Skip to main content
SpringerLink
Log in

Visualization of Parkinson’s Disease Tremor for a Telemedicine System

  • Conference paper
  • First Online:
HCI International 2022 – Late Breaking Posters (HCII 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1654))

Included in the following conference series:

  • 1033 Accesses

Abstract

In this paper, we propose a method for visualizing Parkinson’s disease tremors using augmented reality to support telemedicine. The proposed method utilizes OpenPose to extract body joints and connects the coordinates of every two adjacent joints with straight lines in the video. We made the angular changes of adjacent joints representing tremors, with each angle’s latest 64 data points as a set of signals. The color and thickness of the lines are changed by using the frequency of the maximum amplitude and the strength of tremors. We created a prototype system and conducted an experiment to evaluate the proposed method. The experimental results showed significant color and thickness changes in the lines where tremors occurred. Moreover, we sent the video with the visualization results to a doctor, and the doctor found this method to be useful in visualizing the characteristics of tremors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bhat, M., Inamdar, S., Kulkarni, D., Kulkarni, G., Shriram, R.: Parkinson’s disease prediction based on hand tremor analysis. In: 2017 International Conference on Communication and Signal Processing (ICCSP), pp. 0625–0629 (2017)

    Google Scholar 

  2. Kostikis, N., Hristu-Varsakelis, D., Arnaoutoglou, M., Kotsavasiloglou, C.: A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J. Biomed. Health Inform. 19(6), 1835–1842 (2015)

    Article  Google Scholar 

  3. LeMoyne, R., Mastroianni, T., Cozza, M., Coroian, C., Grundfest, W.: Implementation of an iPhone for characterizing Parkinson’s disease tremor through a wireless accelerometer application. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 4954–4958 (2010)

    Google Scholar 

  4. Pang, Y., et al.: Automatic detection and quantification of hand movements toward development of an objective assessment of tremor and bradykinesia in Parkinson’s disease. J. Neurosci. Methods 333, 108576 (2020)

    Article  Google Scholar 

  5. Polat, K.: Freezing of Gait (FoG) detection using logistic regression in parkinson’s disease from acceleration signals. In: 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT), pp. 1–4 (2019)

    Google Scholar 

  6. El-Attar, A., Ashour, A.S., Dey, N., Abdelkader, H., Abd El-Naby, M.M., Sherratt, R.S.: Discrete wavelet transform-based freezing of gait detection in Parkinson’s disease. J. Exp. Theor. Artif. Intell. 33(4), 543–559 (2018)

    Article  Google Scholar 

  7. Bigy, A. A. M., Banitsas, K., Badii, A., Cosmas, J.: Recognition of postures and freezing of gait in Parkinson’s disease patients using Microsoft Kinect sensor. In: 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 731–734 (2015)

    Google Scholar 

  8. Fleyeh, H., Westin, J.: Extracting body landmarks from videos for Parkinson gait analysis. In: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), 2019, pp. 379–384 (2019)

    Google Scholar 

  9. Li, M.H., Mestre, T.A., Fox, S.H., Taati, B.: Vision-based assessment of Parkinsonism and levodopa-induced dyskinesia with deep learning pose estimation. J. Neuroeng. Rehabil. 15(1), 1–13 (2017)

    Google Scholar 

  10. Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y.: OpenPose: realtime MultiPerson 2D pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 172–186 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-Ku, Saitama City, 338-8570, Japan

    Tingyu Du

  2. Department of General Medicine, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-Ku, Hiroshima City, 734-8551, Japan

    Takashi Komuro & Keiko Ogawa-Ochiai

Authors
  1. Tingyu Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takashi Komuro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keiko Ogawa-Ochiai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Takashi Komuro .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Foundation for Research and Technology Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  3. Foundation for Research and Technology Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

  4. University of Central Florida, Orlando, FL, USA

    Gavriel Salvendy

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Du, T., Komuro, T., Ogawa-Ochiai, K. (2022). Visualization of Parkinson’s Disease Tremor for a Telemedicine System. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1654. Springer, Cham. https://doi.org/10.1007/978-3-031-19679-9_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19679-9_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19678-2

  • Online ISBN: 978-3-031-19679-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation