Muscular fatigue detection using sEMG in dynamic contractions.
Annu Int Conf IEEE Eng Med Biol Soc
; 2015: 494-7, 2015 Aug.
Article
en En
| MEDLINE
| ID: mdl-26736307
In this work we have studied different indicators of muscle fatigue from the electrical signal produced by the muscles when contract (sEMG or EMG: surface electromyography): Mean Frequency of the power spectrum (MNF), Median Frequency (Fmed), Dimitrov Spectral Index (FInsm5), Root Mean Square (RMS), and Zerocrossing (ZC). The most reliable features are selected to develop a detection algorithm that estimates muscle fatigue. The approach used in the algorithm is probabilistic and is based on the technique of Gaussian Mixture Model (GMM). The system is divided into two stages: training and validation. During training, the algorithm learns the distribution of data regarding fatigue evolution; after that, the algorithm is validated with data that have not been used to train. Therefore, two experimental sessions have been performed with 6 healthy subjects for biceps.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Fatiga Muscular
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2015
Tipo del documento:
Article
Pais de publicación:
Estados Unidos