title: 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 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%.
authors.affiliation: Federal University of Technology-Paraná, Pato Branco-PR 85503-390, Brazil. vinicius@utfpr.edu.br.
authors.forename: Vinicius
authors.initials: V
authors.lastname: Pegorini
authors.affiliation: Federal University of Technology-Paraná, Pato Branco-PR 85503-390, Brazil. leandro.karam@pucpr.br.|Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil. leandro.karam@pucpr.br.
authors.forename: Leandro Zen
authors.initials: LZ
authors.lastname: Karam
authors.affiliation: Federal Institute-Paraná, Palmas-PR 85555-000, Brazil. christiano.pitta@ifpr.edu.br.
authors.forename: Christiano Santos Rocha
authors.initials: CS
authors.lastname: Pitta
authors.affiliation: Federal University of Technology-Paraná, Pato Branco-PR 85503-390, Brazil. rcardoso@utfpr.edu.br.
authors.forename: Rafael
authors.initials: R
authors.lastname: Cardoso
authors.affiliation: Federal University of Technology-Paraná, Pato Branco-PR 85503-390, Brazil. jeanccs@utfpr.edu.br.
authors.forename: Jean Carlos Cardozo
authors.initials: JC
authors.lastname: da Silva
authors.affiliation: Federal University of Technology-Paraná, Pato Branco-PR 85503-390, Brazil. hjkalin@utfpr.edu.br.
authors.forename: Hypolito José
authors.initials: HJ
authors.lastname: Kalinowski
authors.affiliation: Federal University of Technology-Paraná, Pato Branco-PR 85503-390, Brazil. richardsonr@utfpr.edu.br.
authors.forename: Richardson
authors.initials: R
authors.lastname: Ribeiro
authors.affiliation: Federal University of Technology-Paraná, Pato Branco-PR 85503-390, Brazil. bertotti@utfpr.edu.br.
authors.forename: Fábio Luiz
authors.initials: FL
authors.lastname: Bertotti
authors.affiliation: Federal University of Technology-Paraná, Pato Branco-PR 85503-390, Brazil. tangriani@utfpr.edu.br.
authors.forename: Tangriani Simioni
authors.initials: TS
authors.lastname: Assmann
mesh_terms: D000465:Algorithms; D000818:Animals; D001696:Biomechanical Phenomena; D004867:Equipment Design; D005247:Feeding Behavior / Q000145:classification* / Q000502:physiology*; D005336:Fiber Optic Technology / Q000295:instrumentation* / Q000379:methods; D000069550:Machine Learning; D008409:Mastication / Q000502:physiology*; D012418:Ruminants
keywords: biomechanical forces; fiber Bragg grating sensor (FBG); ingestive behavior; machine learning; pattern classification