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

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ID: 110077
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 …
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v2015sensorsin Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pegorini V;Karam LZ;Pitta CS;Cardoso R;da Silva JC;Kalinowski HJ;Ribeiro R;Bertotti FL;Assmann TS;;
Journal sensors
Year 2015
DOI
DOI not found
URL
https://www.ncbi.nlm.nih.gov/pubmed/26569250
Keywords
National Center for Biotechnology Information NCBI NLM MEDLINE animals pubmed abstract nih national institutes of health national library of medicine research support non-u.s. gov't algorithms pmid:26569250 pmc4701289 doi:10.3390/s151128456 vinicius pegorini leandro zen karam tangriani simioni assmann biomechanical phenomena equipment design feeding behavior / classification* feeding behavior / physiology* fiber optic technology / instrumentation* fiber optic technology / methods machine learning* mastication / physiology* ruminants Machine learning pattern classification ingestive behavior biomechanical forces fiber bragg grating sensor (fbg)

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