Novel functional electrical stimulation parameter optimization for neurorehabilitation using both conventional and AI techniques

Neurological conditions such as stroke or spinal cord trauma often attenuate or disrupt nerve connections, leading to loss of muscle function, sensation, or responsiveness. The application of physical and occupational therapy rehabilitation protocols can help regain some of the lost functions and significantly improve a patient’s quality of life. These protocols leverage the principle of neuroplasticity, an inherent property of the brain which allows the formation of new neural connections in response to external stimuli. Electrical Muscle Stimulation (EMS) has been proven to amplify the effects of rehabilitation as it adds new stimuli in the form of suitable electric pulse trains directly to the neuromuscular system. Certain rehabilitation protocols incorporate functional exercises which mimic natural movements, which can in turn benefit by the application of synchronized electric pulses. This process, known as Functional Electrical Stimulation (FES), has been demonstrated to be beneficial with respect to the nature and longevity of neuromuscular adaptations as well as brain reorganization. This paper considers techniques for the optimization of these parameters and presents preliminary in vivo experimental results demonstrating the proposed methodology.