Prediction of antagonistic muscle forces using inverse dynamic optimization during flexion/extension of the knee

G. Li, K. R. Kaufman, E. Y.S. Chao, H. E. Rubash

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

This paper examined the feasibility of using different optimization criteria in inverse dynamic optimization to predict antagonistic muscle forces and joint reaction forces during isokinetic flexion/extension and isometric extension exercises of the knee. Both quadriceps and hamstrings muscle groups were included in this study. The knee joint motion included flexion/extension, varus/valgus, and internal/external rotations. Four linear, nonlinear, and physiological optimization criteria were utilized in the optimization procedure. All optimization criteria adopted in this paper were shown to be able to predict antagonistic muscle contraction during flexion and extension of the knee. The predicted muscle forces were compared in temporal patterns with EMG activities (averaged data measured from five subjects). Joint reaction forces were predicted to be similar using all optimization criteria. In comparison with previous studies, these results suggested that the kinematic information involved in the inverse dynamic optimization plays an important role in prediction of the recruitment of antagonistic muscles rather than the selection of a particular optimization criterion. Therefore, it might be concluded that a properly formulated inverse dynamic optimization procedure should describe the knee joint rotation in three orthogonal planes.

Original languageEnglish (US)
Pages (from-to)316-322
Number of pages7
JournalJournal of Biomechanical Engineering
Volume121
Issue number3
DOIs
StatePublished - Jun 1999

ASJC Scopus subject areas

  • Biomedical Engineering
  • Physiology (medical)

Fingerprint

Dive into the research topics of 'Prediction of antagonistic muscle forces using inverse dynamic optimization during flexion/extension of the knee'. Together they form a unique fingerprint.

Cite this