Activity profiling is key to understand individual behavior and group dynamics for a species. To date, individuals monitoring is directly performed by the ethologist, leading to several limitations in the quantity and quality of the results. In this work, we propose a data-driven collaborative system for automatic remote monitoring of wild animals, in a challenging environment, properly designed to satisfy the ethologist's needs. This smart system fuses sensors data to perform an intelligent behavior identification, allowing for automatic activity profiling. As a case study, we leverage a dataset collecting time-series acquired by tri-Axial accelerometer and GPS applied to 26 baboons for 35 days, to identify running, walking, sitting, standing and feeding activities. The results obtained in terms of prediction accuracy and decision-making process interpretability show that the system can overcome the hostile environment's challenges, proving to be an effective support to perform smart remote automatic profiling.

Data-Driven Collaborative Intelligent System for Automatic Activities Monitoring of Wild Animals

Leoni J.;Tanelli M.;Strada S. C.;
2020-01-01

Abstract

Activity profiling is key to understand individual behavior and group dynamics for a species. To date, individuals monitoring is directly performed by the ethologist, leading to several limitations in the quantity and quality of the results. In this work, we propose a data-driven collaborative system for automatic remote monitoring of wild animals, in a challenging environment, properly designed to satisfy the ethologist's needs. This smart system fuses sensors data to perform an intelligent behavior identification, allowing for automatic activity profiling. As a case study, we leverage a dataset collecting time-series acquired by tri-Axial accelerometer and GPS applied to 26 baboons for 35 days, to identify running, walking, sitting, standing and feeding activities. The results obtained in terms of prediction accuracy and decision-making process interpretability show that the system can overcome the hostile environment's challenges, proving to be an effective support to perform smart remote automatic profiling.
Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020
978-1-7281-5871-6
data-driven
machine-learning
sensors-fusion
smart system
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1169205
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