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

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ID: 118789
2015
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Abstract
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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pegorini2015sensorsin Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Vinicius Pegorini;Leandro Zen Karam;Christiano Santos Rocha Pitta;Rafael Cardoso;Jean Carlos Cardozo Da Silva;Hypolito José Kalinowski;Richardson Ribeiro;Fábio Luiz Bertotti;Tangriani Simioni Assmann;Pegorini, Vinicius;Zen Karam, Leandro;Pitta, Christiano Santos Rocha;Cardoso, Rafael;Da Silva, Jean Carlos Cardozo;Kalinowski, Hypolito José;Ribeiro, Richardson;Bertotti, Fábio Luiz;Assmann, Tangriani Simioni;
Journal sensors
Year 2015
DOI
10.3390/s151128456
URL
https://dx.doi.org/10.3390/s151128456
Keywords
Machine learning pattern classification ingestive behavior biomechanical forces fiber bragg grating sensor (fbg)

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