Functional restoration of elbow extension after spinal-cord injury using a neural network-based synergistic FES controller.

Individuals with a C5/C6 spinal-cord injury (SCI) have paralyzed elbow extensors, yet retain weak to strong voluntary control of elbow flexion and some shoulder movements. They lack elbow extension, which is critical during activities of daily living. This research focuses on the functional evaluation of a developed synergistic controller employing remaining voluntary elbow flexor and shoulder electromyography (EMG) to control elbow extension with functional electrical stimulation (FES). Remaining voluntarily controlled upper extremity muscles were used to train an artificial neural network (ANN) to control stimulation of the paralyzed triceps. Surface EMG was collected from SCI subjects while they produced isometric endpoint force vectors of varying magnitude and direction using triceps stimulation levels predicted by a biomechanical model. ANNs were trained with the collected EMG and stimulation levels. We hypothesized that once trained and implemented in real-time, the synergistic controller would provide several functional benefits. We anticipated the synergistic controller would provide a larger range of endpoint force vectors, the ability to grade and maintain forces, the ability to complete a functional overhead reach task, and use less overall stimulation than a constant stimulation scheme.