PhyDaC - Stress Detection from Physiological Data in Cattle: Challenges in IoT
Descripción del Articulo
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...
Autores: | , , , |
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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 |
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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 |
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https://hdl.handle.net/20.500.12724/17625 |
dc.language.iso.none.fl_str_mv |
eng |
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eng |
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urn:issn:16130073 |
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https://ceur-ws.org/Vol-3144/RP-paper9.pdf |
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Repositorio Institucional - Ulima Universidad de Lima |
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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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 |
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