Published

Journal of Biomechanics 45 (2012) 1842–1849

Authors

  • Amy Silder
    Department of Orthopaedic Surgery, Stanford University, CA, USA
    Department of Bioengineering, Stanford University, CA, USA
  • Thor Besier
    Auckland Bioengineering Institute, The University of Auckland, New Zealand
  • Scott L. Delp
    Department of Bioengineering, Stanford University, CA, USA
    Department of Mechanical Engineering, Stanford University, CA, USA

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Abstract

The goal of this study was to identify which muscle activation patterns and gait features best predict the metabolic cost of incline walking. We measured muscle activation patterns, joint kinematics and kinetics, and metabolic cost in sixteen subjects during treadmill walking at inclines of 0%, 5%, and 10%. Multivariate regression models were developed to predict the net metabolic cost from selected groups of the measured variables. A linear regression model including incline and the squared integrated electromyographic signals of the soleus and vastus lateralis explained 96% of the variance in metabolic cost, suggesting that the activation patterns of these large muscles have a high predictive value for metabolic cost. A regression model including only the peak knee flexion angle during stance phase, peak knee extension moment, peak ankle plantarflexion moment, and peak hip flexion moment explained 89% of the variance in metabolic cost; this finding indicates that kinematics and kinetics alone can predict metabolic cost during incline walking. The ability of these models to predict metabolic cost from muscle activation patterns and gait features points the way toward future work aimed at predicting metabolic cost when gait is altered by changes in neuromuscular control or the use of an assistive technology.