Machine learning in control of functional electrical stimulation for locomotion

Abstract

Two machine learning techniques were evaluated for automatic design of a rule-based control of functional electrical stimulation (FES) for locomotion of spinal cord injured humans. The task was to map the relationship between sensory information and the FES-control signal by using off-line supervised training. Signals were recorded using pressure sensors installed in insoles of a patient's shoes and goniometers attached across the joints of the affected leg. The FES-control signal consisted of pulses corresponding to time intervals when the patient pressed on the manual push-button to deliver the stimulation during FES-assisted ambulation. The machine learning techniques evaluated were the adaptive logic network (ALN) and inductive learning algorithm (IL). Results to date suggest that, given the same training data, the IL learned faster than ALN while both performed the test rapidly. The generalization was better with an ALN, especially if past points were used to reflect the time dimension. Both techniques were able to predict future stimulation events. An advantage of ALN was that it can be retrained with new data without losing previously collected knowledge. The advantages of IL were that IL produces explicit and comprehensible trees and that the relative importance of each sensory contribution can be quantified.<>