Multibody System Dynamics

, Volume 41, Issue 4, pp 297–316 | Cite as

A musculoskeletal model driven by dual Microsoft Kinect Sensor data

  • Sebastian Skals
  • Kasper P. Rasmussen
  • Kaare M. Bendtsen
  • Jian Yang
  • Michael S. Andersen
Article
  • 307 Downloads

Abstract

Musculoskeletal modeling is becoming a standard method to estimate muscle, ligament and joint forces non-invasively. As input, these models often use kinematic data obtained using marker-based motion capture, which, however, is associated with several limitations, such as soft tissue artefacts and the time-consuming task of attaching markers. These issues can potentially be addressed by applying marker-less motion capture. Therefore, we developed a musculoskeletal model driven by marker-less motion capture data, based on two Microsoft Kinect Sensors and iPi Motion Capture software, which incorporated a method for predicting ground reaction forces and moments. For validation, selected model outputs (e.g. ground reaction forces, joint reaction forces, joint angles and joint range-of-motion) were compared to musculoskeletal models driven by simultaneously recorded marker-based motion capture data from 10 males performing gait and shoulder abduction with and without external load. The primary findings were that the vertical ground reaction force during gait and the shoulder abduction/adduction angles, glenohumeral joint reaction forces and deltoideus forces during both shoulder abduction tasks showed comparable results. In addition, shoulder abduction/adduction range-of-motions were not significantly different between the two systems. However, the lower extremity joint angles, moments and reaction forces showed discrepancies during gait with correlations ranging from weak to strong, and for the majority of the variables, the marker-less system showed larger standard deviations. Although discrepancies between the systems were identified, the marker-less system shows potential, especially for tracking simple upper-body movements.

Keywords

Marker-less motion capture Microsoft Kinect Sensor iPi Motion Capture Ground reaction force prediction Musculoskeletal modeling AnyBody Modeling System 
MBS

Marker-based system

MKS

Microsoft Kinect Sensor

ROM

Range-of-motion

MGRF

Measured ground reaction force

PGRF

Predicted ground reaction force

MLS

Marker-less system

PCSA

Physiological cross-sectional area

SA

Unloaded shoulder abduction

LSA

Loaded shoulder abduction

GRF

Ground reaction force

AMS

AnyBody Modeling System

JRF

Joint reaction force

\(r\)

Pearson’s correlation coefficient

RMSD

Root-mean-square deviation

Notes

Acknowledgements

This work was supported by the Danish Agency for Science, Technology and Innovation under the Patient@Home project to M. S. Andersen and J. Yang and the Danish Council for Independent Research under grant number DFF-4184-00018 to M. S. Andersen.

Supplementary material

11044_2017_9573_MOESM1_ESM.docx (688 kb)
<Sup_Figure1 - Marker protocol> (DOCX 688 kB)
11044_2017_9573_MOESM2_ESM.docx (444 kb)
<Sup_Figure2 - Virtual marker placement> (DOCX 444 kB)

References

  1. 1.
    Mellon, S.J., Grammatopoulos, G., Andersen, M.S., Pegg, E.C., Pandit, H.G., Murray, D.W., Gill, H.S.: Individual motion patterns during gait and sit-to-stand contribute to edge-loading risk in metal-on-metal hip resurfacing. Proc. Inst. Mech. Eng., H 227, 799–810 (2013) CrossRefGoogle Scholar
  2. 2.
    Alkjær, T., Wieland, M.R., Andersen, M.S., Simonsen, E.B., Rasmussen, J.: Computational modeling of a forward lunge: towards a better understanding of the function of the cruciate ligaments. J. Anat. 221, 590–597 (2012) CrossRefGoogle Scholar
  3. 3.
    Weber, T., Dendorfer, S., Dullien, S., Grifka, J., Verkerke, G.J., Renkawitz, T.: Measuring functional outcome after total hip replacement with subject-specific hip joint loading. Proc. Inst. Mech. Eng., H 226, 939–946 (2012) CrossRefGoogle Scholar
  4. 4.
    Cappozzo, A., Della Croce, U., Leardini, A., Chiari, L.: Human movement analysis using stereophotogrammetry. Part 1: theoretical background. Gait Posture 21, 186–196 (2005) Google Scholar
  5. 5.
    Bonnechère, B., Jansen, B., Salvia, P., Bouzahouene, H., Omelina, L., Moiseev, F., Sholukha, V., Cornelis, J., Rooze, M., Van Sint Jan, S.: Validity and reliability of the Kinect within functional assessment activities: comparison with standard stereophotogrammetry. Gait Posture 39, 593–598 (2014) CrossRefGoogle Scholar
  6. 6.
    Herda, L., Fua, P., Plänkers, R., Boulic, R., Thalmann, D.: Using skeleton-based tracking to increase the reliability of optical motion capture. Hum. Mov. Sci. 20, 313–341 (2001) CrossRefGoogle Scholar
  7. 7.
    Dutta, T.: Evaluation of the KinectTM sensor for 3-D kinematic measurement in the workplace. Appl. Ergon. 43, 645–649 (2012) CrossRefGoogle Scholar
  8. 8.
    Andersen, M.S., Damsgaard, M., MacWilliams, B., Rasmussen, J.: A computationally efficient optimisation-based method for parameter identification of kinematically determinate and over-determinate biomechanical systems. Comput. Methods Biomech. Biomed. Eng. 13, 171–183 (2010) CrossRefGoogle Scholar
  9. 9.
    Stagni, R., Fantozzi, S., Capello, A., Leardini, A.: Quantification of soft tissue artefact in motion analysis by combining 3D fluoroscopy and stereophotogrammetry: a study on two subjects. Clin. Biomech. 20, 320–329 (2005) CrossRefGoogle Scholar
  10. 10.
    Clark, R.A., Pua, Y.-H., Fortin, K., Ritchie, C., Webster, K.E., Denehy, L., Bryant, A.L.: Validity of the Microsoft Kinect for assessment of postural control. Gait Posture 36, 372–377 (2012) CrossRefGoogle Scholar
  11. 11.
    Clark, R.A., Bower, K.J., Mentiplay, B.F., Paterson, K., Pua, Y.-H.: Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables. J. Biomech. 46, 2722–2725 (2013) CrossRefGoogle Scholar
  12. 12.
    Clark, R.A., Pua, Y.-H., Bryant, A.L., Hunt, M.A.: Validity of the Microsoft Kinect for providing lateral trunk lean feedback during gait retraining. Gait Posture 38, 1064–1066 (2013) CrossRefGoogle Scholar
  13. 13.
    Choppin, S., Wheat, J.: Marker-less tracking of human movement using Microsoft Kinect. In: Bradshaw, E.J., Burnett, A., Hume, P.A. (eds.) Proceedings of the 30th Annual Conference of Biomechanics in Sports, Melbourne (2012) Google Scholar
  14. 14.
    Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Efficient model-based 3D tracking of hand articulations using Kinect. In: Hoey, J., McKenna, S., Trucco, E. (eds.) Proceedings of the 22nd British Machine Vision Conference, Dundee (2011) Google Scholar
  15. 15.
    Xia, L., Chen, C.-C., Aggarwal, J.K.: Human detection using depth information by Kinect. In: Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on, Colorado Springs, CO, US, 20–25 June 2011 Google Scholar
  16. 16.
    Khoshelham, K., Elberink, S.O.: Accuracy and resolution of Kinect depth data for indoor mapping applications. Sensors 12, 1437–1454 (2012) CrossRefGoogle Scholar
  17. 17.
    Horsman, M.D.K., Koopman, H.F.J.M., van der Helm, F.C.T., Prosé, L.P., Veeger, H.E.J.: Morphological muscle and joint parameters for musculoskeletal modelling of the lower extremity. Clin. Biomech. 22, 239–247 (2007) CrossRefGoogle Scholar
  18. 18.
    de Zee, M., Hansen, L., Wong, C., Rasmussen, J., Simonsen, E.B.: A generic detailed rigid-body lumbar spine model. J. Biomech. 40, 1219–1227 (2007) CrossRefGoogle Scholar
  19. 19.
    Veeger, H.E.J., van der Helm, F.C.T., van der Woude, L.H.V., Pronk, G.M., Rozendal, R.H.: Inertia and muscle contraction parameters for musculoskeletal modelling of the shoulder mechanism. J. Biomech. 24, 615–629 (1991) CrossRefGoogle Scholar
  20. 20.
    Veeger, H.E.J., Yu, B., An, K.-N., Rozendal, R.H.: Parameters for modeling the upper extremity. J. Biomech. 30, 647–652 (1997) CrossRefGoogle Scholar
  21. 21.
    Van der Helm, F.C., Veeger, H.E., Pronk, G.E., Van der Woude, L.H., Rozendal, R.H.: Geometry parameters for musculoskeletal modeling of the shoulder system. J. Biomech. 25, 129–144 (1992) CrossRefGoogle Scholar
  22. 22.
    Rasmussen, J., de Zee, M., Damsgaard, M., Christensen, S.T., Marek, C., Siebertz, K.: A general method for scaling musculoskeletal models. In: Proceedings of the 10th International Symposium on Computer Simulation in Biomechanics, 28–30 July 2005, Cleveland, OH, US Google Scholar
  23. 23.
    Winter, D.A.: Biomechanics and Motor Control of Human Movement. John Wiley & Sons, Hoboken (2005) Google Scholar
  24. 24.
    Damsgaard, M., Rasmussen, J., Christensen, S.T., Surma, E., de Zee, M.: Analysis of musculoskeletal systems in the AnyBody Modeling System. Simul. Model. Pract. Theory 14, 1100–1111 (2006) CrossRefGoogle Scholar
  25. 25.
    Marra, M.A., Vanheule, V., Fluit, R., Koopman, B.H.F.J.M., Rasmussen, J., Verdonschot, N., Andersen, M.S.: A subject-specific musculoskeletal modeling framework to predict in vivo mechanics of total knee arthroplasty. J. Biomech. Eng. 137, 020904 (2015) CrossRefGoogle Scholar
  26. 26.
    Frankenfield, D.C., Rowe, W.A., Cooney, R.N., Smith, J.S., Becker, D.: Limits of body mass index to detect obesity and predict body composition. Nutrition 17, 26–30 (2001) CrossRefGoogle Scholar
  27. 27.
    Andersen, M.S., Damsgaard, M., Rasmussen, J.: Kinematic analysis of over-determinate biomechanical systems. Comput. Methods Biomech. Biomed. Eng. 12, 371–384 (2009) CrossRefGoogle Scholar
  28. 28.
    Skals, S., Jung, M.K., Damsgaard, M., Andersen, M.S.: Prediction of ground reaction forces and moments during sports-related movements. Multibody Syst. Dyn. 39, 175–195 (2017) CrossRefGoogle Scholar
  29. 29.
    Fluit, R., Andersen, M.S., Kolk, S., Verdonschot, N., Koopman, H.F.J.M.: Prediction of ground reaction forces and moments during various activities of daily living. J. Biomech. 47, 2321–2329 (2014) CrossRefGoogle Scholar
  30. 30.
    Taylor, R.: Interpretation of the correlation coefficient: a basic review. J. Diagn. Med. Sonog. 6(1), 35–39 (1990) CrossRefGoogle Scholar
  31. 31.
    Benoit, D.L., Ramsey, D.K., Lamontagne, M., Xu, L., Wretenberg, P., Renström, P.: Effect of skin movement artifact on knee kinematics during gait and cutting motions measured in vivo. Gait Posture 24, 152–164 (2006) CrossRefGoogle Scholar
  32. 32.
    Barre, A., Thiran, J.-P., Jolles, B.M., Theumann, N., Aminian, K.: Soft tissue artifact assessment during treadmill walking in subjects with total knee arthroplasty. IEEE Trans. Biomed. Eng. 60, 3131–3140 (2013) CrossRefGoogle Scholar
  33. 33.
    Sandau, M., Koblauch, H., Moeslund, T.B., Aanæs, H., Alkjær, T., Simonsen, E.B.: Markerless motion capture can provide reliable 3D gait kinematics in the sagittal and frontal plane. Med. Eng. Phys. 36, 1168–1175 (2014) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Sebastian Skals
    • 1
  • Kasper P. Rasmussen
    • 2
  • Kaare M. Bendtsen
    • 3
  • Jian Yang
    • 4
  • Michael S. Andersen
    • 4
  1. 1.National Research Centre for the Working EnvironmentCopenhagen EastDenmark
  2. 2.AnyBody Technology A/SAalborg EastDenmark
  3. 3.Department of Health Science and TechnologyAalborg UniversityAalborg EastDenmark
  4. 4.Department of Mechanical and Manufacturing EngineeringAalborg UniversityAalborg EastDenmark

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