Smit MIkurior SJCorner-Thomas RAAndrews CJDraganova IThomas DGVanwanseele B2024-10-032024-10-032023-08-14Smit M, Ikurior SJ, Corner-Thomas RA, Andrews CJ, Draganova I, Thomas DG. (2023). The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Cats (Felis catus): A Validation Study.. Sensors (Basel). 23. 16. (pp. 7165-).1424-8220https://mro.massey.ac.nz/handle/10179/71593Animal 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 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 can identify cat specific behaviours.(c) 2023 The Author/sCC BY 4.0https://creativecommons.org/licenses/by/4.0/accelerometerbehaviour classificationdomestic catrandom forestself-organizing mapCatsAnimalsAlgorithmsBehavior, AnimalMachine LearningRandom ForestAccelerometryJournal article10.3390/s231671651424-8220journal-article7165-https://www.ncbi.nlm.nih.gov/pubmed/376317017165s23167165