IoT-based measurement system for classifying cow behavior from tri-axial accelerometer

Detalhes bibliográficos
Autor(a) principal: Wang,Jun
Data de Publicação: 2019
Outros Autores: He,Zhitao, Ji,Jiangtao, Zhao,Kaixuan, Zhang,Haiyang
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Ciência Rural
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000600653
Resumo: ABSTRACT: A cow behavior monitoring system based on the Internet of Things (IoT) has been designed and implemented using tri-axial accelerometer, MSP430 microcontroller, wireless radio frequency (RF) module, and a laptop. The implemented system measured cow movement behavior and transmitted acceleration data to the laptop through the wireless RF module. Results were displayed on the laptop in a 2D graph, through which behavior patterns of cows were predicted. The measured data from the system were analyzed using the Multi-Back Propagation-Adaptive Boosting algorithm to determine the specific behavioral state of cows. The developed system can be used to increase classification performance of cow behavior by detecting acceleration data. Accuracy exceeded 90% for all the classified behavior categories, and the specificity of normal walking reached 96.98%. The sensitivity was good for all behavior patterns except standing up and lying down, with a maximum of 87.23% for standing. Overall, the IoT-based measurement system provides accurate and remote measurement of cow behavior, and the ensemble classification algorithm can effectively recognize various behavior patterns in dairy cows. Future research will improve the classification algorithm parameters and increase the number of enrolled cows. Once the functionality and reliability of the system have been confirmed on a large scale, commercialization may become possible.
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spelling IoT-based measurement system for classifying cow behavior from tri-axial accelerometerinternet of thingstri-axial accelerometermulti-BP-ada Boost algorithmcow behavior classificationABSTRACT: A cow behavior monitoring system based on the Internet of Things (IoT) has been designed and implemented using tri-axial accelerometer, MSP430 microcontroller, wireless radio frequency (RF) module, and a laptop. The implemented system measured cow movement behavior and transmitted acceleration data to the laptop through the wireless RF module. Results were displayed on the laptop in a 2D graph, through which behavior patterns of cows were predicted. The measured data from the system were analyzed using the Multi-Back Propagation-Adaptive Boosting algorithm to determine the specific behavioral state of cows. The developed system can be used to increase classification performance of cow behavior by detecting acceleration data. Accuracy exceeded 90% for all the classified behavior categories, and the specificity of normal walking reached 96.98%. The sensitivity was good for all behavior patterns except standing up and lying down, with a maximum of 87.23% for standing. Overall, the IoT-based measurement system provides accurate and remote measurement of cow behavior, and the ensemble classification algorithm can effectively recognize various behavior patterns in dairy cows. Future research will improve the classification algorithm parameters and increase the number of enrolled cows. Once the functionality and reliability of the system have been confirmed on a large scale, commercialization may become possible.Universidade Federal de Santa Maria2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000600653Ciência Rural v.49 n.6 2019reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20180627info:eu-repo/semantics/openAccessWang,JunHe,ZhitaoJi,JiangtaoZhao,KaixuanZhang,Haiyangeng2019-06-11T00:00:00ZRevista
dc.title.none.fl_str_mv IoT-based measurement system for classifying cow behavior from tri-axial accelerometer
title IoT-based measurement system for classifying cow behavior from tri-axial accelerometer
spellingShingle IoT-based measurement system for classifying cow behavior from tri-axial accelerometer
Wang,Jun
internet of things
tri-axial accelerometer
multi-BP-ada Boost algorithm
cow behavior classification
title_short IoT-based measurement system for classifying cow behavior from tri-axial accelerometer
title_full IoT-based measurement system for classifying cow behavior from tri-axial accelerometer
title_fullStr IoT-based measurement system for classifying cow behavior from tri-axial accelerometer
title_full_unstemmed IoT-based measurement system for classifying cow behavior from tri-axial accelerometer
title_sort IoT-based measurement system for classifying cow behavior from tri-axial accelerometer
author Wang,Jun
author_facet Wang,Jun
He,Zhitao
Ji,Jiangtao
Zhao,Kaixuan
Zhang,Haiyang
author_role author
author2 He,Zhitao
Ji,Jiangtao
Zhao,Kaixuan
Zhang,Haiyang
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Wang,Jun
He,Zhitao
Ji,Jiangtao
Zhao,Kaixuan
Zhang,Haiyang
dc.subject.por.fl_str_mv internet of things
tri-axial accelerometer
multi-BP-ada Boost algorithm
cow behavior classification
topic internet of things
tri-axial accelerometer
multi-BP-ada Boost algorithm
cow behavior classification
description ABSTRACT: A cow behavior monitoring system based on the Internet of Things (IoT) has been designed and implemented using tri-axial accelerometer, MSP430 microcontroller, wireless radio frequency (RF) module, and a laptop. The implemented system measured cow movement behavior and transmitted acceleration data to the laptop through the wireless RF module. Results were displayed on the laptop in a 2D graph, through which behavior patterns of cows were predicted. The measured data from the system were analyzed using the Multi-Back Propagation-Adaptive Boosting algorithm to determine the specific behavioral state of cows. The developed system can be used to increase classification performance of cow behavior by detecting acceleration data. Accuracy exceeded 90% for all the classified behavior categories, and the specificity of normal walking reached 96.98%. The sensitivity was good for all behavior patterns except standing up and lying down, with a maximum of 87.23% for standing. Overall, the IoT-based measurement system provides accurate and remote measurement of cow behavior, and the ensemble classification algorithm can effectively recognize various behavior patterns in dairy cows. Future research will improve the classification algorithm parameters and increase the number of enrolled cows. Once the functionality and reliability of the system have been confirmed on a large scale, commercialization may become possible.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000600653
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000600653
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0103-8478cr20180627
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Universidade Federal de Santa Maria
publisher.none.fl_str_mv Universidade Federal de Santa Maria
dc.source.none.fl_str_mv Ciência Rural v.49 n.6 2019
reponame:Ciência Rural
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Ciência Rural
collection Ciência Rural
repository.name.fl_str_mv
repository.mail.fl_str_mv
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