Animal behaviour can be an indicator of health and welfare.Monitoring behaviour through visual observation is labour-intensive and there is a risk of missing infrequent behaviours.Twelve healthy domestic shorthair cats were fitted with triaxial accelerometers mounted on a collar and harness.
Over seven days, accelerometer and video footage were collected simultaneously.Identifier variables (n = 32) were calculated from the accelerometer data and summarized into 1 s epochs.Twenty-four behaviours were annotated from the video recordings and aligned with the summarised accelerometer data.
Models were created Party Supplies using random forest (RF) and supervised self-organizing map (SOM) machine learning techniques for each mounting location.Multiple modelling rounds were run to select and merge behaviours based on performance values.All models were then tested on a validation accelerometer dataset from the same twelve cats to identify behaviours.
The frequency of behaviours was calculated and compared using Dirichlet regression.Despite the SOM models having higher Kappa (>95%) and overall accuracy (>95%) compared with the RF models (64–76% and 70–86%, respectively), the RF models predicted behaviours more consistently between mounting locations.These results indicate that triaxial accelerometers Antenna Extension can identify cat specific behaviours.