Parkinson’s Disease Recognition using a Gamified Website: Machine Learning Feasibility Study

Abstract

AbstractBackgroundParkinson’s Disease (PD) affects millions globally, causing motor function impairments. Early detection is vital, and diverse data sources aid diagnosis. We focus on lower arm movements during keyboard and trackpad/touchscreen interactions, which serve as reliable indicators of PD. Previous works explore keyboard tapping and unstructured device monitoring, and we attempt to further these works with our structured tests taking account 2D hand movement in addition to finger tapping. Our feasibility study utilizes keystroke and mouse movement data from a structured online test conducted remotely combined with self-reported PD status to create a predictive model for detecting PD presence.ObjectiveThrough analysis of finger tapping speed and accuracy through keyboard input and 2-dimensional hand movement through mouse input, we differentiate between PD and non-PD participants. This comparative analysis enables us to establish clear distinctions between the two groups and explore the feasibility of using motor behavior to predict the presence of the disease.MethodsParticipants were recruited via email by the Hawaii Parkinson’s Association (HPA) and directed to a web application for the tests. The 2023 HPA symposium was also used as a forum to recruit participants and spread information about our study. The application recorded participant demographics, including age, gender, and race, as well as PD status. We conducted a series of tests to assess finger tapping, using on-screen prompts to request key presses of constant and random keys. Response times, accuracy, and unintended movements resulting in accidental presses were recorded. Participants performed a hand movement test consisting of tracing straight and curved on-screen ribbons using a trackpad or mouse, allowing us to evaluate stability and precision of two-dimensional hand movement. From this tracing, the test collected and stored insights concerning lower arm motor movement.ResultsOur formative study included 31 participants, 18 without PD and 13 with PD, and analyzed their lower limb movement data collected from keyboards and computer mice. From the dataset, we extracted 28 features and evaluated their significances using an ExtraTreeClassifier predictor. A Random Forest model was trained using the six most important features identified by the predictor. These selected features included insights into precision and movement speed derived from keyboard tapping and mouse tracing tests. This final model achieved an average F1-score of 0.7311 (±0.1663) and an average accuracy of 0.7429 (±0.1400) over 20 runs for predicting the presence of PD.ConclusionThis preliminary feasibility study suggests the possibility of utilizing technology-based limb movement data to predict the presence of PD, demonstrating the practicality of implementing this approach in a cost-effective and accessible manner. In addition, this study demonstrates that structured mouse movement tests can be used in combination with finger tapping to detect PD.