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Sensors 2015, 15(11), 28456-28471; doi:10.3390/s151128456

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

1
Federal University of Technology-Paraná, Pato Branco-PR 85503-390, Brazil
2
Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil
3
Federal Institute-Paraná, Palmas-PR 85555-000, Brazil
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 10 August 2015 / Revised: 20 October 2015 / Accepted: 30 October 2015 / Published: 11 November 2015
(This article belongs to the Section Physical Sensors)
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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%. View Full-Text
Keywords: pattern classification; machine learning; ingestive behavior; biomechanical forces; fiber Bragg grating sensor (FBG) pattern classification; machine learning; ingestive behavior; biomechanical forces; fiber Bragg grating sensor (FBG)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pegorini, V.; Zen Karam, L.; Pitta, C.S.R.; Cardoso, R.; da Silva, J.C.C.; Kalinowski, H.J.; Ribeiro, R.; Bertotti, F.L.; Assmann, T.S. In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning. Sensors 2015, 15, 28456-28471.

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