Recognition of Lower Limb Movements by Artificial Neural Network for Restoring Gait of Hemiplegic Patients by Functional Electrical Stimulation.

摘要

This study focused on the man-machine interface of functional electrical stimulation (FES) systems for restoring the gait of hemiplegic patients. A method of recognition of lower limb movements using an artificial neural network (ANN) was examined in monitoring restored motions and in giving control command with 5 neurologically intact males, 22 to 24 years of age, and a female hemiplegic patient who was 55 years old. Acceleration signals were measured with a three-axis accelerometer attached to the heel of the normal side (right side) during walking. Subjects performed specific movements with their normal lower limbs supposing control command input in the walking measurements. The ANN recognized three different walking patterns: level floor walking, going up stairs, and going down stairs. Recognition was based on acceleration waveforms with an 80% recognition rate for normal subjects and above 70% for the patient. A similar structure of the ANN discriminated four specific movements by the lower extremity with a more than 90% recognition rate after the third performance of the movement simulated by using recognition and misrecognition rates for experimentally measured data. The method was found to be useful in monitoring FES movements and in giving control commands to the FES system without using upper limbs. The technique is expected to provide information for assuring the safety of patients and to improve operationality of the FES system for practical use. (3 tables, 3 figures, 3 refs.).