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-1300Ministerio de Economía y Competitividad TEC2016-77785-