The 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%.Junta de Andalucía P12-TIC-130
