Muscle force estimation using data fusion from high-density SEMG grid

The aim of the proposed work is to evaluate, by simulation, the introduction of a data fusion process from a HD-sEMG grid (8×8) to improve the muscle force estimation from sEMG signal. For this purpose, twelve electrode arrangements are combined to dimension reduction technique (PCA or channel averaging) to obtain a monodimensional sEMG signal. After, this signal is used in a sEMG-force relationship model to estimate the muscular force. In fact, two models, with different complexity, and used in the biomechanics community are studied. In the simulation, three isometric contractions are simulated (20%, 50% and 80% MVC) using a recent sEMG-force generation model. Finally, the Normalized RMS Difference (NRMSD) between the estimated force and the simulated force by the sEMG-force generation model is calculated for each combination (electrode arrangement and dimension reduction technique, force estimator). According to the obtained results, the combination PCA and Laplacian arrangement gave the best fitting using the second force estimator while the best result obtained for the first force estimator is with the Right Diagonal Bipolar (DBR) arrangement combined with channel averaging. In future works, these force estimators, combined to HD-sEMG data fusion, will be experimentally evaluated.