Skeletal muscle force and surface electromyographic (sEMG) signals have an inherent relationship. Therefore, sEMG can be used to estimate the required skeletal muscle force for a particular task. Usually, the location for the sEMG sensors is near the respective muscle motor unit points. EMG signals generated by skeletal muscles are temporal and spatially distributed which results in cross-talk that is recorded by different sEMG sensors. This research focuses primarily on modeling muscle dynamics in terms of sEMG signals and the generated muscle force. Here we assume sEMG as input and force as output to the skeletal muscle system. We model the two using a nonlinear Hammerstein-Wiener model and Multiple Regression model. Since these two methods are not leak proof, so we propose an entropy based threshold approach, which is more robust and reliable in most of the practical and real-time scenarios. The proposed methods are tested with the data collected on different subjects.
Previous Article in event
Next Article in event
sEMG and Skeletal Muscle Force Modeling: A Nonlinear Hammerstein-Wiener Model, Multiple Regression Model and EntropyBased Threshold Approach
Published: 16 November 2015 by MDPI in 2nd International Electronic Conference on Entropy and Its Applications session Machine Learning and Systems Theory
Keywords: sEMG; Hammerstein-Wiener; Skeletal Muscle; Kalman Estimator; Entropy