Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy

David Pagnon, Mathieu Domalain, Lionel Reveret

Research output: Contribution to journalArticlepeer-review

11 Citations (SciVal)


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.

Original languageEnglish
Article number2712
Issue number7
Publication statusPublished - 1 Apr 2022

Bibliographical note

Funding Information:
Funding: This research has received funding from CNRS (Doctoral Thesis 2019), ANR Equipex PIA 2011 (project Kinovis), and ANR PPR STHP 2020 (project PerfAnalytics, ANR 20-STHP-0003).


  • accuracy
  • computer vision
  • concurrent validity
  • deep learning
  • kinematics
  • markerless motion capture
  • OpenPose
  • OpenSim
  • sports performance analysis

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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