The potential of human pose estimation for motion capture in sports: a validation study

Takashi Fukushima, Patrick Blauberger, Tiago Guedes Russomanno, Martin Lames

Research output: Contribution to journalArticlepeer-review

Abstract

Thanks to the advancement of computer vision technology and knowledge, the accuracy of human pose estimation has improved to the level that can be used for motion capture. Especially, human pose estimation has been gaining attention in research due to its efficiency and accuracy. The traditional motion capture system is not accessible to everyone. Human pose estimation could be a solution to replace the traditional system. However, the validity of human pose estimation has not been investigated enough yet in athletic and sports contexts. For this reason, this research aims to validate the kinematic measurements of human pose estimation by comparing them against the measurement of marker-based motion capture system. Five participants were recruited and asked to perform eight athletic and nine sports movements, respectively while being captured by normal and infrared cameras. Human pose estimation was run on frames from the RGB cameras to estimate human landmarks. From estimated landmarks in human pose estimation and marker-based motion capture system, elbow, shoulder, hip, and knee joint angles on the left and right sides were calculated and compared. Mean absolute error was used to evaluate the accuracy of human pose estimation measurements. The mean errors for athletic and sports movements were 9.7 ± 4.7 degrees and 9.0 ± 3.3 degrees, respectively. Errors were generally largest for elbow joint angles. The errors might be due to occlusion and systematic differences between human pose estimation and marker-based motion capture system. In conclusion, human pose estimation contains room for improvement, but has the potential to be used in some applications in which strictly precise measurements are not required.

Original languageEnglish
Article number19
JournalSports Engineering
Volume27
Issue number1
DOIs
StatePublished - Jun 2024

Keywords

  • Computer vision
  • Motion capture
  • OpenPose
  • Validation

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