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Concurrent assessment of gait kinematics using marker-based and markerless motion capture

Robert Kanko, Elise Laende, Elysia Davis, W. Scott Selbie, Kevin J. Deluzio
doi: https://doi.org/10.1101/2020.12.10.420075
Robert Kanko
1Department of Mechanical & Materials Engineering, Queen’s University, Kingston, Canada
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  • For correspondence: r.kanko@queensu.ca
Elise Laende
1Department of Mechanical & Materials Engineering, Queen’s University, Kingston, Canada
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Elysia Davis
1Department of Mechanical & Materials Engineering, Queen’s University, Kingston, Canada
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W. Scott Selbie
2Theia Markerless Inc., Kingston, Canada
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Kevin J. Deluzio
1Department of Mechanical & Materials Engineering, Queen’s University, Kingston, Canada
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Abstract

Kinematic analysis is a useful and widespread tool used in research and clinical biomechanics for the estimation of human pose and the quantification of human movement. Common marker-based optical motion capture systems are expensive, time intensive, and require highly trained operators to obtain kinematic data. Markerless motion capture systems offer an alternative method for the measurement of kinematic data with several practical benefits. This work compared the kinematics of human gait measured using a deep learning algorithm-based markerless motion capture system to those of a common marker-based motion capture system. Thirty healthy adult participants walked on a treadmill while data were simultaneously recorded using eight video cameras (markerless) and seven infrared optical motion capture cameras (marker-based). Video data were processed using markerless motion capture software, marker-based data were processed using marker-based capture software, and both sets of data were compared. The average root mean square distance (RMSD) between corresponding joints was less than 3 cm for all joints except the hip, which was 4.1 cm. Lower limb segment angles indicated pose estimates from both systems were very similar, with RMSD of less than 6° for all segment angles except those that represent rotations about the long axis of the segment. Lower limb joint angles captured similar patterns for flexion/extension at all joints, ab/adduction at the knee and hip, and toe-in/toe-out at the ankle. These findings demonstrate markerless motion capture can measure similar 3D kinematics to those from marker-based systems.

Competing Interest Statement

WSS is the President of Theia Markerless Inc. (Kingston, Ontario), the developers of Theia3D. He contributed to the conception and design of the study, critically revised the article for intellectual content, and provided final approval of the submitted version. WSS was not involved with the collection, analysis, or interpretation of data.

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 December 11, 2020.
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Concurrent assessment of gait kinematics using marker-based and markerless motion capture
Robert Kanko, Elise Laende, Elysia Davis, W. Scott Selbie, Kevin J. Deluzio
bioRxiv 2020.12.10.420075; doi: https://doi.org/10.1101/2020.12.10.420075
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Concurrent assessment of gait kinematics using marker-based and markerless motion capture
Robert Kanko, Elise Laende, Elysia Davis, W. Scott Selbie, Kevin J. Deluzio
bioRxiv 2020.12.10.420075; doi: https://doi.org/10.1101/2020.12.10.420075

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