In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning

Article, Other literature type English OPEN
Pegorini, Vinicius ; Karam, Leandro Zen ; Pitta, Christiano Santos Rocha ; Cardoso, Rafael ; da Silva, Jean Carlos Cardozo ; Kalinowski, Hypolito José ; Ribeiro, Richardson ; Bertotti, Fábio Luiz ; Assmann, Tangriani Simioni (2015)
  • Publisher: MDPI
  • Journal: Sensors (Basel, Switzerland), volume 15, issue 11, pages 28,456-28,471 (issn: 1424-8220, eissn: 1424-8220)
  • Related identifiers: doi: 10.3390/s151128456, pmc: PMC4701289
  • Subject: pattern classification | TP1-1185 | fiber Bragg grating sensor (FBG) | Chemical technology | machine learning | ingestive behavior | Article | biomechanical forces

Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%.
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