotor rotation paradigm in our simplified myoelectric context, where we rotate the muscle pulling vectors used to reconstruct wrist force from EMG. Then, a model of human adaptation in response to directional error was used to simulate various co-adaptation settings, where perturbations and machine co-adaptation are both applied on muscle pulling vectors. These simulations established that a relatively low gain of machine co-adaptation that minimizes final errors generates slow and incomplete adaptation, while higher gains increase adaptation rate but also errors by amplifying noise. After experimental verification on real subjects, we tested a variable gain that cumulates the advantages of both, and implemented it with directionally tuned neurons similar to those used to model human adaptation. This enables machine co-adaptation to locally improve myoelectric control, and to absorb more challenging perturbations. Significance. The simplified context used here enabled to explore co-adaptation settings in both simulations and experiments, and to raise important considerations such as the need for a variable gain encoded locally. The benefits and limits of extending this approach to more complex and functional myoelectric contexts are discussed. Model and experiments to optimize co-adaptation in a simplified myoelectric control system2
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Contributor : Pierre-Yves Oudeyer <>
Submitted on : Monday, January 8, 2018 - 10:51:02 AM
Last modification on : Friday, November 20, 2020 - 5:48:03 PM

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Mathilde Couraud, Daniel Cattaert, Florent Paclet, Pierre-Yves Oudeyer, Aymar de Rugy. Model and experiments to optimize co-adaptation in a simplified myoelectric control system. Journal of Neural Engineering, IOP Publishing, 2017, pp.1-32. ⟨10.1088/1741-2552/aa87cf⟩. ⟨hal-01677222⟩

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