title: Muscular fatigue detection using sEMG in dynamic contractions.
abstract: 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.
authors.forename: Diana R
authors.initials: DR
authors.lastname: Bueno
authors.forename: J M
authors.initials: JM
authors.lastname: Lizano
authors.forename: L
authors.initials: L
authors.lastname: Montano
mesh_terms: D000465:Algorithms; D001132:Arm; D004576:Electromyography; D006801:Humans; D009119:Muscle Contraction; D018763:Muscle Fatigue; D018482:Muscle, Skeletal