Data for: Domestic cat accelerometer data calibrated with behaviours

Observing animals in the wild often poses extreme challenges, but
animal-borne accelerometers are increasingly revealing unobservable
behaviours. Automated machine learning streamlines behaviour
identification from the substantial datasets generated during
multi-animal, long-term studies, however, the accuracy of such models
depends on the qualities of the training data. We examined how data
processing influenced the predictive accuracy of random forest (RF)
models, leveraging the easily observed domestic cat (Felis catus) as a
model organism for terrestrial mammalian behaviours. Nine indoor domestic
cats were equipped with collar-mounted tri-axial accelerometers, and
behaviours were recorded alongside video footage. From this calibrated
data, eight datasets were derived with; (i) additional descriptive
variables; (ii) altered frequencies of acceleration data (40 Hz vs. a mean
over 1 second); and (iii) standardised durations of different behaviours.
These training datasets were used to generate RF models which were
validated against calibrated cat behaviours before identifying behaviours
of five free-ranging tag-equipped cats. These predictions were compared to
those identified manually to validate the accuracy of the RF models for
free-ranging animal behaviours. RF models accurately predicted the
behaviours of indoor domestic cats (F-measure up to 0.96) with discernible
improvements observed with post-data-collection processing. Additional
variables, standardized durations of behaviours, and higher recording
frequencies improved model accuracy. However, prediction accuracy varied
with different behaviours, where high-frequency models excelled in
identifying fast paced behaviours (e.g. locomotion), while lower frequency
models (1 Hz) more accurately identified slower, aperiodic behaviours such
as grooming and feeding, particularly when examining free-ranging cat
behaviours. While RF modelling offered a robust means of behaviour
identification from accelerometer data, field validations were important
to validate model accuracy for free-ranging individuals. Future studies
may benefit from employing similar data processing methods that enhance RF
behaviour identification accuracy, with extensive advantages for
investigations into ecology, welfare, and management of wild animals.