PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT

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Stress in cattle is one of the main factors that generate economic losses in the livestock sector (e.g., reduction in the quality of milk or meat). In this field, heat stress has been considered as one of the main types of stress that negatively affects cattle. In addition, thanks to the arising of...

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Detalles Bibliográficos
Autores: Suni Lopez, Franci, Mayhua-Quispe, Angela, Condori-Fernandez, Nelly, Flores Quenaya, Elisban
Formato: objeto de conferencia
Fecha de Publicación:2022
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/17625
Enlace del recurso:https://hdl.handle.net/20.500.12724/17625
Nivel de acceso:acceso abierto
Materia:Stress
Cattle
Detectors
Artificial intelligence
Estrés
Ganado
Detectores
Inteligencia artificial
https://purl.org/pe-repo/ocde/ford#2.02.04
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dc.title.es_PE.fl_str_mv PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT
title PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT
spellingShingle PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT
Suni Lopez, Franci
Stress
Cattle
Detectors
Artificial intelligence
Estrés
Ganado
Detectores
Inteligencia artificial
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT
title_full PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT
title_fullStr PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT
title_full_unstemmed PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT
title_sort PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT
author Suni Lopez, Franci
author_facet Suni Lopez, Franci
Mayhua-Quispe, Angela
Condori-Fernandez, Nelly
Flores Quenaya, Elisban
author_role author
author2 Mayhua-Quispe, Angela
Condori-Fernandez, Nelly
Flores Quenaya, Elisban
author2_role author
author
author
dc.contributor.other.none.fl_str_mv Suni Lopez, Franci
dc.contributor.author.fl_str_mv Suni Lopez, Franci
Mayhua-Quispe, Angela
Condori-Fernandez, Nelly
Flores Quenaya, Elisban
dc.subject.en_EN.fl_str_mv Stress
Cattle
Detectors
Artificial intelligence
topic Stress
Cattle
Detectors
Artificial intelligence
Estrés
Ganado
Detectores
Inteligencia artificial
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.es_PE.fl_str_mv Estrés
Ganado
Detectores
Inteligencia artificial
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
description Stress in cattle is one of the main factors that generate economic losses in the livestock sector (e.g., reduction in the quality of milk or meat). In this field, heat stress has been considered as one of the main types of stress that negatively affects cattle. In addition, thanks to the arising of the Internet of Things in Animal Health, some researchers have proposed systems and models for the detection of this type of stress in an automated way, collecting and using data from meteorological variables (e.g., temperature, humidity), heart rate and others. However, the proposed models are mainly focused on heat stress detection that uses threshold-based estimation to determine the presence of stress; but, the level of stress experienced by cows can vary depending on their breed, or their ability to adapt to the environment where they are located. Therefore, in this project we propose an IoT platform for automatic detection of stress in cattle based on physiological signals; which is divided into three parts: i) implement a sensing device to collect physiological data, ii) a new method for automatic detection of stress based on physiological signals, and iii) an intuitive visualizer for monitoring cattle in individually way. The future research project, named PhyDac, is going to be carried out for two years with the participation of farmers from Peruvian regions (Arequipa, Cusco).
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2023-02-14T15:48:36Z
dc.date.available.none.fl_str_mv 2023-02-14T15:48:36Z
dc.date.issued.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
dc.type.other.none.fl_str_mv Artículo de conferencia en Scopus
format conferenceObject
dc.identifier.citation.es_PE.fl_str_mv Suni-Lopez, F., Mayhua-Quispe, A., Condori-Fernandez, N. & Flores Quenaya, E. (2022). PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT. In Proceedings of the 2022 Joint 16th Research Challenges in Information Science Workshops and Research Projects Track, RCIS-WS and RP 2022, Barcelona, España, May 2022, 3144. https://ceur-ws.org/Vol-3144/RP-paper9.pdf
dc.identifier.issn.none.fl_str_mv 1613-0073
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12724/17625
dc.identifier.event.none.fl_str_mv CEUR Workshop Proceedings
identifier_str_mv Suni-Lopez, F., Mayhua-Quispe, A., Condori-Fernandez, N. & Flores Quenaya, E. (2022). PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT. In Proceedings of the 2022 Joint 16th Research Challenges in Information Science Workshops and Research Projects Track, RCIS-WS and RP 2022, Barcelona, España, May 2022, 3144. https://ceur-ws.org/Vol-3144/RP-paper9.pdf
1613-0073
CEUR Workshop Proceedings
url https://hdl.handle.net/20.500.12724/17625
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv urn:issn:16130073
dc.relation.uri.none.fl_str_mv https://ceur-ws.org/Vol-3144/RP-paper9.pdf
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eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
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dc.publisher.es_PE.fl_str_mv CEUR-WS
dc.publisher.country.es_PE.fl_str_mv DE
dc.source.es_PE.fl_str_mv Repositorio Institucional - Ulima
Universidad de Lima
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spelling Suni Lopez, FranciMayhua-Quispe, AngelaCondori-Fernandez, NellyFlores Quenaya, ElisbanSuni Lopez, Franci2023-02-14T15:48:36Z2023-02-14T15:48:36Z2022Suni-Lopez, F., Mayhua-Quispe, A., Condori-Fernandez, N. & Flores Quenaya, E. (2022). PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT. In Proceedings of the 2022 Joint 16th Research Challenges in Information Science Workshops and Research Projects Track, RCIS-WS and RP 2022, Barcelona, España, May 2022, 3144. https://ceur-ws.org/Vol-3144/RP-paper9.pdf1613-0073https://hdl.handle.net/20.500.12724/17625CEUR Workshop ProceedingsStress in cattle is one of the main factors that generate economic losses in the livestock sector (e.g., reduction in the quality of milk or meat). In this field, heat stress has been considered as one of the main types of stress that negatively affects cattle. In addition, thanks to the arising of the Internet of Things in Animal Health, some researchers have proposed systems and models for the detection of this type of stress in an automated way, collecting and using data from meteorological variables (e.g., temperature, humidity), heart rate and others. However, the proposed models are mainly focused on heat stress detection that uses threshold-based estimation to determine the presence of stress; but, the level of stress experienced by cows can vary depending on their breed, or their ability to adapt to the environment where they are located. Therefore, in this project we propose an IoT platform for automatic detection of stress in cattle based on physiological signals; which is divided into three parts: i) implement a sensing device to collect physiological data, ii) a new method for automatic detection of stress based on physiological signals, and iii) an intuitive visualizer for monitoring cattle in individually way. The future research project, named PhyDac, is going to be carried out for two years with the participation of farmers from Peruvian regions (Arequipa, Cusco).application/pdfengCEUR-WSDEurn:issn:16130073https://ceur-ws.org/Vol-3144/RP-paper9.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/Repositorio Institucional - UlimaUniversidad de Limareponame:ULIMA-Institucionalinstname:Universidad de Limainstacron:ULIMAStressCattleDetectorsArtificial intelligenceEstrésGanadoDetectoresInteligencia artificialhttps://purl.org/pe-repo/ocde/ford#2.02.04PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoTinfo:eu-repo/semantics/conferenceObjectArtículo de conferencia en ScopusIngeniería de SistemasUniversidad de Lima9LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ulima.edu.pe/bitstream/20.500.12724/17625/3/license.txt8a4605be74aa9ea9d79846c1fba20a33MD53CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81037https://repositorio.ulima.edu.pe/bitstream/20.500.12724/17625/2/license_rdf8fc46f5e71650fd7adee84a69b9163c2MD5220.500.12724/17625oai:repositorio.ulima.edu.pe:20.500.12724/176252025-03-06 19:37:45.097Repositorio Universidad de Limarepositorio@ulima.edu.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