The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Cats (Felis catus): A Validation Study

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dc.citation.issue16
dc.citation.volume23
dc.contributor.authorSmit M
dc.contributor.authorIkurior SJ
dc.contributor.authorCorner-Thomas RA
dc.contributor.authorAndrews CJ
dc.contributor.authorDraganova I
dc.contributor.authorThomas DG
dc.contributor.editorVanwanseele B
dc.coverage.spatialSwitzerland
dc.date.accessioned2024-10-03T20:20:32Z
dc.date.available2024-10-03T20:20:32Z
dc.date.issued2023-08-14
dc.description.abstractAnimal 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.
dc.description.confidentialfalse
dc.edition.editionAugust 2023
dc.format.pagination7165-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/37631701
dc.identifier.citationSmit 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-).
dc.identifier.doi10.3390/s23167165
dc.identifier.eissn1424-8220
dc.identifier.elements-typejournal-article
dc.identifier.issn1424-8220
dc.identifier.number7165
dc.identifier.piis23167165
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71593
dc.languageeng
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/1424-8220/23/16/7165
dc.relation.isPartOfSensors (Basel)
dc.rights(c) 2023 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectaccelerometer
dc.subjectbehaviour classification
dc.subjectdomestic cat
dc.subjectrandom forest
dc.subjectself-organizing map
dc.subjectCats
dc.subjectAnimals
dc.subjectAlgorithms
dc.subjectBehavior, Animal
dc.subjectMachine Learning
dc.subjectRandom Forest
dc.subjectAccelerometry
dc.titleThe Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Cats (Felis catus): A Validation Study
dc.typeJournal article
pubs.elements-id479941
pubs.organisational-groupOther
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