Musculoskeletal modeling of human lower limb during normal walking, one-legged forward hopping and side jumping: Comparison of measured EMG and predicted muscle activity patterns

A. D. Wibawa*, N. Verdonschot, J. P.K. Halbertsma, J. G.M. Burgerhof, R. L. Diercks, G. J. Verkerke

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

This study focused on comparing muscle activities predicted by the Musculoskeletal Modeling System with EMG from ten healthy subjects who performed normal walking, one-legged forward hopping and side jumping. Eight EMG electrodes measured the activity of eight right leg muscles. Specific thresholds per muscle were applied on the EMG prior comparison. These thresholds were determined by equalizing the duration of EMG to AMS muscle activity. Three graph variables, number of onsets, offsets and hills were used to quantify the level of agreement by using Cohen׳s kappa analysis. The Pearson correlation coefficient was also calculated as a result comparison. Overall, visual inspection showed comparable activity patterns. However, when quantifying them some differences became apparent. The mean level of agreement of all tests was <0.20, meaning poor agreement. Pearson correlation showed better agreement compared to kappa analysis. In general, a more prescribed motion like FH and SJ showed a better agreement than NW. This explorative study shows that there are distinct differences between the model and EMG pattern. Those differences can be attributed to inevitable modeling limitation within the AMS framework like miscalculating the knee net moment, absence of co-contraction, simplified knee joint. Moreover, the delay between EMG and AMS has a clear effect on the comparison and this delay is obviously missing in the model. Despite those differences, this study can serve as a baseline measurement allowing progress in scientific work in order to reduce uncertainties with the aim to generate more reliable and robust musculoskeletal models in a valid manner.

Original languageEnglish
Pages (from-to)3660-3666
Number of pages7
JournalJournal of Biomechanics
Volume49
Issue number15
DOIs
Publication statusPublished - 7 Nov 2016

Keywords

  • AnyBody modeling system
  • EMG
  • Inverse dynamics analysis
  • Muscle activity prediction
  • Musculoskeletal model

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