DOI: 10.5061/dryad.q2bvq83sx
title: Data for: Domestic cat accelerometer data calibrated with behaviours

description: 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.
description: Indoor cats were fitted with neck collars to which tri-axial
 accelerometers recording at 40 Hz were affixed. Accelerometer data was
 synchronised with video footage of the cats and distinct behaviours were
 labelled (‘rest’, ‘walk’, ‘trot’, ‘run’, ‘collar shake’, ‘feed’, and
 ‘groom’) using bespoke software DDMT (Wildbyte technologies,
 http://wildbytetechnologies.com/software.html). Transitions between
 behaviours were not included in any behaviour sample.
 <em>Details</em> Nine adult
 domestic cats (4 females, 5 males; aged 6 months – 8 years) housed at Mid
 Antrim Animal Sanctuary, Antrim, Northern Ireland in rooms (2 m x 3 m)
 were studied in June and July 2017. Cats were free to move to an enclosed
 outside area (2 x 2 m). All individuals were either neutered or spayed and
 were certified as healthy by a veterinarian prior to participation in the
 study. Cats were fitted with quick release collars
 (Breakaway buckle collar, Rogz Ltd. 2002/030628/07) to which a tri-axial
 accelerometer (‘Daily Diary’, Wilson et al. 2008) recording at 40 Hz was
 attached. The total weight of the collar and logger was 25g (less than 1%
 of the cats’ body weight). Daily Diary loggers were fitted under the chin
 of the cat in line with the lateral (sway, X), vertical (heave, Y), and
 sagittal (surge, Z) body axes. Whilst wearing the collars, cats were
 filmed using a Sony Alpha a58 DSLR camera (Sony, Latin America, Inc.) for
 15 minutes during the morning while a second researcher encouraged the cat
 to undertake different behaviours, such as running after a toy, or
 provided food to observe feeding behaviours. In addition, naturally
 occurring behaviours were observed, such as walking, trotting, resting,
 grooming, and shaking the collar. These behaviours were selected as they
 accounted for much of the cat’s (and other wild equivalent predator’s)
 daily behaviours, are of ecological significance, and the repertoire can
 be indicative of welfare. Each cat was filmed for 15 minutes to record the
 different behaviours it undertook.
 <em>Accelerometer data and video
 synchronisation</em> Accelerometer data and video
 footage from the indoor cats were synchronised using the timestamp of the
 data and video. To guard against any potential inaccuracies of their
 internal clocks, during the video, the collar was shaken up and down by
 the observers to create a distinct marking point in the accelerometer data
 that could be synchronised with the camera timestamp on the recording. Any
 offset that was required between the camera and accelerometer was noted
 and added to the accelerometer data once the data was downloaded and
 loaded into DDMT software. Distinct behaviours that lasted at least two
 seconds were selected on the video, and identified in the accelerometer
 data via the corrected timestamp. Transitions between behaviours were not
 included in any behaviour sample. The data was also examined to confirm it
 contained likely values for this behaviour (e.g. resting did not contain
 extremely high accelerometer values) to ensure behaviours were not
 incorrectly classified. DDMT is a specialised accelerometer handing
 software that allows various data manipulations to be done, including
 “labelling” of behaviours that can then be extracted individually. This
 was conducted for all distinct identifiable behaviours within the video
 footage and the accelerometer samples extracted.
 <em>Ethical Approval</em> This
 project was given ethical approval by committee at Queen’s University
 Belfast (QUB-BS-AREC-19-005).
description: # Domestic cat accelerometer data calibrated with behaviours
 [https://doi.org/10.5061/dryad.q2bvq83sx](https://doi.org/10.5061/dryad.q2bvq83sx) ## Description of the data and file structure The data is structured according to distinct behaviours demonstrated by the domestic cats. Accelerometers were set to record at 40 Hz, represented by the Time column. ID refers to individual cats. AccX, AccY, and AccZ are acceleration values in three axes. Behaviour refers to the video calibrated behaviour defined above and in the affiliated manuscript. ## Code/Software Accelerometers were set, calibrated, and the behaviours extracted using DDMT software; Wildbyte technologies, [http://wildbytetechnologies.com/software.html](http://wildbytetechnologies.com/software.html)