Sistema de aquisição e processamento de sinais de sEMG em tempo real para o estudo do comportamento ingestivo em ruminantes
The study of the ingestive behavior of grazing ruminants is fundamental in several aspects since the quantity and quality of ingested food influence the performance of animal production. Additionally, it provides the ability to early diagnosis of diseases, even before its clinical manifestation, whi...
| Autor principal: | Gomes, Otavio Augusto |
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| Formato: | Dissertação |
| Idioma: | Português |
| Publicado em: |
Universidade Tecnológica Federal do Paraná
2020
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| Assuntos: | |
| Acesso em linha: |
http://repositorio.utfpr.edu.br/jspui/handle/1/4784 |
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| Resumo: |
The study of the ingestive behavior of grazing ruminants is fundamental in several aspects since the quantity and quality of ingested food influence the performance of animal production. Additionally, it provides the ability to early diagnosis of diseases, even before its clinical manifestation, which will enable the producer to take the necessary actions. The higher demand for quality and accuracy of the processes requires more accurate modeling of the production system and strategic tools for the management of animals in pasture, such as technologies for the individual and remote monitoring, which have been provided by advances of precision farming. In such scenario, this work presents the development of a portable system for the acquisition of sEMG signals from the masseter muscle of bovines with a focus on animal monitoring in a grazing environment. The proposed system is capable of pre-processing the sEMG signal in order to extract its features and send them by a wireless communication system to a remote station for analysis. The results of experiments carried out with bovines shown that it is possible to visually discriminate three ingestive behaviors such as feeding, ruminating, and idleness. Also, through a Neural Artificial Network classifier, it was possible to find the suitable duration of the segmentation window that is used to extract sEMG features, in order to improve classification accuracy, and also enabling automatic discrimination of ingestive activities, including feeding, ruminating and idleness. |
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