Validation of OpenCap: A low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks

Context: The Open Landing Error Scoring System (OpenLESS) is a novel development aimed at automating the LESS for assessment of lower extremity movement quality during a jump-landing task. With increasing utilization of clinical measures to monitor outcomes and limited time during clinical visits for a lengthy analysis of functional movement, there is a pressing need to extend automation efforts. Addressing these issues, OpenLESS is an open-source tool that utilizes a freely available markerless motion capture system to automate the LESS using three-dimensional kinematics. Objective: To describe the development of OpenLESS, examine the validity against expert rater LESS scores in healthy and clinically relevant cohorts, and assess the intersession reliability collected across four time points in an athlete cohort. Design: Cross-Sectional Participants: 92 participants (72 females and 20 males, mean age 23.3 years) from healthy, post-anterior cruciate ligament reconstruction (ACLR; median 33 months since surgery) and amateur athlete cohorts. Main Outcome Measure(s): A software package, OpenLESS, was developed to interpret movement quality (LESS score) from kinematics captured from markerless motion capture. Validity and reliability were assessed with intraclass correlation coefficients (ICC), standard error of measure (SEM), and minimal detectable change (MDC). Results: OpenLESS agreed well with expert rater LESS scores for healthy (ICC2,k=0.79) and clinically relevant, post-ACLR cohorts (ICC2,k=0.88). The automated OpenLESS system reduced scoring time, processing all 159 trials in under 15 minutes compared to the 18.5 hours (7 minutes per trial) required for manual expert rater scoring. When tested outside laboratory conditions, OpenLESS showed excellent reliability across repeated sessions (ICC2,k>0.89), with a SEM of 0.98 errors and MDC of 2.72 errors. Conclusion: OpenLESS shows promise as an efficient, automated tool for clinically assessing jump-landing quality, with good validity versus experts in healthy and post-ACLR populations, and excellent field reliability, addressing the need for objective movement analysis.

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Research on 3D Animation Capture Driving Technology for Digital Media

Kong

2024

Applied Mathematics and Nonlinear Sciences

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In recent years, the extensive use of personalized cartoon models in film and television entertainment, games, and other fields has made 3D animation capture drive technology an important research topic in the field of virtual reality. This paper focuses on research in the field of 3D animation capture technology, specifically from two perspectives: human movement and facial expression. The human body movement node data are collected using sensors, and the movement state is represented in three-dimensional space using the form of quaternion, and the Euler angle and rotation matrix are applied to realize the data conversion, respectively. Personalized models of human facial expression data are acquired and built using optical motion capture technology. Enter the data into the database, finish the dynamic 3D re-modeling after data segmentation, and implement 3D animation utilizing 3D motion capture driving technology in 3D animation. The facial fluency index based on the sensor in human body movement with optical capture is about 80, and the fluency index is higher than 100 in four frames of 105, 110, 127, and 128. The mean value of the animated movie designed based on 3D animation capture driving technology is higher than that of the control group in the four aspects of interactivity, interestingness, informativeness, and behavioral change, and the differences between the two groups are 3.1977, 1.899, 0.4378, 1.1444, and the mean value is less than 0.01, the animated movie designed based on 3D motion capture technology brings better viewing experience to the audience.

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Validation of OpenCap: A low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks

Cited by 4 publications

References 41 publications

Repeatability and minimal detectable change including clothing effects for smartphone-based 3D markerless motion capture

Repeatability and minimal detectable change including clothing effects for smartphone-based 3D markerless motion capture

Open Source, Open Science: Development of OpenLESS as the Automated Landing Error Scoring System

Research on 3D Animation Capture Driving Technology for Digital Media

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