ArticleSensors (Basel, Switzerland)2021
Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics-Part 1: Robustness.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
19 citing papers in PubMed, 79 citations in OpenAlex.
- Contact Depth Affects Most of the Other Hitting Metrics We Analyze.Sports (Basel, Switzerland) · 2026Article
- Functional Profiling in Paralympic Water Polo Using Deep Learning, Stereo Vision, and Phase-Based Kinematic Analysis: A Pilot Study.Bioengineering (Basel, Switzerland) · 2026Article
- Validation of a Markerless Multi-Camera Pipeline for Bouldering Fall Kinematics.Sensors (Basel, Switzerland) · 2026Article
- Neuromuscular strength, not sagittal-plane joint or modeled muscle-tendon kinematics, is associated with 50-m sprint performance in youth male sprinters.Frontiers in physiology · 2026Article
- Comparison of deep learning-based three-dimensional human pose estimation methods with motion capture for gesture research.PloS one · 2026Article
- Patient-Perceived feasibility of implementing OpenCap smartphone-based motion capture technology to remotely assess musculoskeletal movement in individuals with orthopedic disorders presenting to a rural midwestern tertiary center.Frontiers in digital health · 2026Article
- Generating synthetic images of human skeletal motion for pose and kinematics estimation tasks.Scientific data · 2025Article
- A Confidence-Based Multibody Kinematics Optimization for Markerless Motion Capture: A Proof of Concept.International journal for numerical methods in biomedical engineering · 2025Article
- Efficient Markerless Motion Classification Using Radar.Sensors (Basel, Switzerland) · 2025Article
- The influence of the marker set on inverse kinematics results to inform markerless motion capture annotations.Scientific reports · 2025Article
- Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments.Scientific reports · 2024Article
- Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Validation of portable in-clinic video-based gait analysis for prosthesis users.Scientific reports · 2024Article
- Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation.Sensors (Basel, Switzerland) · 2023Article
- Towards Single Camera Human 3D-Kinematics.Sensors (Basel, Switzerland) · 2022Article
- Ubiquitous Computing in Sports and Physical Activity-Recent Trends and Developments.Sensors (Basel, Switzerland) · 2022Article
- Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method.Biosensors · 2022Article
- Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics-Part 2: Accuracy.Sensors (Basel, Switzerland) · 2022Article
- A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport.Frontiers in sports and active living · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Being able to capture relevant information about elite athletes' movement "in the wild" is challenging, especially because reference marker-based approaches hinder natural movement and are highly sensitive to environmental conditions. We propose Pose2Sim, a markerless kinematics workflow that uses OpenPose 2D pose detections from multiple views as inputs, identifies the person of interest, robustly triangulates joint coordinates from calibrated cameras, and feeds those to a 3D inverse kinematic full-body OpenSim model in order to compute biomechanically congruent joint angles. We assessed the robustness of this workflow when facing simulated challenging conditions: (Im) degrades image quality (11-pixel Gaussian blur and 0.5 gamma compression); (4c) uses few cameras (4 vs. 8); and (Cal) introduces calibration errors (1 cm vs. perfect calibration). Three physical activities were investigated: walking, running, and cycling. When averaged over all joint angles, stride-to-stride standard deviations lay between 1.7° and 3.2° for all conditions and tasks, and mean absolute errors (compared to the reference condition-Ref) ranged between 0.35° and 1.6°. For walking, errors in the sagittal plane were: 1.5°, 0.90°, 0.19° for (Im), (4c), and (Cal), respectively. In conclusion, Pose2Sim provides a simple and robust markerless kinematics analysis from a network of calibrated cameras.
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Registered trials
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.