Evaluation of Adaptive Logic Networks for control of walking in paralyzed patients

An Adaptive Logic Network (ALN), a type of Neural Network (NN), was evaluated for the control of walking in Spinal Cord Injured (SCI) patients. The motivation behind this research was to explore more reliable methods for control of simple Functional Neuromuscular Stimulation (FNS) systems in incomplete SCI patients. The ALN was used to recognize a patient\´s intention to make a step by stimulating muscles in a partially paralyzed leg. Signals from four force sensors, installed under the toes and heels, have been used as inputs and a gating pulse associated with the stimulation as an output for learning and testing the function of the control system. Manual control, by either the patient or a physio therapist, has been used as a template to be matched by the ALN. Generalization of the learned functions by the ALN to previously unseen data was also tested. Finally, we manipulated the number of input channels, the inclusion of information from past samples and prediction of future events. The ALN is capable of generating the same time series of output pulses as those generated by "human experts." Furthermore, it can predict the stimulation event early enough so that the requirement for stimulation can be verified and the patient informed to prepare for stimulation.