In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-11-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/15/11/28456 |
| _version_ | 1828393494288269312 |
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| author | Vinicius Pegorini Leandro Zen Karam Christiano Santos Rocha Pitta Rafael Cardoso Jean Carlos Cardozo da Silva Hypolito José Kalinowski Richardson Ribeiro Fábio Luiz Bertotti Tangriani Simioni Assmann |
| author_facet | Vinicius Pegorini Leandro Zen Karam Christiano Santos Rocha Pitta Rafael Cardoso Jean Carlos Cardozo da Silva Hypolito José Kalinowski Richardson Ribeiro Fábio Luiz Bertotti Tangriani Simioni Assmann |
| author_sort | Vinicius Pegorini |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-12-10T07:40:05Z |
| format | Article |
| id | doaj.art-7652132c6e4d4830894aaa94369d4a7a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:40:05Z |
| publishDate | 2015-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7652132c6e4d4830894aaa94369d4a7a2022-12-22T01:57:20ZengMDPI AGSensors1424-82202015-11-011511284562847110.3390/s151128456s151128456In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine LearningVinicius Pegorini0Leandro Zen Karam1Christiano Santos Rocha Pitta2Rafael Cardoso3Jean Carlos Cardozo da Silva4Hypolito José Kalinowski5Richardson Ribeiro6Fábio Luiz Bertotti7Tangriani Simioni Assmann8Federal University of Technology-Paraná, Pato Branco-PR 85503-390, BrazilFederal University of Technology-Paraná, Pato Branco-PR 85503-390, BrazilFederal Institute-Paraná, Palmas-PR 85555-000, BrazilFederal University of Technology-Paraná, Pato Branco-PR 85503-390, BrazilFederal University of Technology-Paraná, Pato Branco-PR 85503-390, BrazilFederal University of Technology-Paraná, Pato Branco-PR 85503-390, BrazilFederal University of Technology-Paraná, Pato Branco-PR 85503-390, BrazilFederal University of Technology-Paraná, Pato Branco-PR 85503-390, BrazilFederal University of Technology-Paraná, Pato Branco-PR 85503-390, BrazilPattern 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%.http://www.mdpi.com/1424-8220/15/11/28456pattern classificationmachine learningingestive behaviorbiomechanical forcesfiber Bragg grating sensor (FBG) |
| spellingShingle | Vinicius Pegorini Leandro Zen Karam Christiano Santos Rocha Pitta Rafael Cardoso Jean Carlos Cardozo da Silva Hypolito José Kalinowski Richardson Ribeiro Fábio Luiz Bertotti Tangriani Simioni Assmann In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning Sensors pattern classification machine learning ingestive behavior biomechanical forces fiber Bragg grating sensor (FBG) |
| title | In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning |
| title_full | In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning |
| title_fullStr | In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning |
| title_full_unstemmed | In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning |
| title_short | In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning |
| title_sort | in vivo pattern classification of ingestive behavior in ruminants using fbg sensors and machine learning |
| topic | pattern classification machine learning ingestive behavior biomechanical forces fiber Bragg grating sensor (FBG) |
| url | http://www.mdpi.com/1424-8220/15/11/28456 |
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