ArticleSensors (Basel, Switzerland)2022
Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics-Part 2: Accuracy.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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.
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Who cites it
22 citing papers in PubMed.
- 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
- IMU- and Vision-Based Measurement Techniques for Joint Kinematics: A Narrative Review.Sensors (Basel, Switzerland) · 2026Review
- Functional Profiling in Paralympic Water Polo Using Deep Learning, Stereo Vision, and Phase-Based Kinematic Analysis: A Pilot Study.Bioengineering (Basel, Switzerland) · 2026Article
- Joint torque estimation from daily living motion for passive sarcopenia monitoring in older adults.Journal of neuroengineering and rehabilitation · 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
- Marker Data Enhancement for Markerless Motion Capture.IEEE transactions on bio-medical engineering · 2025Article
- Use of Technologies for the Acquisition and Processing Strategies for Motion Data Analysis.Biomimetics (Basel, Switzerland) · 2025Review
- The influence of the marker set on inverse kinematics results to inform markerless motion capture annotations.Scientific reports · 2025Article
- Impact of Running Clothes on Accuracy of Smartphone-Based 2D Joint Kinematic Assessment During Treadmill Running Using OpenPifPaf.Sensors (Basel, Switzerland) · 2025Article
- Influence of the Camera Viewing Angle on OpenPose Validity in Motion Analysis.Sensors (Basel, Switzerland) · 2025Article
- Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces.Medical & biological engineering & computing · 2024Article
- Review
- Marker Data Enhancement For Markerless Motion Capture.bioRxiv : the preprint server for biology · 2024Article
- Obesity-Specific Considerations for Assessing Gait with Inertial Measurement Unit-Based vs. Optokinetic Motion Capture.Sensors (Basel, Switzerland) · 2024Article
- A review of combined functional neuroimaging and motion capture for motor rehabilitation.Journal of neuroengineering and rehabilitation · 2024Review
- Effect of Foot Orthoses on Angular Velocity of Feet.Sensors (Basel, Switzerland) · 2023Article
- OpenCap: Human movement dynamics from smartphone videos.PLoS computational biology · 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
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Two-dimensional deep-learning pose estimation algorithms can suffer from biases in joint pose localizations, which are reflected in triangulated coordinates, and then in 3D joint angle estimation. Pose2Sim, our robust markerless kinematics workflow, comes with a physically consistent OpenSim skeletal model, meant to mitigate these errors. Its accuracy was concurrently validated against a reference marker-based method. Lower-limb joint angles were estimated over three tasks (walking, running, and cycling) performed multiple times by one participant. When averaged over all joint angles, the coefficient of multiple correlation (CMC) remained above 0.9 in the sagittal plane, except for the hip in running, which suffered from a systematic 15° offset (CMC = 0.65), and for the ankle in cycling, which was partially occluded (CMC = 0.75). When averaged over all joint angles and all degrees of freedom, mean errors were 3.0°, 4.1°, and 4.0°, in walking, running, and cycling, respectively; and range of motion errors were 2.7°, 2.3°, and 4.3°, respectively. Given the magnitude of error traditionally reported in joint angles computed from a marker-based optoelectronic system, Pose2Sim is deemed accurate enough for the analysis of lower-body kinematics in walking, cycling, and running.
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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.