Model Touch Pointing and Detect Parkinson's Disease via a Mobile Game

Abstract

Touch pointing is one of the primary interaction actions on mobile devices. In this research, we aim to (1) model touch pointing for people with Parkinson's Disease (PD), and (2) detect PD via touch pointing. We created a mobile game called MoleBuster in which a user performs a sequence of pointing actions. Our study with 40 participants shows that PD participants exhibited distinct pointing behavior. PD participants were much slower and had greater variances in movement time (MT), while their error rate was slightly lower than age-matched non-PD participants, indicating PD participants traded speed for accuracy. The nominal width Finger-Fitts law showed greater fitness than Fitts' law, suggesting this model should be adopted in lieu of Fitts' law to guide mobile interface design for PD users. We also proposed a CNN-Transformer-based neural network model to detect PD. Taking touch pointing data and comfort rating of finger movement as input, this model achieved an AUC of 0.97 and sensitivity of 0.95 in leave-one-user-out cross-validation. Overall, our research contributes models that reveal the temporal and spatial characteristics of touch pointing for PD users, and provide a new method (CNN-Transformer model) and a mobile game (MoleBuster) for convenient PD detection.