Detecting Parkinson’s Disease Using Machine Learning from Movement

Abstract

This study explored the potential of machine learning techniques, specifically decision trees and artificial neural networks (ANNs), to detect Parkinson's Disease (PD) using data collected from the mPower mobile iOS app. Released in 2016 by Sage Bionetworks, mPower enables individuals, both with and without PD, to assess their cognitive and physical abilities through various tasks related to memory, tapping, voice, and movement. The main focus of this study is on the walking task within the app's version 1.0 build 7. Participants were required to walk unassisted for approximately 20 steps in a straight line, followed by a 30-second period of standing still, and then returning with 20 steps. The smartphone's accelerometer and gyroscope captured three-dimensional (3D) rotation data (x, y, z) during these movements, with the device placed in the participant's pocket or bag. A convolutional neural network was applied to the movement dataset to assess confirmed PD cases, utilizing accelerometer and gyroscope readings during outward walking, return walking, and rest periods.