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

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.