Semi-wildlife gait patterns classification using Statistical Methods and Artificial Neural Networks
| Autor: | L. Miro-Amarante, F. Gomez-Rodriguez, Alejandro Linares-Barranco, Daniel Gutierrez-Galan, Manuel Domínguez-Morales, M. Rivas-Perez, Juan Pedro Dominguez-Morales, Angel Jimenez-Fernandez |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación |
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
business.industry Computer science Wildlife Reconfigurability Context (language use) 04 agricultural and veterinary sciences 02 engineering and technology Machine learning computer.software_genre Pattern recognition (psychology) 040103 agronomy & agriculture 0202 electrical engineering electronic engineering information engineering 0401 agriculture forestry and fisheries 020201 artificial intelligence & image processing Artificial intelligence business Wireless sensor network computer |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname IJCNN |
| Popis: | Several studies have focused on classifying behavioral patterns in wildlife and captive species to monitor their activities and so to understanding the interactions of animals and control their welfare, for biological research or commercial purposes. The use of pattern recognition techniques, statistical methods and Overall Dynamic Body Acceleration (ODBA) are well known for animal behavior recognition tasks. The reconfigurability and scalability of these methods are not trivial, since a new study has to be done when changing any of the configuration parameters. In recent years, the use of Artificial Neural Networks (ANN) has increased for this purpose due to the fact that they can be easily adapted when new animals or patterns are required. In this context, a comparative study between a theoretical research is presented, where statistical and spectral analyses were performed and an embedded implementation of an ANN on a smart collar device was placed on semi-wild animals. This system is part of a project whose main aim is to monitor wildlife in real time using a wireless sensor network infrastructure. Different classifiers were tested and compared for three different horse gaits. Experimental results in a real time scenario achieved an accuracy of up to 90.7%, proving the efficiency of the embedded ANN implementation. Junta de Andalucía P12-TIC-1300 Ministerio de Economía y Competitividad TEC2016-77785-P |
| Databáze: | OpenAIRE |
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