Abstract
The study and monitoring of the behavior of wildlife has always been a subject of great interest. Although many systems can track animal positions using GPS systems, the behavior classification is not a common task. For this work, a multi-sensory wearable device has been designed and implemented to be used in the Doñana National Park in order to control and monitor wild and semi-wild life animals. The data obtained with these sensors is processed using a Spiking Neural Network (SNN), with Address-Event-Representation (AER) coding, and it is classified between some fixed activity behaviors. This works presents the full infrastructure deployed in Doñana to collect the data, the wearable device, the SNN implementation in SpiNNaker and the classification results.
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Acknowledgement
The authors would like to thank the Advanced Processor Technologies (APT) Research Group of the University of Manchester for instructing us on the SpiNNaker platform on the 5th SpiNNaker Workshop. This work is supported by the Spanish government grant BIOSENSE (TEC2012-37868-C04-02) and by the excellence project from Andalusian Council MINERVA (P12-TIC-1300), with support from the European Regional Development Fund.
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Rios-Navarro, A., Dominguez-Morales, J.P., Tapiador-Morales, R., Dominguez-Morales, M., Jimenez-Fernandez, A., Linares-Barranco, A. (2016). A Sensor Fusion Horse Gait Classification by a Spiking Neural Network on SpiNNaker. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_5
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DOI: https://doi.org/10.1007/978-3-319-44778-0_5
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