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Computer Aided Identification of Motion Disturbances Related to Parkinson’s Disease

Research output: Chapter in Book/Report/Conference proceedingBook chapterCommunication

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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.
Original languageEnglish
Title of host publicationPRedictive Intelligence in MEdicine. PRIME 2018. : Lecture Notes in Computer Science
EditorsIslem Rekik, Gozde Unal, Ehsan Adeli, Sang Hyun Park
Number of pages8
Volume11121
Place of PublicationSwitzerland
PublisherSpringer Nature Switzerland AG
Publication date2018
Pages1-8
ISBN (Print)978-3-030-00319-7
ISBN (Electronic)978-3-030-00320-3
DOIs
Publication statusPublished - 2018
SeriesLecture Notes in Computer Science
ISSN0302-9743

ID: 55456466