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Quantifying normal and parkinsonian gait features from home movies: Practical application of a deep learning–based 2D pose estimator

Kenichiro Sato, Yu Nagashima, Tatsuo Mano, View ORCID ProfileAtsushi Iwata, Tatsushi Toda
doi: https://doi.org/10.1101/782367
Kenichiro Sato
Department of Neurology, Graduate School of Medicine, University of Tokyo Hongo, Bunkyo-ku, Tokyo, Japan
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  • For correspondence: kenisatou-tky@umin.ac.jp iwata@m.u-tokyo.ac.jp
Yu Nagashima
Department of Neurology, Graduate School of Medicine, University of Tokyo Hongo, Bunkyo-ku, Tokyo, Japan
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Tatsuo Mano
Department of Neurology, Graduate School of Medicine, University of Tokyo Hongo, Bunkyo-ku, Tokyo, Japan
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Atsushi Iwata
Department of Neurology, Graduate School of Medicine, University of Tokyo Hongo, Bunkyo-ku, Tokyo, Japan
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  • ORCID record for Atsushi Iwata
  • For correspondence: kenisatou-tky@umin.ac.jp iwata@m.u-tokyo.ac.jp
Tatsushi Toda
Department of Neurology, Graduate School of Medicine, University of Tokyo Hongo, Bunkyo-ku, Tokyo, Japan
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Abstract

Objective Gait movies recorded in daily clinical practice are usually not filmed with specific devices, which prevents neurologists benefitting from leveraging gait analysis technologies. Here we propose a novel unsupervised approach to quantifying gait features and to extract cadence from normal and parkinsonian gait movies recorded with a home video camera by applying OpenPose, a deep learning–based 2D-pose estimator that can obtain joint coordinates from pictures or videos recorded with a monocular camera.

Methods Our proposed method consisted of two distinct phases: obtaining sequential gait features from movies by extracting body joint coordinates with OpenPose; and estimating cadence of periodic gait steps from the sequential gait features using the short-time pitch detection approach.

Results The cadence estimation of gait in its coronal plane (frontally viewed gait) as is frequently filmed in the daily clinical setting was successfully conducted in normal gait movies using the short-time autocorrelation function (ST-ACF). In cases of parkinsonian gait with prominent freezing of gait and involuntary oscillations, using ACF-based statistical distance metrics, we quantified the periodicity of each gait sequence; this metric clearly corresponded with the subjects’ baseline disease statuses.

Conclusion The proposed method allows us to analyze gait movies that have been underutilized to date in a completely data-driven manner, and might broaden the range of movies for which gait analyses can be conducted.

Footnotes

  • Data availability: The main data used in this study is distributed from CASIA database (http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp).

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 25, 2019.
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Quantifying normal and parkinsonian gait features from home movies: Practical application of a deep learning–based 2D pose estimator
Kenichiro Sato, Yu Nagashima, Tatsuo Mano, Atsushi Iwata, Tatsushi Toda
bioRxiv 782367; doi: https://doi.org/10.1101/782367
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Quantifying normal and parkinsonian gait features from home movies: Practical application of a deep learning–based 2D pose estimator
Kenichiro Sato, Yu Nagashima, Tatsuo Mano, Atsushi Iwata, Tatsushi Toda
bioRxiv 782367; doi: https://doi.org/10.1101/782367

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