Machine learning-based classification of simple drawing movements in Parkinson's disease

This work explores the use of a pen-and-tablet device to study differences in hand movement and muscle coordination between healthy subjects and Parkinson's disease patients. We let volunteers draw simple horizontal lines and recorded the trajectory of the pen's tip on the pad's surface. The signals thus obtained were then processed to compute various features which correspond to the variability of the pen tip's velocity, the deviation from the horizontal plane, and the trajectory's entropy. Our goal was to establish simple and objective metrics which can be used to differentiate between normal and pathological movement. In a small-scale clinical trial, 44 age-matched subjects were divided in two groups, namely 20 healthy subjects (H), and 24 Parkinson's disease (PD) patients. We applied a comprehensive machine learning approach to build a model that could classify unknown subjects based on their line-drawing performance. We were able to achieve an average prediction accuracy of 91% (88% sensitivity [ΤP], 95% specificity [ΤN]). Our results show that the proposed method is a good candidate for differentiating between healthy and Parkinson's disease individuals, and shows promise in the context of telemedicine applications and tracking of the disease's symptoms via inexpensive, widely available hardware.