sEMG and Skeletal Muscle Force Modeling: A Nonlinear Hammerstein-Wiener Model, Multiple Regression Model and Entropy Based Threshold Approach
Parmod Kumar1,*, Devanand R2 and Chandershekhar Potluri3
1 NPA Diversity Officer, Alumni of INRIA Sophia Antipolis Mediterranee, France; E-Mails: kumaparm@isu.edu (F.L.)
2 IEOR, IIT Bombay, Mumbai, India; E-Mails: dev.ismdhanbad@gmail.com (F.L.); chandupotluri@gmail.com (F.L.)
* Author to whom correspondence should be addressed; E-Mail: kumaparm@isu.edu (F.L.);
Tel.: +91-9968811549.
Abstract
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.