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KMID : 1812820210050010034
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Journal of Korean Dental Hygiene Science
2021 Volume.5 No. 1 p.34 ~ p.40
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Validity of an Artificial Intelligence-Assisted Motion-Analysis System Using a Smartphone for Evaluating Weight-Bearing Activities in Individuals with Patellofemoral Pain Syndrome
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Kim Jun-Seok
Kim Yong-Wook
Woo Young-Keun
Park Kyue-Nam
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Abstract
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Background: An artificial intelligence-assisted motion-analysis system without markers, OpenPose, is used to calculate joint angles in sports and medical analyses. Measuring the angles of the hip and knee joint in the frontal plane during the performance of weight-bearing activities is valuable in patients with patellofemoral pain syndrome (PFPS).
Purpose: The purpose of this study was to assess the validity of OpenPose using a pre-trained human motion-tracking algorithm for measuring the angles of the hip and knee joint in the frontal plane during standing hip abduction, semi-squat movements, and forward step-down movements compared with marker-based three-dimensional motion analysis.
Study design: Cross-sectional study
Methods: Eight individuals with PFPS participated in the current study. To investigate the validity of OpenPose, the angles of the hip and knee in the frontal plane were measured simultaneously with a smartphone camera using the OpenPose library and Vicon as the gold-standard motion-analysis system while performing three weight-bearing activities. Pearson and Spearman correlation analysis was used to assess the validity of the OpenPose-based motion-analysis system.
Results: Correlation coefficients ranged from 0.04 to 0.61 on the more symptomatic side and from 0.02 to 0.88 on the less symptomatic side for the three weight-bearing activities. When performing standing hip abduction and step-down movements, the validity of the measurements of hip abduction was fair or good. When performing semi-squat movements, the validity of the knee abduction measurements was fair.
Conclusions: The OpenPose-based motion-analysis system can provide fair or good level of validity of measurements of frontal hip and knee angles during weight-bearing activities of individuals with PFPS in real environments and for remote rehabilitation.
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KEYWORD
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Artificial intelligence, Markerless motion analysis, Patellofemoral pain syndrome, Valgus, Validity
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