Finite state control of functional electrical stimulation for the rehabilitation of gait

Finite state control is an established technique for the implementation of intention detection and activity co-ordination levels of hierarchical contr ol in neural prostheses, and has been used for these purposes over the last thirty years. The first finite state controllers (FSC) in the functional e lectrical stimulation of gait were manually crafted systems, based on obser vations of the events occurring during the gait cycle. Subsequent systems u sed machine learning to automatically learn finite state control behaviour directly from human experts. Recently, fuzzy control has been utilised as a n extension of finite state control, resulting in improved state defection over standard finite state control systems in some instances. Clinical expe rience over the last thirty years has been positive, and has shown finite s tate control to be an effective and intuitive method for the control of fun ctional electrical stimulation (FES) in neural prostheses. However, while f inite state controlled neural prostheses are of interest in the research co mmunity, they are not widely used outside of this setting. This is largely due to the cumbersome nature of many neural prostheses which utilise extern ally mounted gait sensors and FES electrodes. FES-based control of movement has been subject to the constraints of artificial sensor and FES actuator technologies. However, continued advances in natural sensors and implanted multi-channel stimulators are broadening the boundaries of artificial contr ol of movement driving an evolutionary process towards increasingly humanli ke control of FES-based gait rehabilitation systems.