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

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...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Switzerland), 2015-11, Vol.15 (11), p.28456-28471
Main Authors:	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
Format:	Article
Language:	eng
Subjects:	Algorithms Animal behavior Animals Automation biomechanical forces Biomechanical Phenomena Biomechanics Biomedical materials Chewing Classification Decision trees Equipment Design Feeding Behavior - classification Feeding Behavior - physiology fiber Bragg grating sensor (FBG) Fiber Optic Technology - instrumentation Fiber Optic Technology - methods Food In vivo testing In vivo tests ingestive behavior Machine Learning Mastication - physiology pattern classification Ruminants Sensors Success Surgical implants
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Summary:	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%.
ISSN:	1424-8220 1424-8220