Types of anomalies in two-dimensional video-based gait analysis in uncontrolled environments

Two-dimensional video-based pose estimation is a technique that can be used to estimate human skeletal coordinates from video data alone. It is also being applied to gait analysis and in particularly, due to its simplicity of measurement, it has the potential to be applied to gait analysis of large...

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Bibliographic Details
Published in:	PLoS computational biology 2023-01, Vol.19 (1), p.e1009989-e1009989
Main Authors:	Sugiyama, Yuki, Uno, Kohei, Matsui, Yusuke
Format:	Article
Language:	eng
Subjects:	Accuracy Anomalies Biology and Life Sciences Biomechanical engineering Biomechanical Phenomena Biomechanics Cameras Computer Simulation Errors Gait Gait Analysis Health aspects Humans Medicine and Health Sciences Motion capture Observations Populations Pose estimation Range of motion Two dimensional analysis Video data Workflow
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Summary:	Two-dimensional video-based pose estimation is a technique that can be used to estimate human skeletal coordinates from video data alone. It is also being applied to gait analysis and in particularly, due to its simplicity of measurement, it has the potential to be applied to gait analysis of large populations. However, it is considered difficult to completely homogenize the environment and settings during the measurement of large populations. Therefore, it is necessary to appropriately deal with technical errors that are not related to the biological factors of interest. In this study, by analyzing a large cohort database, we have identified four major types of anomalies that occur during gait analysis using OpenPose in uncontrolled environments: anatomical, biomechanical, and physical anomalies and errors due to estimation. We have also developed a workflow for identifying and correcting these anomalies and confirmed that this workflow is reproducible through simulation experiments. Our results will help obtain a comprehensive understanding of the anomalies to be addressed during pre-processing for 2D video-based gait analysis of large populations.
ISSN:	1553-7358 1553-734X 1553-7358