Single-Channel sEMG Dictionary Learning Classification of Ingestive Behavior on Cows
| Autor: | Paulo José Abatti, André Eugênio Lazzaretti, Daniel Prado Campos, João Ari Gualberto Hill, Fabio Luiz Bertotti, Otavio Augusto Gomes, André Luís Finkler da Silveira |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 010401 analytical chemistry Feature extraction Pattern recognition Sparse approximation Linear discriminant analysis 01 natural sciences 0104 chemical sciences Test set Segmentation Artificial intelligence Electrical and Electronic Engineering business Instrumentation Classifier (UML) Sparse matrix |
| Zdroj: | IEEE Sensors Journal. 20:7199-7207 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2020.2977768 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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