A 3-DoF Wrist Control Based on Natural Arm Movements Outperforms Current Myoelectric Prosthesis in VR

Bardisbanian L, Leconte V, Doat E, Klotz R, de Rugy A.

While mechatronics is progressing to overcome poor wrist capabilities of most
current prosthetic devices, an efficient control system for a full 3
degrees-of-freedom (DoF) wrist is still lacking. We showed recently that novel
controls based on Artificial Neural Network (ANN) trained on natural arm
movements can predict multiple distal joints so well that participants with a
transhumeral arm amputation could use them to reach objects as well as with a
natural arm in virtual reality (VR). Here, we adapted this control to the case
of transradial amputation, included important changes necessary for real-life
applications, and compared it to current myoelectric control on two functional
tasks (pick-and-place and clothespin relocation) performed in VR. When
mechanical constraints of typical actual prostheses were simulated on
participants without upper limb loss using a wrist brace (Exp1, n= 20), success
rates and movement times were only slightly degraded, but this was at the
expense of large compensatory movements. When our 3-DoF wrist control was
applied, good performances were maintained together with a dramatic reduction of
those large compensatory movements. Participants with a transradial amputation
(Exp2, n= 8) had much lower performances with their prosthesis than with their
intact arm, and benefited markedly from our wrist 3-DoF control both in terms of
improved performances and reduced compensatory movements. These results
demonstrate that the proposed movement-based 3-DoF wrist control outperforms
current myoelectric prostheses in VR. This motivates further efforts needed
toward the application to a real prosthesis.The system and controls are
illustrated in the accompanying video at https://youtu.be/e9UMVN2kaSI.

DOI: 10.1109/TNSRE.2026.3689147
PMID: 42060416 [Indexed for MEDLINE]

Auteurs Bordeaux Neurocampus