Machine learning approaches for identifying prey handling activity in otariid pinnipeds

• URL: http://arxiv.org/abs/2002.03866v1
• Date: Mon, 10 Feb 2020 15:30:08 GMT
• Title: Machine learning approaches for identifying prey handling activity in otariid pinnipeds
• Authors: Rita Pucci and Alessio Micheli and Stefano Chessa and Jane Hunter
• Abstract summary: This paper focuses on the identification of prey handling activity in seals. Data taken into consideration are streams of 3D accelerometers and depth sensors values collected by devices attached directly on seals. We propose an automatic model based on Machine Learning (ML) algorithms.
• Score: 12.814241588031685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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