Embedded neural network for real-time animal behavior classification
|Author||Gutiérrez Galán, Daniel
Domínguez Morales, Juan Pedro
Cerezuela Escudero, Elena
Ríos Navarro, José Antonio
Tapiador Morales, Ricardo
Rivas Pérez, Manuel
Domínguez Morales, Manuel Jesús
Jiménez Fernández, Ángel Francisco
Linares Barranco, Alejandro
|Department||Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores|
|Published in||Neurocomputing, 272 (january 2018), 17-26.|
|Abstract||Recent biological studies have focused on understanding animal interactions and welfare. To help biolo- gists to obtain animals’ behavior information, resources like wireless sensor networks are needed. More- over, large ...
Recent biological studies have focused on understanding animal interactions and welfare. To help biolo- gists to obtain animals’ behavior information, resources like wireless sensor networks are needed. More- over, large amounts of obtained data have to be processed off-line in order to classify different behaviors.There are recent research projects focused on designing monitoring systems capable of measuring someanimals’ parameters in order to recognize and monitor their gaits or behaviors. However, network unre- liability and high power consumption have limited their applicability.In this work, we present an animal behavior recognition, classification and monitoring system based ona wireless sensor network and a smart collar device, provided with inertial sensors and an embeddedmulti-layer perceptron-based feed-forward neural network, to classify the different gaits or behaviorsbased on the collected information. In similar works, classification mechanisms are implemented in aserver (or base station). The main novelty of this work is the full implementation of a reconfigurableneural network embedded into the animal’s collar, which allows a real-time behavior classification andenables its local storage in SD memory. Moreover, this approach reduces the amount of data transmittedto the base station (and its periodicity), achieving a significantly improving battery life. The system hasbeen simulated and tested in a real scenario for three different horse gaits, using different heuristics andsensors to improve the accuracy of behavior recognition, achieving a maximum of 81%.
|Cite||Gutiérrez Galán, D., Domínguez Morales, J.P., Cerezuela Escudero, E., Rios Navarro, A., Tapiador Morales, R., Rivas Pérez, M.,...,Linares Barranco, A. (2018). Embedded neural network for real-time animal behavior classification. Neurocomputing, 272 (january 2018), 17-26.|