CBER Day 2006

KNEE JOINT MOMENT CONTRIBUTION TO ANKLE JOINT MOMENT PREDICTION USING AN EMG-DRIVEN MODEL

Daniel N. Bassett, Qi Shao, Daniel L. Benoit, Kurt T. Manal, Thomas S. Buchanan
Center for Biomedical Engineering Research, University of Delaware, Newark, DE

INTRODUCTION: EMG-driven models are typically limited to one joint without accounting for moment contributions of biarticular muscles to other joints. The purpose of this study was to investigate the effect of accounting for moment contributions of biarticular ankle joint muscles in predicting ankle joint moments by comparing the ankle and multi-joint ankle/knee model predictions.

METHODS: EMGs were recorded from eleven muscles about the ankle and knee during gait in healthy subjects. Kinematic data were used to estimate muscle tendon lengths and moment arms, and then combined with ground reaction forces to calculate inverse dynamic joint moments for the ankle and knee. Our EMG-driven Hill-type model (Buchanan et al., 2005) was used to estimate joint moments based on a forward dynamics approach. The muscle model requires parameters that are hard to measure in vivo and must therefore be optimized (Goffe at al 1994) using the inverse dynamic joint moments as the criterion. We calibrated both the ankle and multi-joint models to predict joint moments for novel trials.

RESULTS & DISCUSSION: The joint moments predicted by the models were compared to the inverse dynamic joint moments, and found to be correlated and accurate for both ankle (R2=0.97; RMS=7.7%) and multi-joint (R2=0.96; RMS=8.1%) predictions. However, muscle forces of the biarticular gastrocnemii changed by 15-20% when the knee was included in the model. The differences found in muscle force estimation are most likely due to improved physiological accuracy of the model.

REFERENCES: [1] Buchanan TS, et al., Estimation of muscle forces and joint moments using a forward-inverse dynamics model, Med Sci Sports Exerc. 37:1911-1916, 2005. [2] Goffe WL, et al., Global optimization of statistical functions with simulated annealing. J Econometrics., 60:65-99,1994