  Systems developed in wearable devices with sensors onboard are widely used to
collect data of humans and animals activities with the perspective of an
on-board automatic classification of data. An interesting application of these
systems is to support animals' behaviour monitoring gathered by sensors' data
analysis. This is a challenging area and in particular with fixed memories
capabilities because the devices should be able to operate autonomously for
long periods before being retrieved by human operators, and being able to
classify activities onboard can significantly improve their autonomy. In this
paper, we focus on the identification of prey handling activity in seals (when
the animal start attaching and biting the prey), which is one of the main
movement that identifies a successful foraging activity. Data taken into
consideration are streams of 3D accelerometers and depth sensors values
collected by devices attached directly on seals. To analyse these data, we
propose an automatic model based on Machine Learning (ML) algorithms. In
particular, we compare the performance (in terms of accuracy and F1score) of
three ML algorithms: Input Delay Neural Networks, Support Vector Machines, and
Echo State Networks. We attend to the final aim of developing an automatic
classifier on-board. For this purpose, in this paper, the comparison is
performed concerning the performance obtained by each ML approach developed and
its memory footprint. In the end, we highlight the advantage of using an ML
algorithm, in terms of feasibility in wild animals' monitoring.
