Investigation on parametric analysis of dynamic EMG signals by a muscle-structured simulation model

For the analysis of electromyographic (EMG) signals during dynamic movement, the authors propose an estimation algorithm for the time-varying parameters of an autoregressive model. The parameters correspond to less biased time-varying reflection coefficients. The authors determined the less biased estimation using a locally quasi-stationary model and named these parameters ´k parameters.´ They estimated k parameters up to the fifth order for the surface EMG signals of a masseter muscle during rapid open-close movement of the lower jaw, a ballistic contraction, and fatigue. According to the results, the time courses of the k parameters displayed remarkable properties. In order to study the behavior of k parameters physiologically, the authors produced a muscle-structured simulation model based on anatomical and physiological data. The simulation results suggested that the behavior of the third parameter is related to the number of active motor units (MUs) at the shallow layer of a muscle. The detailed recruitment mechanism in terms of the MU types has not yet been solved.