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The objective of this study was to develop an integrated control system (ICS) for FES-assisted locomotion after incomplete spinal cord injury (SCI). The ICS incorporates a method for automatic generation of control rules for rule-based control. The rules are extracted from a set of sensory feedback signals and stimulation control signals recorded during FES-assisted walking controlled by a skilled therapist or the subject. The rule-generation method uses Adaptive Logic Networks (ALNs), a type of artificial neural network. The ICS provides a very efficient tool to acquire sensory and control signals, to process these signals, to train the ALNs in mapping the control function, to test the trained ALNs, and to use them for control signal generation in real-time control of the FES-assisted walking. Through experimental work it's been demonstrated that ALNs are able to generate control rules quickly and to generalize not only over daily subsequent walking sessions but also over the sessions occurring several days after the training, which provides a good basis for design of robust control systems for FES-assisted walking. Evaluation of new subjects and automatic generation of control rules using ICS is possible within minutes compared to classic 'hand-crafting' methods which usually require weeks.
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Kostov et al. (2002) studied this question.