The Accuracy, Validity and Reliability of Theia3D Markerless Motion Capture for Studying the Biomechanics of Human Movement: A Systematic Review
Date
Authors
Varcin, F
Boocock, MG
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier BV
Abstract
Recent advancements in computer vision recognition combined with the use of pose estimation algorithms has led to a rapid increase in the use of 3D video-based markerless (ML) motion capture to study human movement. One such prominent system is Theia3D. To determine the accuracy, validity, and reliability of Theia3D, a systematic literature review was conducted across five electronic databases using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines. Studies were included if they investigated the accuracy, validity, or reliability of Theia3D against a standardised method and reported on at least one biomechanical measure. A modified version of COSMIN (Consensus-based Standards for the Selection of Health Measurement Instruments) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation) were used to evaluate the quality of evidence. Sixteen studies met the inclusion criteria, the majority of which assessed the validity of kinematics during gait or running. Pooled lower limb kinematics showed reasonable accuracy, whilst hip flexion/extension and rotations of the lower limb joints in the transverse plane suggests poor accuracy. Most spatiotemporal gait parameters measured using Theia3D demonstrated excellent validity (Intraclass correlation coefficient (ICC) > 0.9) and inter-session reliability (gait speed, Standard Error of Measurement (SEM) ≤ 0.07 m/s; step/stride length, SEM ≤ 0.06 m; ICC > 0.95). The accuracy, validity, and reliability of Theia3D used in the biomechanical analysis of functional tasks and in different population groups shows promise. However, there is a need for improved methods by which to compare data and a standardisation of biomechanical modelling approaches.Description
Keywords
Biomechanics, Gait, Kinematics, Markerless motion capture, Systematic review, Theia3D, 32 Biomedical and Clinical Sciences, 4201 Allied Health and Rehabilitation Science, 42 Health Sciences, 3202 Clinical Sciences, 4207 Sports Science and Exercise, Bioengineering, 08 Information and Computing Sciences, 09 Engineering, Medical Informatics, 32 Biomedical and clinical sciences, 42 Health sciences, 46 Information and computing sciences
Source
Artificial Intelligence in Medicine, ISSN: 0933-3657 (Print); 1873-2860 (Online), Elsevier BV, 173, 103332-. doi: 10.1016/j.artmed.2025.103332
Publisher's version
Rights statement
© 2025 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.