In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning
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 …
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
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110077
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