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Asian Conference on Computer Vision

ACCV 2018: Computer Vision – ACCV 2018 pp 564–576Cite as

Vision-Based Freezing of Gait Detection with Anatomic Patch Based Representation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11361))

Abstract

Parkinson’s disease (PD) impacts millions of people in the world. As freezing of gait (FoG) is a common symptom of PD patients that leads to falls and nursing home placement, it is very important to identify FoG event effectively and efficiently. Direct observation based assessment of FoG by doctors or trained experts is the de facto ‘gold standard’ for clinical diagnosis, which is time consuming. While several computer aided FoG event detection methods have been proposed, they were not particularly designed for video data collected during clinical diagnosis. In this paper, we treat video based FoG detection as a fine grained human action recognition problem and reduce the interference of visual content which is irrelevant to FoG, such as gait motion of supporting staff involved in clinical assessment. In order to effectively characterize FoG patterns, we propose to identify anatomic patches as the candidate regions which could be relevant to FoG events, and formulate FoG detection as a weakly-supervised learning task. The formulation will help identify the patches contributing to FoG events. To take both the global context of a clinical video and the local anatomic patches into account, several fusion strategies are investigated. Experimental results on videos collected from 45 subjects during clinical trials demonstrated promising results of our proposed method in terms of AUC of 0.869. To the best of our knowledge, this is one of the first studies on automatic FoG detection from clinical assessment videos.

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Acknowledgement

This research was partially supported by NHMRC-ARC Dementia Fellowship #1110414. We thank our patients who participated into the clinical assessment video collection. We would like to acknowledge and thank Moran Gilat, Julie Hall, Alana Muller, Jennifer Szeto and Courtney Walton for conducting and scoring the freezing of gait assessments. We would also like to acknowledge ForeFront, a large collaborative research group dedicated to the study of neurodegenerative diseases. It is funded by the National Health and Medical Research Council of Australia Program Grant (#1037746), Dementia Research Team Grant (#1095127) and NeuroSleepCentre of Research Excellence (#1060992), as well as the Australian Research Council Centre of Excellence in Cognition and its Disorders MemoryProgram (#CE110001021), and the Sydney Research Excellence Initiative 2020.

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Authors and Affiliations

  1. School of Information Technologies, The University of Sydney, Sydney, Australia

    Kun Hu & Zhiyong Wang

  2. Parkinson’s Disease Research Clinic, Brain and Mind Centre, The University of Sydney, Sydney, Australia

    Kaylena Ehgoetz Martens & Simon Lewis

Authors
  1. Kun Hu
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  2. Zhiyong Wang
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  3. Kaylena Ehgoetz Martens
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  4. Simon Lewis
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Corresponding author

Correspondence to Kun Hu .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C. V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Hu, K., Wang, Z., Ehgoetz Martens, K., Lewis, S. (2019). Vision-Based Freezing of Gait Detection with Anatomic Patch Based Representation. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_35

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  • DOI: https://doi.org/10.1007/978-3-030-20887-5_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20886-8

  • Online ISBN: 978-3-030-20887-5

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