Determination of chewing patterns in goats using fiber Bragg gratings

Wosniak, C.; Silva, W. J.; Cardoso, Rafael; Assmann, Tangriani Simioni; Hill, João Ari Gualberto; Silveira, André Luís Finkler da; Sousa, Kleiton de Morais; Kalinowski, Hypolito José; Silva, J. C. C.

doi:10.1117/12.974332

Cited by 4 publications

(3 citation statements)

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“…The access to the mandible was made through a surgical procedure, through an incision of the skin and the masseter muscle, in order to reach the masseteric tuberosity, over which the sensor is placed. The location of the instrumentation was based on and described in [ 15 , 16 ], which defined the position of the sensor to better capture the strain generated from masticatory processes of different materials.…”

Section: Methodsmentioning

confidence: 99%

“…The optical biocompatible sensors [ 14 ] used in this work were developed from a new technique of packaging with preliminary in vitro tests [ 15 ], where the viability of the technique has been demonstrated. Furthermore, with the objective of developing a package for the optical sensor, tests were performed ex vivo [ 16 ].…”

Section: Introduction and Contextualizationmentioning

confidence: 99%

“…Studies for the assessment of ruminant feeding behavior using FBG arose from in vitro [ 15 ] and ex vivo [ 16 ] experiments. Therefore, these sensors allow the evaluation of strain applied by the animal along time for each chewing movement.…”

Section: Introduction and Contextualizationmentioning

confidence: 99%

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

Pegorini

Karam

Pitta

et al. 2015

Sensors

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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%.

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Section: Methodsmentioning

confidence: 99%