{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T21:37:46Z","timestamp":1785533866126,"version":"3.56.0"},"reference-count":51,"publisher":"ASME International","issue":"12","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Markerless motion capture (MMC) shows promise for examining human movement across many domains because of its nonintrusive nature and negligible per-subject setup time. However, published MMC systems typically require specific hardware. This validation study compared lower-body joint kinematics from Ergomechanic, a hardware-agnostic pose model-based MMC system, to an established marker-based motion capture (MBMC) system. Static trial data from eighteen people were used to register MMC keypoints within a widely used musculoskeletal model. The registered model was used to calculate joint kinematics for static pose, squatting, and walking trials. A novel perturbation analysis estimated the contributions to differences in MBMC and MMC approaches to measurement disparities. Very good (0.87\u20131.0) correlations between the systems were calculated for ankle, knee, and hip flexion\u2013extension angles. Good (0.70-0.86) correlations were found for hip external\u2013internal and abduction\u2013adduction. Pelvis and lumbar spine angles had a wider range of correlation results (-0.06 to 0.95), likely due to the few MMC keypoints in these body regions. Relative contributions from the perturbation analysis were (i) 75% from variations in MMC data relative to MBMC; (ii) 8% because MMC keypoints (26)\u2009&amp;lt;\u2009MBMC markers (67); and (iii) 3% from differences in musculoskeletal model scaling. These results validate Ergomechanic for leg kinematics during standing, walking, and squatting. Further, they suggest system improvements for pelvis and torso kinematics and provide new insights into the sources of known differences between MMC and MBMC measurements.<\/jats:p>","DOI":"10.1115\/1.4069821","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T15:33:09Z","timestamp":1758295989000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":2,"title":["Evaluation of the Ergomechanic Markerless Motion Capture System for Lower Body Kinematics During Standing, Squatting, and Walking"],"prefix":"10.1115","volume":"147","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4772-4917","authenticated-orcid":false,"given":"Simon M.","family":"Harrison","sequence":"first","affiliation":[{"name":"Data61, CSIRO, Clayton, VIC 3168, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond C. Z.","family":"Cohen","sequence":"additional","affiliation":[{"name":"Data61, CSIRO, Clayton, VIC 3168, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott C.","family":"Starkey","sequence":"additional","affiliation":[{"name":"Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, The University of Melbourne, Parkville, VIC 3010, Australia"},{"id":[{"id":"https:\/\/ror.org\/01ej9dk98","id-type":"ROR","asserted-by":"publisher"}],"name":"The University of Melbourne"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayan","family":"Greenwood","sequence":"additional","affiliation":[{"name":"Human Robotics Lab, Department of Mechanical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia"},{"id":[{"id":"https:\/\/ror.org\/01ej9dk98","id-type":"ROR","asserted-by":"publisher"}],"name":"The University of Melbourne"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ernest","family":"Cheong","sequence":"additional","affiliation":[{"name":"Human Robotics Lab, Department of Mechanical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia"},{"id":[{"id":"https:\/\/ror.org\/01ej9dk98","id-type":"ROR","asserted-by":"publisher"}],"name":"The University of Melbourne"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khoi","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Human Robotics Lab, Department of Mechanical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia"},{"id":[{"id":"https:\/\/ror.org\/01ej9dk98","id-type":"ROR","asserted-by":"publisher"}],"name":"The University of Melbourne"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N. H. Phu","family":"Trinh","sequence":"additional","affiliation":[{"name":"Human Robotics Lab, Department of Mechanical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia"},{"id":[{"id":"https:\/\/ror.org\/01ej9dk98","id-type":"ROR","asserted-by":"publisher"}],"name":"The University of Melbourne"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomislav","family":"Ba\u010dek","sequence":"additional","affiliation":[{"name":"Human Robotics Lab, Department of Mechanical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denny","family":"Oetomo","sequence":"additional","affiliation":[{"name":"Human Robotics Lab, Department of Mechanical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"33","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"issue":"2","key":"2025102911043694100_bib1","first-page":"100097","article-title":"IMU-Based Motion Capture System for Rehabilitation Applications: A Systematic Review","volume":"3","year":"2023","journal-title":"Biomim. Intell. Robot."},{"issue":"1","key":"2025102911043694100_bib2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s12984-023-01186-9","article-title":"A Systematic Review of the Applications of Markerless Motion Capture (MMC) Technology for Clinical Measurement in Rehabilitation","volume":"20","year":"2023","journal-title":"J. NeuroEng. Rehabil."},{"issue":"19","key":"2025102911043694100_bib3","doi-asserted-by":"crossref","first-page":"5687","DOI":"10.3390\/s20195687","article-title":"Motion Capture Technology in Industrial Applications: A Systematic Review","volume":"20","year":"2020","journal-title":"Sensors"},{"key":"2025102911043694100_bib4","doi-asserted-by":"crossref","first-page":"241","DOI":"10.47197\/retos.v0i32.56072","article-title":"Application of Motion Capture Technology for Sport Performance Analysis","volume":"32","year":"2017","journal-title":"Retos"},{"issue":"6","key":"2025102911043694100_bib5","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1080\/17461391.2018.1463397","article-title":"Accuracy of Human Motion Capture Systems for Sport Applications; State-of-the-Art Review","volume":"18","year":"2018","journal-title":"Eur. J. Sport Sci."},{"issue":"4","key":"2025102911043694100_bib6","first-page":"560","article-title":"Gait Analysis: Clinical Facts","volume":"52","year":"2016","journal-title":"Eur. J. Phys. Rehabil. Med."},{"issue":"3","key":"2025102911043694100_bib7","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/s20030673","article-title":"Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review","volume":"20","year":"2020","journal-title":"Sensors"},{"issue":"1","key":"2025102911043694100_bib8","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TAES.2023.3256973","article-title":"MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures","volume":"60","year":"2024","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"issue":"6","key":"2025102911043694100_bib9","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.1109\/JIOT.2017.2754578","article-title":"MoSense: An RF-Based Motion Detection System Via Off-the-Shelf WiFi Devices","volume":"4","year":"2017","journal-title":"IEEE Internet Things J."},{"issue":"5","key":"2025102911043694100_bib10","doi-asserted-by":"crossref","first-page":"3062","DOI":"10.1109\/TMC.2021.3133114","article-title":"WiTraj: Robust Indoor Motion Tracking With WiFi Signals","volume":"22","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"1","key":"2025102911043694100_bib11","doi-asserted-by":"crossref","first-page":"e0316119","DOI":"10.1371\/journal.pone.0316119","article-title":"Reliability of Artificial Intelligence-Driven Markerless Motion Capture in Gait Analyses of Healthy Adults","volume":"20","year":"2025","journal-title":"PLoS One"},{"issue":"10","key":"2025102911043694100_bib12","doi-asserted-by":"crossref","first-page":"e1011462","DOI":"10.1371\/journal.pcbi.1011462","article-title":"OpenCap: Human Movement Dynamics From Smartphone Videos","volume":"19","year":"2023","journal-title":"PLoS Comput. Biol."},{"key":"2025102911043694100_bib13","unstructured":"Jiang, T., Lu, P., Zhang, L., Ma, N., Han, R., Lyu, C., Li, Y., and Chen, K., 2023, \u201cRTMPose: Real-Time Multi-Person Pose Estimation Based on MMPose,\u201d arXiv:2303.07399.https:\/\/arxiv.org\/abs\/2303.07399"},{"issue":"1","key":"2025102911043694100_bib14","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","article-title":"OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields","volume":"43","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2025102911043694100_bib15","article-title":"Shape-From-Silhouette of Articulated Objects and Its Use for Human Body Kinematics Estimation and Motion Capture","volume-title":"Computer Society Conference on Computer Vision and Pattern Recognition,","year":"2003"},{"key":"2025102911043694100_bib16","doi-asserted-by":"crossref","first-page":"110665","DOI":"10.1016\/j.jbiomech.2021.110665","article-title":"Concurrent Assessment of Gait Kinematics Using Marker-Based and Markerless Motion Capture","volume":"127","year":"2021","journal-title":"J. Biomech."},{"key":"2025102911043694100_bib17","doi-asserted-by":"crossref","first-page":"111793","DOI":"10.1016\/j.jbiomech.2023.111793","article-title":"A Comparison of Three-Dimensional Kinematics Between Markerless and Marker-Based Motion Capture in Overground Gait","volume":"159","year":"2023","journal-title":"J. Biomech."},{"key":"2025102911043694100_bib18","doi-asserted-by":"crossref","first-page":"112200","DOI":"10.1016\/j.jbiomech.2024.112200","article-title":"Validation of OpenCap: A Low-Cost Markerless Motion Capture System for Lower-Extremity Kinematics During Return-to-Sport Tasks","volume":"171","year":"2024","journal-title":"J. Biomech."},{"issue":"11","key":"2025102911043694100_bib19","doi-asserted-by":"crossref","first-page":"e32078","DOI":"10.1016\/j.heliyon.2024.e32078","article-title":"Calibrationless Monocular Vision Musculoskeletal Simulation During Gait","volume":"10","year":"2024","journal-title":"Heliyon"},{"key":"2025102911043694100_bib20","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/978-3-031-29717-5","article-title":"Pose Estimation and Joint Angle Detection Using Mediapipe Machine Learning Solution","volume-title":"Applied Artificial Intelligence: Medicine, Biology, Chemistry, Financial, Games, Engineering","author":"Filipovic","year":"2023"},{"issue":"9","key":"2025102911043694100_bib21","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.3390\/s24092912","article-title":"Estimation of Shoulder Joint Rotation Angle Using Tablet Device and Pose Estimation Artificial Intelligence Model","volume":"24","year":"2024","journal-title":"Sensors"},{"issue":"1","key":"2025102911043694100_bib22","doi-asserted-by":"crossref","first-page":"20673","DOI":"10.1038\/s41598-021-00212-x","article-title":"The Accuracy of Several Pose Estimation Methods for 3D Joint Centre Localisation","volume":"11","year":"2021","journal-title":"Sci. Rep."},{"key":"2025102911043694100_bib23","first-page":"455","article-title":"Learning Delicate Local Representations for Multi-Person Pose Estimation","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Vedaldi","year":"2020"},{"key":"2025102911043694100_bib24","first-page":"183","article-title":"Mechanical Digital Twinning of the Human Body in the Workplace for Reduced Injury Risk and Improved Health","volume-title":"24th International Congress on Modelling and Simulation (ModSim2021)","year":"2021"},{"key":"2025102911043694100_bib25","first-page":"1","article-title":"Proof-of-Concept System Evaluation of Ergomechanic for Non-Invasive Estimation of Upper-Body Posture and Body Load Exposure in the Workplace","year":"2025","journal-title":"Int. J. Occup. Saf. Ergon."},{"key":"2025102911043694100_bib26","first-page":"197","article-title":"Ergomechanic: A Markerless Motion Capture and Ergonomic Assessment Tool Developed Using the Workspace Workflow Engine","volume-title":"24th International Congress on Modelling and Simulation (ModSim2021)","year":"2021"},{"issue":"8","key":"2025102911043694100_bib27","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1016\/S0021-9290(03)00087-3","article-title":"Repeatability of Gait Data Using a Functional Hip Joint Centre and a Mean Helical Knee Axis","volume":"36","year":"2003","journal-title":"J. Biomech."},{"key":"2025102911043694100_bib28","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.matcom.2019.11.011","article-title":"Workspace: A Workflow Platform for Supporting Development and Deployment of Modelling and Simulation","volume":"175","year":"2020","journal-title":"Math. Comput. Simul."},{"key":"2025102911043694100_bib29","article-title":"Workspace - a Scientific Workflow System for Enabling Research Impact","volume-title":"MODSIM 2017","year":"2017"},{"issue":"6","key":"2025102911043694100_bib30","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1109\/TBME.2025.3530848","article-title":"Marker Data Enhancement for Markerless Motion Capture","volume":"72","year":"2025","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"2025102911043694100_bib31","doi-asserted-by":"crossref","first-page":"2280","DOI":"10.1016\/j.patcog.2014.01.005","article-title":"Automatic Generation and Detection of Highly Reliable Fiducial Markers Under Occlusion","volume":"47","year":"2014","journal-title":"Pattern Recognit."},{"issue":"7","key":"2025102911043694100_bib32","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1016\/j.jbiomech.2010.01.002","article-title":"Non-Invasive Assessment of Soft-Tissue Artifact and Its Effect on Knee Joint Kinematics During Functional Activity","volume":"43","year":"2010","journal-title":"J. Biomech."},{"issue":"3","key":"2025102911043694100_bib33","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.jbiomech.2012.09.023","article-title":"Kinematic Differences Between Optical Motion Capture and Biplanar Videoradiography During a Jump\u2013Cut Maneuver","volume":"46","year":"2013","journal-title":"J. Biomech."},{"issue":"6","key":"2025102911043694100_bib34","doi-asserted-by":"crossref","first-page":"7157","DOI":"10.1109\/TPAMI.2022.3222784","article-title":"AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time","volume":"45","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"2025102911043694100_bib35","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1038\/s41593-018-0209-y","article-title":"DeepLabCut: Markerless Pose Estimation of User-Defined Body Parts With Deep Learning","volume":"21","year":"2018","journal-title":"Nat. Neurosci."},{"key":"2025102911043694100_bib36","article-title":"MediaPipe: A Framework Building Perception Pipelines","volume-title":"CVPR","year":"2019"},{"key":"2025102911043694100_bib37","first-page":"34","article-title":"Quality Evaluation of the Theia3D Markerless Motion Capture System for Measuring Football-Specific Movements","year":"2024"},{"issue":"11","key":"2025102911043694100_bib38","first-page":"120","article-title":"The openCV Library","volume":"25","year":"2000","journal-title":"Dr Dobbs J. Softw. Tools Prof. Program."},{"issue":"2","key":"2025102911043694100_bib39","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/0021-9290(74)90056-6","article-title":"Measurement and Reduction of Noise in Kinematics of Locomotion","volume":"7","year":"1974","journal-title":"J. Biomech."},{"issue":"10","key":"2025102911043694100_bib40","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1109\/TBME.2016.2586891","article-title":"Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait","volume":"63","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"11","key":"2025102911043694100_bib41","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TBME.2007.901024","article-title":"OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement","volume":"54","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"2","key":"2025102911043694100_bib42","doi-asserted-by":"crossref","first-page":"020905","DOI":"10.1115\/1.4029304","article-title":"Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement","volume":"137","year":"2015","journal-title":"ASME J. Biomech. Eng."},{"key":"2025102911043694100_bib43","unstructured":"Harrison, S. M., and Cohen, R. C., 2019, \u201cThe Bio-Mechanic Workspace Plugin: Enabling Biomechanical Measurement and Simulation Across Workflows and Software Applications,\u201d 23rd International Congress on Modelling and Simulation, Canberra ACT, Australia, pp. 442\u2013448.https:\/\/mssanz.org.au\/modsim2019\/D2\/harrison.pdf"},{"key":"2025102911043694100_bib44","unstructured":"King, D. E., 2009, \u201cDlib-ml: A Machine Learning Toolkit,\u201d J. Mach. Learn. Res., 10, pp. 1755\u20131758.https:\/\/www.jmlr.org\/papers\/volume10\/king09a\/king09a.pdf"},{"issue":"3","key":"2025102911043694100_bib45","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.gaitpost.2008.09.003","article-title":"The Reliability of Three-Dimensional Kinematic Gait Measurements: A Systematic Review","volume":"29","year":"2009","journal-title":"Gait Posture"},{"issue":"1","key":"2025102911043694100_bib46","doi-asserted-by":"crossref","first-page":"27","DOI":"10.11613\/BM.2021.010502","article-title":"Sample Size, Power and Effect Size Revisited: Simplified and Practical Approaches in Pre-Clinical, Clinical and Laboratory Studies","volume":"31","year":"2021","journal-title":"Biochem. Med."},{"issue":"11","key":"2025102911043694100_bib47","doi-asserted-by":"crossref","first-page":"3686","DOI":"10.3390\/s24113686","article-title":"Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems Versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis","volume":"24","year":"2024","journal-title":"Sensors"},{"issue":"7","key":"2025102911043694100_bib48","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1080\/10255842.2017.1295305","article-title":"Robustness of Kinematic Weighting and Scaling Concepts for Musculoskeletal Simulation","volume":"20","year":"2017","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"2025102911043694100_bib49","doi-asserted-by":"crossref","first-page":"112602","DOI":"10.1016\/j.jbiomech.2025.112602","article-title":"Validation of OpenCap on Lower Extremity Kinematics During Functional Tasks","volume":"183","year":"2025","journal-title":"J. Biomech."},{"key":"2025102911043694100_bib50","doi-asserted-by":"crossref","first-page":"112560","DOI":"10.1016\/j.jbiomech.2025.112560","article-title":"BIASMECHANICS: Does an Unconscious Bias Still Persist in Biomechanics, Positioning Males as the Default in Human Research? A Meta-Analysis on the Journal of Biomechanics 2024 Publications","volume":"181","year":"2025","journal-title":"J. Biomech."},{"issue":"6","key":"2025102911043694100_bib51","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/42.929617","article-title":"In Vivo Measurement of 3-D Skeletal Kinematics From Sequences of Biplane Radiographs: Application to Knee Kinematics","volume":"20","year":"2001","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Journal of Biomechanical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/biomechanical\/article-pdf\/147\/12\/121003\/7538644\/bio-25-1076.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/biomechanical\/article-pdf\/147\/12\/121003\/7538644\/bio-25-1076.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T15:04:52Z","timestamp":1761750292000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/biomechanical\/article\/147\/12\/121003\/1222345\/Evaluation-of-the-Ergomechanic-Markerless-Motion"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,28]]},"references-count":51,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4069821","relation":{},"ISSN":["0148-0731","1528-8951"],"issn-type":[{"value":"0148-0731","type":"print"},{"value":"1528-8951","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,28]]},"article-number":"121003"}}