Implementation of Machine Learning for Classifying Hemiplegic Gait Disparity through Use of a Force Plate

Abstract

The synergy of gait analysis tools with machine learning enables the capacity to classify disparity existing in hemiplegic gait. Hemiplegic gait is characterized by an affected leg and unaffected leg, which can be quantified by the measurement of a force plate. The characteristic features of the force plate recording for gait consist of a two local maxima that represent the braking phase and push off phase of stance and their associated parameters. The quantified features of a hemiplegic pair of affected leg and unaffected leg force plate recordings are intuitively disparate. Logistic regression achieves 100% classification between an affected and unaffected hemiplegic leg pair based on the feature set of the force plate data.