The effects of ArUco marker velocity and size on motion capture detection and accuracy in the context of human body kinematics analysis

  • 1 Institute of Machine Design, Faculty of Mechanical Engineering, Poznan University of Technology
  • 2 Institute of Mechanical Technology, Faculty of Mechanical Engineering, Poznan University of Technology
  • 3 Institute of Machine Design, Faculty of Mechanical Engineering, Poznan University of Technology

Abstract

The research aim was to analyse the influence of velocity and size of markers on the accuracy of motion capture measurement utilising image processing with the use of OpenCV. On the basis of the obtained results, the usefulness of the applied measurement method in studying the kinematics of the human body while driving operating a wheelchair was determined. This article presents the test results for a low-budget motion capture measurement system for testing the kinematics of the human body in a single plane. The tested measuring system includes a standard activity camera Xiaomi Yi4K, expanded polystyrene markers with printed ArUco codes, and original software for marker position detection developed by the author. The analysis of the measurement method with regard to its applicability in biomechanical studies has highlighted several key factors: the number of measuring points, measurement accuracy expressed as a relative error and the limit velocity at which the marker trajectory is correctly represented. The article shows that the limit velocity of the marker is 2.2 m/s for 50x50 mm markers and 1.4 m/s for 30x30 mm markers. The number of measured points ranged from 233 to 2,457 depending on the marker velocity. The relative error did not exceed 5% for the marker velocities and thus provided a correct representation of its trajectory.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Arnet, U., van Drongelen, S., Scheel-Sailer, A., van der Woude, L.H., Veeger, D.H. (2012). Shoulder load during synchronous handcycling and handrim wheelchair propulsion in persons with paraplegia. Journal of rehabilitation medicine, 44(3), 222–228.

  • Baran, K. (2018). Rozpoznawanie emocji za pomocą technologii: elektroencefalografia (EEG), motion capture i wirtualna rzeczywistość (VR). Prace doktorantów Wydziału Elektrotechniki i Informatyki Politechniki Lubelskiej, Wydawnictwo Politechniki Lubelskiej, Lublin 2018, 32–46.

  • Boninger, M.L., Cooper, R.A., Shimada, S.D., Rudy T.E. (1998). Shoulder and elbow motion during two speeds of wheelchair propulsion: a description using a local coordinate system. Spinal cord, 36(6), 418–426.

  • Carse, B., Meadows, B., Bowers, R., Rowe, P. (2013). Affordable clinical gait analysis: An assessment of the marker tracking accuracy of a new low-cost optical 3D motion analysis system. Physiotherapy, 99(4), 347–351.

  • Chow, J.W., Chae, W.S., Crawford, M.J. (2000). Kinematic analysis of shot-putting performed by wheelchair athletes of different medical classes. Journal of Sports Sciences, 18(5), 321–330.

  • Crespo-Ruiz, B.M., Del Ama-Espinosa, A.J., Gil-Agudo, Á.M. (2011). Relation between kinematic analysis of wheelchair propulsion and wheelchair functional basketball classification. Adapted physical activity quarterly, 28(2), 157–172.

  • Głodzik, J., Krężałek, P., Strój, E., Przybytek, M., Szczygieł, E., Hładki, W. (2017). Ocena zaburzeń chodu z wykorzystaniem analizy komputerowej BTS-SMART. Ostry Dyżur, 10(4).

  • Hachaj, T., Ogiela, M.R., Koptyra, K. (2018). Human actions recognition from motion capture recordings using signal resampling and pattern recognition methods. Annals of Operations Research, 265(2), 223–239.

  • Hughes, C.J., Weimar, W.H., Sheth, P.N., Brubaker, C.E. (1992). Biomechanics of wheelchair propulsion as a function of seat position and user-to-chair interface. Archives of physical medicine and rehabilitation, 73(3), 263–269.

  • Jóźwiak, P., Jaśkowski, B.M., Jóźwiak, A., Kosek, W., Knapkiewicz, P., Jakowski, J.M. (2014). Kinematyczna ocena ruchu konia. Med. Weter, 70(1), 30–35.

  • Kamiński, M., Kopniak, P., Zyla, K. (2014). Zdalne sterowanie ramieniem robota z wykorzystaniem inercyjnych czujników rejestracji ruchu. Logistyka, 6, 5168–5177.

  • Kania, E., Głowacka-Kwiecień, A., Jochymczyk, K., Jureczko, P. (2008). Badania doświadczalne chodu dzieci zdrowych. Aktualne Problemy Biomechaniki, 2.

  • Kopniak, P. (2012). Rejestracja ruchu za pomocą urządzenia Microsoft Kinect. Pomiary Automatyka Kontrola, 58(11), 1016–1018.

  • Kopniak, P. (2014). Pomiary kątów ugięcia kończyny w stawie z wykorzystaniem inercyjnego systemu Motion Capture. Pomiary Automatyka Kontrola, 60(8).

  • Skublewska-Paszkowska, M., Łukasik, E., Smołka, J. (2015). Wykorzystanie systemu akwizycji ruchu do badania kierowców. Logistyka, 4, 5678–5684.

  • Skublewska-Paszkowska, M., Montusiewicz, J., Łukasik, E., Pszczoła-Pasierbiewicz, I., Baran, K.R., Smołka, J., Pueo, B. (2016). Motion capture as a modern technology for analysing ergometer rowing. Advances in Science and Technology Research Journal, 10(29).

  • Vanlandewijck, Y., Theisen, D., Daly, D. (2001). Wheelchair propulsion biomechanics. Sports medicine, 31(5), 339–367.

  • Wu, G., van der Helm, F.C.T, Veeger, H.E.J., Makhsous, M., Van Roy, P., Anglin, C., Nagels, J., Karduna, A.R., McQuade, K., Wang, X., Werner, F.W., Buchholz, B. (2005). ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion–Part II: Shoulder, elbow, wrist and hand. Journal of Biomechanics, 38, 981–992.

OPEN ACCESS

Journal + Issues

Search