Multibody System Dynamics

, Volume 39, Issue 3, pp 175–195 | Cite as

Prediction of ground reaction forces and moments during sports-related movements

  • Sebastian Skals
  • Moon Ki Jung
  • Michael Damsgaard
  • Michael S. AndersenEmail author


When performing inverse dynamic analysis (IDA) of musculoskeletal models to study human motion, inaccuracies in experimental input data and a mismatch between the model and subject lead to dynamic inconsistency. By predicting the ground reaction forces and moments (GRF&Ms) this inconsistency can be reduced and force plate measurements become unnecessary. In this study, a method for predicting GRF&Ms was validated for an array of sports-related movements. The method was applied to ten healthy subjects performing, for example, running, a side-cut manoeuvre, and vertical jump. Pearson’s correlation coefficient (\(r\)) and root-mean-square deviation were used to compare the predicted GRF&Ms and associated joint kinetics to the traditional IDA approach, where the GRF&Ms were measured using force plates. The main findings were that the method provided estimates comparable to traditional IDA across all movements for vertical GRFs (\(r\) ranging from 0.97 to 0.99, median 0.99), joint flexion moments (\(r\) ranging from 0.79 to 0.98, median 0.93), and resultant joint reaction forces (\(r\) ranging from 0.78 to 0.99, median 0.97). Considering these results, this method can be used instead of force plate measurements, hereby, facilitating IDA in sports science research and enabling complete IDA using motion analysis systems that do not incorporate force plate data.


Musculoskeletal model Inverse dynamics Sports science AnyBody Modeling System Force plates 

Inverse dynamic analysis


Ground reaction forces and moments


Acceleration from a standing position


AnyBody Modeling System




Ground reaction force


Ground reaction moment


Ankle flexion moment


Ankle subtalar eversion moment


Knee flexion moment


Hip flexion moment


Hip abduction moment


Hip external rotation moment


Joint reaction force


Pearson’s correlation coefficient


Root-mean-square deviation


Right leg


Left leg



This work received funding from the Danish Council for Independent Research under grant number DFF-4184-00018 to M.S. Andersen and from the European Union’s Seventh Framework Programme (FP7/2007–2013) under the LifeLongJoints Project, Grant Agreement no. GA-310477 to M. Jung and M. Damsgaard.

Conflict of Interest

M. Damsgaard is the head of development, minority shareholder, and member of the board of directors of AnyBody Technology A/S that owns and sells the AnyBody Modeling System.

Supplementary material

11044_2016_9537_MOESM1_ESM.docx (3.2 mb)
(DOCX 3.2 MB)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Sebastian Skals
    • 1
    • 2
  • Moon Ki Jung
    • 3
  • Michael Damsgaard
    • 3
  • Michael S. Andersen
    • 4
    Email author
  1. 1.National Research Centre for the Working EnvironmentCopenhagen EastDenmark
  2. 2.Department of Health Science and TechnologyAalborg UniversityAalborg EastDenmark
  3. 3.AnyBody Technology A/SAalborg EastDenmark
  4. 4.Department of Mechanical and Manufacturing EngineeringAalborg UniversityAalborg EastDenmark

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