Quantifying Levodopa-Induced Dyskinesia Using Depth Camera

Maria Dyshel, David Arkadir, Hagai Bergman, Daphna Weinshall

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We present a novel method to detect and assess the severity of Levodopa-Induced Dyskinesia (LID) in Parkinson's Disease (PD) patients, based on Microsoft Kinect recordings of the patients. Dyskinesia denotes involuntary movements induced by chronic treatment with levodopa in patients with PD. Detection and objective quantification of dyskinesia is essential for optimizing the medication regime and developing novel treatments for PD. We used Microsoft Kinect sensor to track limb and neck movements of a patient performing two motor tasks. Using a new motion segmentation algorithm, kinematic features were extracted from the videos and classified using Support Vector Machines (SVMs). The method was tested on 25 recordings of 9 PD patients, and achieved sensitivity of 0.82 at EER in overall dyskinesia detection. Moreover, it provided a numerical overall score for the severity of dyskinesia, which showed high correlation with the neurologist's assessment of the patient's state. The study shows that depth camera recordings can be used to monitor and grade the severity of levodopa-induced dyskinesia, and therefore can potentially provide valuable aid to clinicians and researchers.

Original languageAmerican English
Title of host publicationProceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781467383905
StatePublished - 11 Feb 2016
Event15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015 - Santiago, Chile
Duration: 11 Dec 201518 Dec 2015

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


Conference15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015


  • Biomedical monitoring
  • Cameras
  • Feature extraction
  • Medical diagnostic imaging
  • Monitoring
  • Motion segmentation
  • Videos

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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