Using nerve signals from muscle afferent electrodes to control FES-based ankle motion in a rabbit

Abstract

Electroneurographic (ENG) signals were extracted from muscle afferent fibers and used for real-time closed-loop control of FES-based ankle movements in a rabbit preparation. For extraction of the ENG signals, tripolar cuff electrodes were implanted onto the peroneal and tibial nerves in the left hind limb. A neural network was used for extraction of joint angles from the recorded ENGs. For stimulation purposes, percutaneous stainless steel wires were placed intramuscularly into the tibialis anterior and lateral gastrocnemius muscles, respectively. Stimulation intensity was varied by changing the applied pulse width (PW). Step and sinusoidal tracking tasks were performed using a standard PID controller. Results showed that the system's performance is highly sensitive to the initial joint angle. Further, angles estimated from the ENG (by the neural network) lost correlation with measured angles as a given experiment progressed. Improvements were seen when the neural network was allowed to learn intermittently during an experimental session. Finally, a standard PID controller required frequent retuning during an experimental session, which, not surprisingly, suggests that an adaptive controller should be used.