Data from: Behavioural compass: animal behaviour recognition using magnetometers

Background: Animal-borne data loggers today often house several sensors
recording simultaneously at high frequency. This offers opportunities to
gain fine-scale insights into behaviour from individual-sensor as well as
integrated multi-sensor data. In the context of behaviour recognition,
even though accelerometers have been used extensively, magnetometers have
recently been shown to detect specific behaviours that accelerometers
miss. The prevalent constraint of limited training data necessitates the
importance of identifying behaviours with high robustness to data from new
individuals, and may require fusing data from both these sensors. However,
no study yet has developed an end-to-end approach to recognise common
animal behaviours such as foraging, locomotion, and resting from
magnetometer data in a common classification framework capable of
accommodating and comparing data from both sensors. Methods: We address
this by first leveraging magnetometers’ similarity to accelerometers to
develop biomechanical descriptors of movement: we use the static component
given by sensor tilt with respect to Earth’s local magnetic field to
estimate posture, and the dynamic component given by change in sensor tilt
with time to characterise movement intensity and periodicity. We use these
descriptors within an existing hybrid scheme that combines biomechanics
and machine learning to recognise behaviour. We showcase the utility of
our method on triaxial magnetometer data collected on ten wild Kalahari
meerkats (Suricata suricatta), with annotated video recordings of each
individual serving as groundtruth. Finally, we compare our results with
accelerometer-based behaviour recognition. Results: The overall
recognition accuracy of >94% obtained with magnetometer data was
found to be comparable to that achieved using accelerometer data.
Interestingly, higher robustness to inter-individual variability in
dynamic behaviour was achieved with the magnetometer, while the
accelerometer was better at estimating posture. Conclusions: Magnetometers
were found to accurately identify common behaviours, and were particularly
robust to dynamic behaviour recognition. The use of biomechanical
considerations to summarise magnetometer data makes the hybrid scheme
capable of accommodating data from either or both sensors within the same
framework according to each sensor’s strengths. This provides future
studies with a method to assess the added benefit of using magnetometers
for behaviour recognition.