An Ensemble Classification Technique of Neurodegenerative Diseases from Gait Analysis

Abstract

Neurodegenerative diseases (NDDs) lead to extreme locomotion disorders, which stem from the impairment of motor function caused by these diseases. They alter the gait rhythms and gait dynamics of their patients, which can be reflected in their time-series recordings of footfall contact times. Studies has shown that human gait can be used to identify NDDs. In this paper, an ensemble classification-based diagnostic system that uses patients’ gait data to diagnose NDDs is introduced. The diagnostic system is an ensemble of four binary classifications. The proposed technique is applied to gait dynamics data recorded from 64 subjects representing three NDDs: Amyotrophic Lateral Sclerosis (ALS), Parkinson’s Disease (PD), and Huntington’s Disease (HD), in addition to healthy subjects. Our results demonstrate the ability of the proposed technique to recognize gait dynamics of ALS, PD and HD with an accuracy of 96.8, 86.8% and 85.5%, respectively. These results demonstrate the efficacy of the proposed approach in diagnosing NDDs from gait analysis.