ArticleScientific reports2021
The accuracy of several pose estimation methods for 3D joint centre localisation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
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Who cites it
70 citing papers in PubMed, 1 synthesis or guideline pooled it, 163 citations in OpenAlex.
- Validations and applications of markerless motion capture using OpenCap: a scoping review.Frontiers in digital health · 2026Pooled it
- Validation of a Deep Learning-Based Markerless Video System for Gait Speed Assessment During the 10-Meter Walk Test in Outdoor Clinical Settings.Journal of functional morphology and kinesiology · 2026Article
- Toward Automation of Motor Assessments in Stroke: Proof-of-Concept Model to Interpret Balance and Gait in Patients With Stroke.Archives of rehabilitation research and clinical translation · 2026Article
- Comparison of Joint Kinematics and Spatiotemporal Gait Parameters Between Markerless and Marker-Based Motion Capture in a Large Knee Osteoarthritis Cohort.Annals of biomedical engineering · 2026Article
- A Joint-Level Hybrid Framework for Gait Analysis Using Camera-IMU Fusion and LSTM-Based Temporal Correction.Sensors (Basel, Switzerland) · 2026Article
- Intra-Rater and Test-Retest Reliability of Kinovea for the Kinematic Analysis of Squatting in Healthy Active Women.Sensors (Basel, Switzerland) · 2026Article
- The Clinician's Guide to Computer Vision in the Motion Capture Galaxy.Restorative neurology and neuroscience · 2026Article
- Automated Gait Assessment for Rehabilitation Training Using Pose Tracking and Dynamic Time Warping.Diagnostics (Basel, Switzerland) · 2026Article
- Concurrent validation of OpenCap for identifying ACL re-injury risk factors during a drop jump test in a healthy cohort.Scientific reports · 2026Article
- Evaluating a Multi-Camera Markerless System for Capturing Basketball-Specific Movements: An Exploration Using 25 Hz Video Streams.Sensors (Basel, Switzerland) · 2026Article
- Verification of Markerless Gait Analysis: Multi-Camera and Single-Camera Approaches in Comparison to Marker-Based Gait Analysis.Medicina (Kaunas, Lithuania) · 2026Article
- Video-Based Motion Capture Smartphone Apps for Testing Human Motor Performance Skills: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Synchronization of OpenCap with Force Platforms: Validation of an Event-Based Algorithm.Sensors (Basel, Switzerland) · 2026Article
- Validating Single-Camera Pose Estimation Against Multi-Camera Motion Capture for Accessible Biomechanical Assessment.IEEE access : practical innovations, open solutions · 2026Article
- Geometry-informed correction of projection bias in browser-based monocular squat assessment.Frontiers in sports and active living · 2026Article
- Comparison of deep learning-based three-dimensional human pose estimation methods with motion capture for gesture research.PloS one · 2026Article
- Influence of camera geometry on 3D joint angle estimation in markerless motion capture.Frontiers in sports and active living · 2026Article
- Generating synthetic images of human skeletal motion for pose and kinematics estimation tasks.Scientific data · 2025Article
- 3D pose estimation for scalable remote gait kinematics assessment.NPJ digital medicine · 2025Article
- Assessing single camera markerless motion capture with OpenSim inverse kinematics during upper limb activities of daily living.International biomechanics · 2025Article
10 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human movement researchers are often restricted to laboratory environments and data capture techniques that are time and/or resource intensive. Markerless pose estimation algorithms show great potential to facilitate large scale movement studies 'in the wild', i.e., outside of the constraints imposed by marker-based motion capture. However, the accuracy of such algorithms has not yet been fully evaluated. We computed 3D joint centre locations using several pre-trained deep-learning based pose estimation methods (OpenPose, AlphaPose, DeepLabCut) and compared to marker-based motion capture. Participants performed walking, running and jumping activities while marker-based motion capture data and multi-camera high speed images (200 Hz) were captured. The pose estimation algorithms were applied to 2D image data and 3D joint centre locations were reconstructed. Pose estimation derived joint centres demonstrated systematic differences at the hip and knee (~ 30-50 mm), most likely due to mislabeling of ground truth data in the training datasets. Where systematic differences were lower, e.g., the ankle, differences of 1-15 mm were observed depending on the activity. Markerless motion capture represents a highly promising emerging technology that could free movement scientists from laboratory environments but 3D joint centre locations are not yet consistently comparable to marker-based motion capture.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.