Skip to Main content Skip to Navigation
Journal articles

Speedup computation of HD-sEMG signals using a motor unit-specific electrical source model

Abstract : Nowadays, bio-reliable modeling of muscle contraction is becoming more accurate and complex. This increasing complexity induces a significant increase in computation time which prevents the possibility of using this model in certain applications and studies. Accordingly, the aim of this work is to significantly reduce the computation time of high-density surface electromyogram (HD-sEMG) generation. This will be done through a new model of motor unit (MU)-specific electrical source based on the fibers composing the MU. In order to assess the efficiency of this approach, we computed the normalized root mean square error (NRMSE) between several simulations on single generated MU action potential (MUAP) using the usual fiber electrical sources and the MU-specific electrical source. This NRMSE was computed for five different simulation sets wherein hundreds of MUAPs are generated and summed into HD-sEMG signals. The obtained results display less than 2% error on the generated signals compared to the same signals generated with fiber electrical sources. Moreover, the computation time of the HD-sEMG signal generation model is reduced to about 90% compared to the fiber electrical source model. Using this model with MU electrical sources, we can simulate HD-sEMG signals of a physiological muscle (hundreds of MU) in less than an hour on a classical workstation.
Document type :
Journal articles
Complete list of metadatas

Cited literature [26 references]  Display  Hide  Download

https://hal.utc.fr/hal-02163701
Contributor : Jeremy Laforet <>
Submitted on : Thursday, December 12, 2019 - 11:49:05 AM
Last modification on : Friday, July 10, 2020 - 3:18:02 PM

Identifiers

Collections

Citation

Vincent Carriou, Sofiane Boudaoud, Jeremy Laforet. Speedup computation of HD-sEMG signals using a motor unit-specific electrical source model. Medical and Biological Engineering and Computing, Springer Verlag, 2018, 56 (8), pp.1459-1473. ⟨10.1007/s11517-018-1784-5⟩. ⟨hal-02163701⟩

Share

Metrics

Record views

22