EMG-Driven Muscle Force Distribution Model Of Knee Joint

Muscle forces are considered to be an important tool for orthopedists, biomechanists, and physical therapists because muscle forces must be estimated in order to understand joint and bone loading and pathology. The calculation of muscle forces generated during complex activities is not trivial. In this study, an EMG-diven muscle models will be established, with active force-length, force-velocity and passive force length properties. Inverse and forward dynamics approaches were studied to reslove the redundant joint problem. The purpose of this study was to develop an electromyography (EMG) driven knee model to estimate dynamic knee muscle force.The results of simulations using generic muscle parameters may not be valid since muscle properties vary from subject to subject. Sensitivity analyses were performed to assess the effect of muscle parameters on estimated muscle contraction force. Our results of sensitivity analysis also suggest it is especially important to use accurate values of optimal muscle length. Conclusions regarding muscle function based on simulations with generic musculoskeletal parameters should be interpreted with caution. In order to solve this problem, the model calibrated muscle parameters using mathematical optimisation to match the net flexion/extension muscle moment with those measured by inverse dynamics.Three-dimensional kinematic and kinetic data of the lower extremity and electrographic data from ten lower leg muscles across the knee joint were collected for 9 athletes for stop-jumping and cutting tasks. An EMG driven knee model was developed to estimate individual muscle contraction force. The knee model was validated by using muscle parameters (optimal muscle length, maximum contraction velocity and Electromechanical delay) calculated in stop-jump task to predict knee flexion-extension moment during running. The result of the study showed a good relationship between optimized muscle moment and knee resultant flexion-extension moment during the stop jump task. When optimal muscle length, maximum contraction velocity and electromechanical delay estimated in the stop-jump task were used to predict muscle generated knee flexion-extension moment in the running task, the accuracy of the model slightly decreased.A graphics-based software system has been created that enable users to analyze musculoskeletal knee model. The software can analyze muscle length, moment arm, muscle force, knee joint net moments and coefficient of multiple correlation.