Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway
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Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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The Ottawa Hospital Research Institute, Ottawa, ON K1H 8M2, Canada
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Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Yudong Zhang
Computation 2021, 9(12), 130; https://doi.org/10.3390/computation9120130
Received: 13 October 2021
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Revised: 22 November 2021
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Accepted: 22 November 2021
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Published: 2 December 2021
(This article belongs to the Section Computational Engineering)
New artificial intelligence- (AI) based marker-less motion capture models provide a basis for quantitative movement analysis within healthcare and eldercare institutions, increasing clinician access to quantitative movement data and improving decision making. This research modelled, simulated, designed, and implemented a novel marker-less AI motion-analysis approach for institutional hallways, a Smart Hallway. Computer simulations were used to develop a system configuration with four ceiling-mounted cameras. After implementing camera synchronization and calibration methods, OpenPose was used to generate body keypoints for each frame. OpenPose BODY25 generated 2D keypoints, and 3D keypoints were calculated and postprocessed to extract outcome measures. The system was validated by comparing ground-truth body-segment length measurements to calculated body-segment lengths and ground-truth foot events to foot events detected using the system. Body-segment length measurements were within 1.56 (SD = 2.77) cm and foot-event detection was within four frames (67 ms), with an absolute error of three frames (50 ms) from ground-truth foot event labels. This Smart Hallway delivers stride parameters, limb angles, and limb measurements to aid in clinical decision making, providing relevant information without user intervention for data extraction, thereby increasing access to high-quality gait analysis for healthcare and eldercare institutions.
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Keywords:
Smart Hallway; artificial intelligence; motion analysis; marker-less; computer vision; 3D reconstruction; gait
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MDPI and ACS Style
McGuirk, C.J.C.; Baddour, N.; Lemaire, E.D. Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway. Computation 2021, 9, 130. https://doi.org/10.3390/computation9120130
AMA Style
McGuirk CJC, Baddour N, Lemaire ED. Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway. Computation. 2021; 9(12):130. https://doi.org/10.3390/computation9120130
Chicago/Turabian StyleMcGuirk, Connor J.C., Natalie Baddour, and Edward D. Lemaire. 2021. "Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway" Computation 9, no. 12: 130. https://doi.org/10.3390/computation9120130
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