Cerezuela Escudero, ElenaRíos Navarro, José AntonioDomínguez Morales, Juan PedroTapiador Morales, RicardoGutiérrez Galán, DanielMartín Cañal, CarlosLinares Barranco, Alejandro2020-02-062020-02-062016Cerezuela Escudero, E., Ríos Navarro, J.A., Domínguez Morales, J.P., Tapiador Morales, R., Gutiérrez Galán, D., Martín Cañal, C. y Linares Barranco, A. (2016). Performance Evaluation of Neural Networks for Animal Behaviors Classification: Horse Gaits Case Study. En DCAI 2016: 13th International Conference on Distributed Computing and Artificial Intelligence (377-385), Sevilla, España: Springer.978-3-319-40161-42194-5357https://hdl.handle.net/11441/92813The study and monitoring of wildlife has always been a subject of great interest. Studying the behavior of wildlife animals is a very complex task due to the difficulties to track them and classify their behaviors through the collected sensory information. Novel technology allows designing low cost systems that facilitate these tasks. There are currently some commercial solutions to this problem; however, it is not possible to obtain a highly accurate classification due to the lack of gathered information. In this work, we propose an animal behavior recognition, classification and monitoring system based on a smart collar device provided with inertial sensors and a feed-forward neural network or Multi-Layer Perceptron (MLP) to classify the possible animal behavior based on the collected sensory information. Experimental results over horse gaits case study show that the recognition system achieves an accuracy of up to 95.6%.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Multi-Layer PerceptronFeed-forward neural networkPattern recognitionInertial sensorsSensor fusioninfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1007/978-3-319-40162-1_41