IoT-based measurement system for classifying cow behavior from tri-axial accelerometer
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
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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Ciência rural (Online) |
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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 |
|
_version_ |
1749140553873752064 |