We present a framework for assessing which types of simple movement tasks are most discriminative between healthy controls and Parkinson’s patients. We collected movement data in a game-like environment, where we used the Microsoft Kinect sensor for tracking the user’s joints. We recruited 63 individuals for the study, of whom 30 had been diagnosed with Parkinson’s disease. A physician evaluated all participants on movement-related rating scales, e.g., elbow rigidity. The participants also completed the game task, moving their arms through a specific pattern. We present an innovative approach for data acquisition in a game-like environment, and we propose a novel method, sparse ordinal regression, for predicting the severity of motion disorders from the data.
CITATION STYLE
Einarsson, G., Clemmensen, L. K. H., Rudå, D., Fink-Jensen, A., Nielsen, J. B., Pagsberg, A. K., … Paulsen, R. R. (2018). Computer Aided Identification of Motion Disturbances Related to Parkinson’s Disease. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11121 LNCS, pp. 1–8). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-00320-3_1
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