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Article

Validity of AI-Based Gait Analysis for Simultaneous Measurement of Bilateral Lower Limb Kinematics Using a Single Video Camera

by
Takumi Ino
1,2,
Mina Samukawa
3,*,
Tomoya Ishida
3,
Naofumi Wada
4,
Yuta Koshino
3,
Satoshi Kasahara
3 and
Harukazu Tohyama
3
1
Graduate School of Health Sciences, Hokkaido University, Sapporo 0600812, Japan
2
Department of Physical Therapy, Faculty of Health Sciences, Hokkaido University of Science, Sapporo 0068585, Japan
3
Faculty of Health Sciences, Hokkaido University, Sapporo 0600812, Japan
4
Department of Information and Computer Science, Faculty of Engineering, Hokkaido University of Science, Sapporo 0068585, Japan
*
Author to whom correspondence should be addressed.
Sensors 2023, 23(24), 9799; https://doi.org/10.3390/s23249799
Submission received: 16 November 2023 / Revised: 2 December 2023 / Accepted: 12 December 2023 / Published: 13 December 2023
(This article belongs to the Special Issue Deep Learning in Visual and Wearable Sensing for Motion Analysis and Healthcare)
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Abstract

Accuracy validation of gait analysis using pose estimation with artificial intelligence (AI) remains inadequate, particularly in objective assessments of absolute error and similarity of waveform patterns. This study aimed to clarify objective measures for absolute error and waveform pattern similarity in gait analysis using pose estimation AI (OpenPose). Additionally, we investigated the feasibility of simultaneous measuring both lower limbs using a single camera from one side. We compared motion analysis data from pose estimation AI using video footage that was synchronized with a three-dimensional motion analysis device. The comparisons involved mean absolute error (MAE) and the coefficient of multiple correlation (CMC) to compare the waveform pattern similarity. The MAE ranged from 2.3 to 3.1° on the camera side and from 3.1 to 4.1° on the opposite side, with slightly higher accuracy on the camera side. Moreover, the CMC ranged from 0.936 to 0.994 on the camera side and from 0.890 to 0.988 on the opposite side, indicating a “very good to excellent” waveform similarity. Gait analysis using a single camera revealed that the precision on both sides was sufficiently robust for clinical evaluation, while measurement accuracy was slightly superior on the camera side.
Keywords: human pose estimation; 2D motion analysis; walking; markerless; motion capture; Vicon; joint kinematics; both legs; lower extremity; reliability human pose estimation; 2D motion analysis; walking; markerless; motion capture; Vicon; joint kinematics; both legs; lower extremity; reliability

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MDPI and ACS Style

Ino, T.; Samukawa, M.; Ishida, T.; Wada, N.; Koshino, Y.; Kasahara, S.; Tohyama, H. Validity of AI-Based Gait Analysis for Simultaneous Measurement of Bilateral Lower Limb Kinematics Using a Single Video Camera. Sensors 2023, 23, 9799. https://doi.org/10.3390/s23249799

AMA Style

Ino T, Samukawa M, Ishida T, Wada N, Koshino Y, Kasahara S, Tohyama H. Validity of AI-Based Gait Analysis for Simultaneous Measurement of Bilateral Lower Limb Kinematics Using a Single Video Camera. Sensors. 2023; 23(24):9799. https://doi.org/10.3390/s23249799

Chicago/Turabian Style

Ino, Takumi, Mina Samukawa, Tomoya Ishida, Naofumi Wada, Yuta Koshino, Satoshi Kasahara, and Harukazu Tohyama. 2023. "Validity of AI-Based Gait Analysis for Simultaneous Measurement of Bilateral Lower Limb Kinematics Using a Single Video Camera" Sensors 23, no. 24: 9799. https://doi.org/10.3390/s23249799

APA Style

Ino, T., Samukawa, M., Ishida, T., Wada, N., Koshino, Y., Kasahara, S., & Tohyama, H. (2023). Validity of AI-Based Gait Analysis for Simultaneous Measurement of Bilateral Lower Limb Kinematics Using a Single Video Camera. Sensors, 23(24), 9799. https://doi.org/10.3390/s23249799

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