Single-view, video-based diagnosis of Parkinson's Disease based on arm and leg joint tracking

Jun Seok Seo, Yiyu Chen, Do Young Kwon, Christian Wallraven

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automatic diagnosis of Parkinson's Disease (PD) from sensor data is an important topic given the growing numbers of patients, and the increasing costs to the quality of life of an aging society. Several approaches have been proposed aimed at such an automatic diagnosis, but often suffer from complicated sensor setups or setups ill-fitting for the limitations in clinical settings. Here, we present a system that uses frequency-based analysis of joint data from both arms and legs from a single, frontally-viewed video of people walking towards a camera. We evaluate three machine-learning models on frequency-based features extracted from the joint dynamics on two larger datasets containing a total of N=300 videos of over 50 PD patients and healthy control people. Results confirm typical clinical expectations (leg frequencies are slower in PD patients) and in addition show excellent generalizability even across datasets with performance of up to 97% for an Ensemble classifier.

Original languageEnglish
Title of host publicationProceedings - 2022 International Conference on Mechanical, Automation and Electrical Engineering, CMAEE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages172-176
Number of pages5
ISBN (Electronic)9798350346572
DOIs
Publication statusPublished - 2022
Event2022 International Conference on Mechanical, Automation and Electrical Engineering, CMAEE 2022 - Virtual, Online, China
Duration: 2022 Dec 162022 Dec 18

Publication series

NameProceedings - 2022 International Conference on Mechanical, Automation and Electrical Engineering, CMAEE 2022

Conference

Conference2022 International Conference on Mechanical, Automation and Electrical Engineering, CMAEE 2022
Country/TerritoryChina
CityVirtual, Online
Period22/12/1622/12/18

Bibliographical note

Funding Information:
This study was supported by the National Research Foundation of Korea under project BK21 FOUR and grant NRF-2020R1F1A1076317 as well as by Institute of Information & Communications Technology Planning & Evaluation (IITP) grants funded by the Korea government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning; No. 2019-0-00079, Department of Artificial Intelligence, Korea University; No. 2021-0-02068, Artificial Intelligence Inovation Hub).

Publisher Copyright:
© 2022 IEEE.

Keywords

  • 2d
  • Parkinson's
  • Parkinson's Disease
  • arm swing
  • artificial intelligence
  • gait
  • machine learning
  • openpose
  • pose analysis
  • single camera
  • video analysis
  • walk

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Media Technology
  • Control and Optimization

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