RT Conference Proceedings
T1 Extracting muscle synergy patterns from EMG data using autoencoders
A1 Spüler, Martin
A1 Irastorza-Landa, Nerea
A1 Sarasola-Sanz, Andrea
A1 Ramos-Murguialday, Ander
A2 Villa, Alessandro E.P.
A2 Masulli, Paolo
A2 Rivero, Antonio Javier Pons
AB Muscle synergies can be seen as fundamental building blocks of motor control. Extracting muscle synergies from EMG data is a widely used method in motor related research. Due to the linear nature of the methods commonly used for extracting muscle synergies, those methods fail to represent agonist-antagonist muscle relationships in the extracted synergies. In this paper, we propose to use a special type of neural networks, called autoencoders, for extracting muscle synergies. Using simulated data and real EMG data, we show that autoencoders, contrary to commonly used methods, allow to capture agonist-antagonist muscle relationships, and that the autoencoder models have a significantly better fit to the data than others methods.
PB Springer Verlag
SN 9783319447803
SN 0302-9743
YR 2016
FD 2016
LK https://hdl.handle.net/11556/1554
UL https://hdl.handle.net/11556/1554
LA eng
NO Spüler , M , Irastorza-Landa , N , Sarasola-Sanz , A & Ramos-Murguialday , A 2016 , Extracting muscle synergy patterns from EMG data using autoencoders . in A E P Villa , P Masulli & A J P Rivero (eds) , Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 9887 LNCS , Springer Verlag , pp. 47-54 , 25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016 , Barcelona , Spain , 6/09/16 . https://doi.org/10.1007/978-3-319-44781-0_6
NO conference
NO Publisher Copyright: © Springer International Publishing Switzerland 2016.
NO This work was supported by the Baden-Württemberg Stiftung (GRUENS), the Deutsche Forschungsgemeinschaft (DFG; SP 1533/2-1) and the German Ministry of Education and Research (MOTOR-BIC; FKZ 136W0053). Andrea Sarasola-Sanz and Nerea Irastorza-Landa are supported by the La Caixa-DAAD and Basque Government scholarships respectively.
DS TECNALIA Publications
RD 28 jul 2024