A non-invasive and intuitive command source for upper limb prostheses

C5/C6 tetraplegic patients and transhumeral amputees may be able to use voluntary shoulder motion as command signals for a functional electrical stimulation (FES) system or a transhumeral prosthesis. Such prostheses require the control of endpoint position in three-dimensions, hand orientation, and grasp. Stereotyped relationships, termed "postural synergies," exist between the shoulder, forearm, and wrist joints emerge during goal-oriented reaching and transport movements as performed by able bodied subjects. Thus, the posture of the shoulder can potentially be used to infer the posture of the elbow and forearm joints during reaching and transport movements. To fit these synergies we utilized three-layer artificial neural networks (ANNs). In contrast to previous work in this field, we initially trained ANNs with three rotational angles at the shoulder to predict the elbow angle during reaches in a horizontal plane. We found that the ANNs could predict elbow angle remarkably well across the entire horizontal workspace during offline and online analysis. In the subsequent works, we extended this paradigm to include shoulder translation movements in addition the shoulder rotational angles to predict forearm angle and to control grasping in 3D extrapersonal space.;The complete inferential command system (ICS) was deployed for use in a virtual reality reach and grasp task. In order to examine whether the ANNs generalized across subjects, we alternated the use of ANNs trained on the subject's own data and ANNs trained with a novel subject's data. Furthermore, we compared the performance of subjects using the ICS with subjects operating the simulated prosthesis in virtual reality according to complete motion tracking of their actual arm and hand movements. Subjects using the ICS were able to complete the task at a high percentage and with low spatial variability across the workspace. Mean task completion times of subjects using the ICS compared favorably to subjects using full motion capture regardless of which set of ANNs were used. Inferring the desired movement of distal joints from voluntary shoulder movements appears to be a relatively simple, intuitive and non-invasive approach to obtaining command signals for prostheses to restore distal arm and hand function.