Finite state control is an established technique for the implementation of intention detection and activity co-ordination levels of hierarchical control in neural prostheses, and has been used for these purposes over the last thirty years. The first finite state controllers (FSC) in the functional electrical stimulation of gait were manually crafted systems, based on observations of the events occurring during the gait cycle. Subsequent systems used machine learning to automatically learn finite state control behaviour directly from human experts. Recently, fuzzy control has been utilised as an extension of finite state control, resulting in improved state detection over standard finite state control systems in some instances. Clinical experience over the last thirty years has been positive, and has shown finite state control to be an effective and intuitive method for the control of functional electrical stimulation (FES) in neural prostheses. However, while finite state controlled neural prostheses are of interest in the research community, they are not widely used outside of this setting. This is largely due to the cumbersome nature of many neural prostheses which utilise externally 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 control of movement, driving an evolutionary process towards increasingly human-like control of FES-based gait rehabilitation systems.
- Functional electrical stimulation
- Finite state control
- Gait rehabilitation