Classification of Parkinson's disease gait using spatial-temporal gait features

Abstract

Quantitative gait assessment is important in diagnosis and management of Parkinson's disease (PD); however, gait characteristicsofacohort are dispersedbypatient physical properties including age, height, body mass, and gender,as wellaswalking speed, which may limit capacity to discern some pathological features. The aim of this study was twofold. First, to use a multiple regression normalization strategy that accounts for subject age, height, body mass, gender, and self-selected walking speed to identify differences in spatial-temporal gait features between PD patients and controls; and second, to evaluate the effectiveness of machine learning strategies in classifying PD gait after gait nor..