A neuromusculoskeletal tracking method for estimating individual muscle forces in human movement

Ajay Seth, Marcus G. Pandy*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

86 Citations (Scopus)


A neuromusculoskeletal tracking (NMT) method was developed to estimate muscle forces from observed motion data. The NMT method combines skeletal motion tracking and optimal neuromuscular tracking to produce forward simulations of human movement quickly and accurately. The skeletal motion tracker calculates the joint torques needed to actuate a skeletal model and track observed segment angles and ground forces in a forward simulation of the motor task. The optimal neuromuscular tracker resolves the muscle redundancy problem dynamically and finds the muscle excitations (and muscle forces) needed to produce the joint torques calculated by the skeletal motion tracker. To evaluate the accuracy of the NMT method, kinematics and ground forces obtained from an optimal control (parameter optimization) solution for maximum-height jumping were contaminated with both random and systematic noise. These data served as input observations to the NMT method as well as an inverse dynamics analysis. The NMT solution was compared to the input observations, the original optimal solution, and a simulation driven by the inverse dynamics torques. The results show that, in contrast to inverse dynamics, the NMT method is able to produce an accurate forward simulation consistent with the optimal control solution. The NMT method also requires 3 orders-of-magnitude less CPU time than parameter optimization. The speed and accuracy of the NMT method make it a promising new tool for estimating muscle forces using experimentally obtained kinematics and ground force data.

Original languageEnglish
Pages (from-to)356-366
Number of pages11
JournalJournal of Biomechanics
Issue number2
Publication statusPublished - 2007
Externally publishedYes


  • Computer simulation
  • Jumping
  • Muscle control
  • Musculoskeletal modeling
  • Optimization


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