A Novel Computer Vision Approach to Kinematic Analysis of Handwriting with Implications for Assessing Neurodegenerative Diseases

Fine motor movement is a demonstrated biomarker for many health conditions that are especially difficult to diagnose early and require sensitivity to change in order to monitor over time. This is particularly relevant for neurodegenerative diseases (NDs), including Parkinson’s Disease (PD) and Alzheimer’s Disease (AD), which are associated with early changes in handwriting and fine motor skills. Kinematic analysis of handwriting is an emerging method for assessing fine motor movement ability, with data typically collected by digitizing tablets; however, these are often expensive, unfamiliar to patients, and are limited in the scope of collectible data. In this paper, we present a vision-based system for the capture and analysis of handwriting kinematics using a commodity camera and RGB video. We achieve writing position estimation within 0.5 mm and speed and acceleration errors of less than 1.1%. We further demonstrate that this data collection process can be part of an ND screening system with a developed ensemble classifier achieving 74% classification accuracy of Parkinson’s Disease patients with vision-based data. Overall, we demonstrate that this approach is an accurate, accessible, and informative alternative to digitizing tablets and with further validation has potential uses in early disease screening and long-term monitoring.Clinical relevance— This work establishes a more accessible alternative to digitizing tablets for extracting handwriting kinematic data through processing of RGB video data captured by commodity cameras, such as those in smartphones, with computer vision and machine learning. The collected data has potential for use in analysis to objectively and quantitatively differentiate between healthy individuals and patients with NDs, including AD and PD, as well as other diseases with biomarkers displayed in fine motor movement. The developed system has potential applications including providing widespread screening systems for NDs in low-income areas and resource-poor health systems, as well as an accessible form of disease long-term monitoring through telemedicine.