Gait Model Analysis of Parkinson’s Disease Patients under Cognitive Load

Abstract

Parkinson's disease is a neurodegenerative disease that affects close to 10 million with various symptoms including tremors and changes in gait. Observing differences or changes in an individual's manifestations of gait may provide a mechanism to identify Parkinson's disease and understand specific changes. In this study, timeseries data from both Control subjects and Parkinson's disease patients was modelled with symbolic regression and extreme gradient boosting. Model effectiveness was analyzed along with the differences in the models between modelling strategies, between Control subjects and Parkinson's disease patients, and between normal walking and walking while under a cognitive load. Both modelling strategies were found to effective. The symbolic regression models were more easily interpreted, while extreme gradient boosting had higher overall accuracy. Interpretation of the models identified certain characteristics that distinguished Control subjects from Parkinson's disease patients and normal walking conditions from walking while under a cognitive load.