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

Sensors

Vinicius Pegorini¹  Leandro Zen Karam¹  Christiano Santos Rocha Pitta²  Rafael Cardoso¹  Jean Carlos Cardozo da Silva¹  Hypolito José Kalinowski¹  Richardson Ribeiro¹  Fábio Luiz Bertotti¹  Tangriani Simioni Assmann¹ 

[1] Federal University of Technology-Paraná, Pato Branco-PR 85503-390, Brazil; E-Mails:;Federal Institute-Paraná, Palmas-PR 85555-000, Brazil; E-Mail:

关键词: pattern classification;    machine learning;    ingestive behavior;    biomechanical forces;    fiber Bragg grating sensor (FBG);   

DOI  :  10.3390/s151128456

来源: mdpi

【 摘 要 】

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%.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.