| Functional Electrical Stimulation (FES) has been used to restore upper extremity function in individuals with C5/C6 level of spinal cord injury (SCI). Neuroprostheses for this SCI population typically restore hand grasp. In their shoulder and elbow, these individuals have a combination of voluntary, denervated and paralyzed muscles that reduces their workspace and forces them to adopt non-natural kinematic strategies. Controlling these FES systems and integrating them seamlessly with the remaining function is still challenging. This project explored the use of electromyographic signals (EMG) recorded from muscles that remain under voluntary control to automatically stimulate paralyzed muscles in the shoulder and elbow; restoring proximal arm function in a more natural manner; interacting synergistically with the remaining function; and complementing the hand grasp function provided by the current systems. A musculoskeletal model of the shoulder and elbow was used to select an optimal set of muscles for stimulation. The model was also used to generate the patterns of activation required to restore high level reaching function. We demonstrated that a neural network controller could be trained to predict activations for the paralyzed muscles using voluntary muscle activations as inputs. The controller was then implemented in one human subject, where his recorded EMG signals were used to train it. The implemented strategy showed that it is possible to restore reaching function, controlling the stimulation automatically. Furthermore, the intervention proved useful in increasing the range of motion of the arm and improving overall shoulder stability. |
