Simultaneous control of 2DOF upper-limb prosthesis with body compensations-based control: a multiple cases study - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Neural Systems and Rehabilitation Engineering Année : 2022

Simultaneous control of 2DOF upper-limb prosthesis with body compensations-based control: a multiple cases study

Amélie Touillet
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Noël Martinet
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Jean Paysant
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Guillaume Morel
Nathanael Jarrasse

Résumé

Controlling several joints simultaneously is a common feature of natural arm movements. Robotic prostheses shall offer this possibility to their wearer. Yet, existing approaches to control a robotic upper-limb prosthesis from myoelectric interfaces do not satisfactorily respond to this need: standard methods provide sequential jointby-joint motion control only; advanced pattern recognitionbased approaches allow the control of a limited subset of synchronized multi-joint movements and remain complex to set up. In this paper, we exploit a control method of an upper-limb prosthesis based on body motion measurement called Compensations Cancellation Control (CCC). It offers a straightforward simultaneous control of the intermediate joints, namely the wrist and the elbow. Four transhumeral amputated participants performed the Refined Rolyan Clothespin Test with an experimental prosthesis alternatively running CCC and conventional joint-by-joint myoelectric control. Task performance, joint motions, body compensations and cognitive load were assessed. This experiment shows that CCC restores simultaneity between prosthetic joints while maintaining the level of performance of conventional myoelectric control (used on a daily basis by three participants), without increasing compensatory motions nor cognitive load.
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Dates et versions

hal-03846023 , version 1 (09-11-2022)

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Mathilde Legrand, Charlotte Marchand, Florian Richer, Amélie Touillet, Noël Martinet, et al.. Simultaneous control of 2DOF upper-limb prosthesis with body compensations-based control: a multiple cases study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, ⟨10.1109/TNSRE.2022.3186266⟩. ⟨hal-03846023⟩
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