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

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 different 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.

The thumb during the crimp grip.

During rock-climbing, fingers grasp holds of various shapes with high force intensities. To ideally place the fingertips on the holds, the thumb is sometimes positioned on the nail of the index finger. This allows using the thumb as an additional actuator by exerting a supplementary force in the same direction as the index, middle, ring and little fingers. This study analysed how the forces exerted by the fingers are modified by the additional action of the thumb. The results showed that the thumb increases the resultant forces exerted on the hold. It was shown that the pathology risks of the middle, ring and little fingers were not modified in this condition. The finger force sharing was totally re-organized due to the support of the thumb. This led to the conclusion that the central nervous system organised the association of the 5 fingers. The results were discussed in regard to the established theories of the virtual fingers and the neutral line of the hand.