A musculoskeletal model driven by dual Microsoft Kinect Sensor data


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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7



Marker-based system


Microsoft Kinect Sensor




Measured ground reaction force


Predicted ground reaction force


Marker-less system


Physiological cross-sectional area


Unloaded shoulder abduction


Loaded shoulder abduction


Ground reaction force


AnyBody Modeling System


Joint reaction force

\(r\) :

Pearson’s correlation coefficient


Root-mean-square deviation


  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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google Scholar 

  7. 7.

    Dutta, T.: Evaluation of the KinectTM sensor for 3-D kinematic measurement in the workplace. Appl. Ergon. 43, 645–649 (2012)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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

  16. 16.

    Khoshelham, K., Elberink, S.O.: Accuracy and resolution of Kinect depth data for indoor mapping applications. Sensors 12, 1437–1454 (2012)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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

  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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google Scholar 

  30. 30.

    Taylor, R.: Interpretation of the correlation coefficient: a basic review. J. Diagn. Med. Sonog. 6(1), 35–39 (1990)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google Scholar 

Download references


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.

Author information



Corresponding author

Correspondence to Michael S. Andersen.

Electronic Supplementary Material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Skals, S., Rasmussen, K.P., Bendtsen, K.M. et al. A musculoskeletal model driven by dual Microsoft Kinect Sensor data. Multibody Syst Dyn 41, 297–316 (2017). https://doi.org/10.1007/s11044-017-9573-8

Download citation


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