In neuroprostheses that use functional electrical stimulation (FES) to restore motor function, closed-loop feedback control may compensate for muscle fatigue, perturbations and nonlinearities in the behavior of the effected muscles. Kinematic state information is naturally represented in the firing rates of primary afferent neurons, which may be recorded with multi-electrode arrays at the level of the dorsal root ganglia (DRG). Previous work in cats has shown that it is feasible to estimate the kinematic state of the hind limb with a multivariate linear regression model of the neural activity in the DRG. In this study we extend these results to estimate the limb state in real-time during intramuscular stimulation in an anesthetized cat. Furthermore, we used the limb state estimates as feedback to a finite state FES controller to generate rudimentary walking behavior. This work demonstrates the feasibility of using DRG activity in a closed-loop FES system.
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