Single-Channel sEMG Dictionary Learning Classification of Ingestive Behavior on Cows

Monitoring the ingestive behavior, especially rumination and eating, is important to assess animals' health, welfare, needs, and to estimate production efficiency. In this paper, the myographic response of masticatory muscles was acquired from cows to directly access ingestive information (graz...

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Bibliographic Details
Published in:IEEE sensors journal 2020-07, Vol.20 (13), p.7199-7207
Main Authors: Campos, Daniel Prado, Lazzaretti, Andre Eugenio, Bertotti, Fabio Luiz, Gomes, Otavio Augusto, Hill, Joao Ari Gualberto, da Silveira, Andre Luis Finkler, Abatti, Paulo Jose
Format: Article
Language:English
Subjects:
Animals
Classifiers
Dictionaries
Discriminant analysis
Electromyography
Feature extraction
Fisher discriminant dictionary learning (FDDL)
Learning
Machine learning
Muscles
pattern recognition
precision livestock farming (PLF)
Representations
Segmentation
Sensors
Signal processing
Sparse matrices
surface electromyograpy (sEMG)
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Summary:Monitoring the ingestive behavior, especially rumination and eating, is important to assess animals' health, welfare, needs, and to estimate production efficiency. In this paper, the myographic response of masticatory muscles was acquired from cows to directly access ingestive information (grazing and ruminating patterns) with high accuracy and time precision. Myographic signal processing is usually based on the same framework and optimal feature combinations have been extensively researched. However, recognition rates of hand-crafted feature based classifiers may be sensible to data deviation. In order to avoid the aforementioned issues and improve robustness, we propose an approach using sparse representation based classifiers which dismiss the need to select the best feature combination and classifier for the application. Also, we present a novel segmentation protocol to select chew related signal windows on cows. Four state-of-the-art multi-feature sets combined with linear discriminant analysis (LDA) were compared to a sparse representation based classifier called Fisher Discriminant Dictionary Learning (FDDL). Results suggest a significantly better performance of FDDL (p
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.2977768