Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network

D. Leserri, N. Grimmelsmann, M. Mechtenberg, H.G. Meyer, A. Schneider, Mathematics 10 (2022).

Download
Es wurde kein Volltext hochgeladen. Nur Publikationsnachweis!
Artikel | Veröffentlicht | Englisch
Abstract
Limb movement prediction based on surface electromyography (sEMG) for the control of wearable robots, such as active orthoses and exoskeletons, is a promising approach since it provides an intuitive control interface for the user. Further, sEMG signals contain early information about the onset and course of limb movements for feedback control. Recent studies have proposed machine learning-based modeling approaches for limb movement prediction using sEMG signals, which do not necessarily require domain knowledge of the underlying physiological system and its parameters. However, there is limited information on which features of the measured sEMG signals provide the best prediction accuracy of machine learning models trained with these data. In this work, the accuracy of elbow joint movement prediction based on sEMG data using a simple feedforward neural network after training with different single- and multi-feature sets and data segmentation parameters was compared. It was shown that certain combinations of time-domain and frequency-domain features, as well as segmentation parameters of sEMG data, improve the prediction accuracy of the neural network as compared to the use of a standard feature set from the literature.
Erscheinungsjahr
Zeitschriftentitel
Mathematics
Band
10
Zeitschriftennummer
6
Artikelnummer
932
eISSN
FH-PUB-ID

Zitieren

Leserri, David ; Grimmelsmann, Nils ; Mechtenberg, Malte ; Meyer, Hanno Gerd ; Schneider, Axel: Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network. In: Mathematics Bd. 10, MDPI AG (2022), Nr. 6
Leserri D, Grimmelsmann N, Mechtenberg M, Meyer HG, Schneider A. Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network. Mathematics. 2022;10(6). doi:10.3390/math10060932
Leserri, D., Grimmelsmann, N., Mechtenberg, M., Meyer, H. G., & Schneider, A. (2022). Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network. Mathematics, 10(6). https://doi.org/10.3390/math10060932
@article{Leserri_Grimmelsmann_Mechtenberg_Meyer_Schneider_2022, title={Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network}, volume={10}, DOI={10.3390/math10060932}, number={6932}, journal={Mathematics}, publisher={MDPI AG}, author={Leserri, David and Grimmelsmann, Nils and Mechtenberg, Malte and Meyer, Hanno Gerd and Schneider, Axel}, year={2022} }
Leserri, David, Nils Grimmelsmann, Malte Mechtenberg, Hanno Gerd Meyer, and Axel Schneider. “Evaluation of SEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network.” Mathematics 10, no. 6 (2022). https://doi.org/10.3390/math10060932.
D. Leserri, N. Grimmelsmann, M. Mechtenberg, H. G. Meyer, and A. Schneider, “Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network,” Mathematics, vol. 10, no. 6, 2022.
Leserri, David, et al. “Evaluation of SEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network.” Mathematics, vol. 10, no. 6, 932, MDPI AG, 2022, doi:10.3390/math10060932.

Export

Markierte Publikationen

Open Data LibreCat

Suchen in

Google Scholar