Movement-Based Prosthesis Control With Angular Trajectory Is Getting Closer to Natural Arm Coordination

Background: Traditional myoelectric controls of prostheses for transhumeral amputees fail to provide intuitive coordination of the necessary degrees of freedom. Building upon promising advances in movement-based controls and computer vision, we have previously demonstrated that reconstructing the distal joints based on Artificial Neural Network (ANN) predictions, while knowing the shoulder posture and the movement goal (i.e., position and orientation of the targeted object), enables participants to position and orient an avatar hand to grasp objects scattered throughout a wide workspace with performances comparable to that of a natural arm. However, this previous control involved rapid and unintended prosthesis movements that resulted from sudden changes in the ANN predictions at each modification of the movement goal, rendering its use impractical for real-life scenarios.