Reanimation of movement using probabilistic control of muscle stimulation
Andrew J. Fuglevand, PhD
Professor, Departments of Physiology and Neuroscience
College of Medicine
University of Arizona, Tucson USA
Host: T. Richard Nichols, PhD
Time: noon - 1:00pm
Location: Applied Physiology Building (555 14th Street NW), Room 1253
Abstract:
Functional electrical stimulation (FES) involves artificial activation of muscles with surface or implanted electrodes to restore motor function in paralyzed individuals. The range of motor behaviors that can be generated by FES, however, is limited to a small set of preprogrammed movements such as hand grasp and release. A broader range of movements has not been implemented because of the substantial difficulty associated with identifying the patterns of muscle stimulation needed to elicit specified movements. In order to overcome this limitation in controlling FES systems, we have used different forms of probability-based models (e.g., Bayesian density estimation, dynamic neural networks) to estimate patterns of muscle activity in human and non-human primates during a wide range of free movements of the upper limb. In addition, we have developed a generalized transfer function to convert predicted levels of muscle activity into appropriate patterns of electrical stimulation. Complex movements generated by probabilistic-controlled FES showed good correspondence to desired trajectories. Therefore, this approach should provide a flexible means to control FES and thereby expand the repertoire of motor functions available to paralyzed individuals.