Machine Learning Algorithm for Gait Analysis and Classification on Early Detection of Parkinson

Abstract

Gait analysis using kinetic data such as pressure distribution underneath the foot has been a topic of interest for assessing falls in elderly and certain pathology such as Parkinson's disease. The disease which affects the central nervous system cannot be ultimately diagnosed by a test. In this letter, we describe a detection algorithm able to classify subjects into Parkinson or normal subjects based on load distribution during gait. This will allow those with the disease to benefit from early detection and thus early treatment. We perform spatial and time signal analyses over vertical ground reaction forces to categorize gaits as balanced or unbalanced, where unbalanced gaits correspond to subjects with Parkinson's disease and balanced gaits could be relevant to both normal and diseased subjects. Then simple features like correlation are used to further differentiate between balanced-normal subjects and balanced-diseased subjects. A 95% overall classification accuracy has been achieved using a linear decision boundary. This letter can be employed to form the basis of designing a portable device for early Parkinson's disease detection on a real-time basis. Moreover, it can be used for evaluation purposes of a rehabilitation program.