Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation.
Sensors (Basel)
; 18(10)2018 Sep 25.
Article
en En
| MEDLINE
| ID: mdl-30257489
ABSTRACT
To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). Eight healthy men were recruited for the experiments, and the results demonstrated that all the three models were applicable for force estimation, and LSTM and C-LSTM achieved better performances. Even under subject-independent situation, they maintained mean RMSE% of as low as 9.07 ± 1.29 and 8.67 ± 1.14. CNN turned out to be a worse choice, yielding a mean RMSE% of 12.13 ± 1.98. To our knowledge, this work was the first to employ CNN, LSTM and C-LSTM in sEMG-based force estimation, and the results not only prove the strength of the proposed networks, but also pointed out a potential way of achieving high accuracy in real-time, subject-independent force estimation.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Electromiografía
/
Músculos
Tipo de estudio:
Prognostic_studies
Límite:
Adult
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Humans
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Male
Idioma:
En
Revista:
Sensors (Basel)
Año:
2018
Tipo del documento:
Article
País de afiliación:
China