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Abstract

We have developed ultra-low power wireless tracking tags which have been deployed on badgers, hares and swans in the wild. The sensors mainly measure 3-D acceleration, but some data is also from our world-first underground 3-D tracking system. The sensors are typically sampled at 8 Hz and collars run for between 2 and 3 months. This has generated a large dataset (> 500 million samples) of animal behaviour traces. For some of these deployments, there are secondary (ground truth) data including video and RFID. The challenge is to develop tools to analyse these datasets and present results to end-users (zoologists) in a user-friendly way. Such a tool would perform classification (typically unsupervised) on behaviour traces, generating classes of behaviour. At a very coarse level, this could simply be sleeping vs active, but the richer the classification, the better for automatic generation of ethograms. This classification can also be used online to alter the behaviour of the tracking tags themselves, such as to reduce the sampling rate when an animal is resting to prolong the lifetime of the collar.

Suitable for MSc student interested in statistical analysis/data mining and interdisciplinary science