Automatic Assessment of Bradykinesia in Parkinson’s Disease Using Tapping Videos

Parkinson’s disease is a chronic neurodegenerative illness that se-verely affects the everyday life of a patient. The severity of Parkin-son’s disease is assessed using the MDS-UPDRS scale. In this study, we explore the feasibility of automatically evaluating bradykinesia, a key symptom of Parkinson’s disease, from tapping videos recorded on smartphones in everyday settings. We collected a dataset of 183 tapping videos, from 91 individuals. Videos were assessed by neu-rologist into 5 classes of the MDS-UPDRS scale. For data extraction, we employed MediaPipe Hand, which provides a time series of hand skeleton movements. The data was preprocessed to eliminate noise and subsequently used for either feature construction or directly in neural networks. Utilizing manually created features in a multilayer perceptron classifier resulted in 61 % accuracy and an F1 score of 0.61 on our test set. Employing a fully convolutional network, we improved the accuracy to 78 % and the F1 score to 0.75. Additionally, we developed the tool for visualising tapping and displaying key data, providing detailed insights into tapping patterns.