The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs (Canis familiaris): A Validation Study

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dc.citation.issue18
dc.citation.volume24
dc.contributor.authorRedmond C
dc.contributor.authorSmit M
dc.contributor.authorDraganova I
dc.contributor.authorCorner-Thomas R
dc.contributor.authorThomas D
dc.contributor.authorAndrews C
dc.contributor.editorFullwood DT
dc.contributor.editorBowden AE
dc.coverage.spatialSwitzerland
dc.date.accessioned2024-10-08T20:02:06Z
dc.date.available2024-10-08T20:02:06Z
dc.date.issued2024-09-13
dc.description.abstractAssessing the behaviour and physical attributes of domesticated dogs is critical for predicting the suitability of animals for companionship or specific roles such as hunting, military or service. Common methods of behavioural assessment can be time consuming, labour-intensive, and subject to bias, making large-scale and rapid implementation challenging. Objective, practical and time effective behaviour measures may be facilitated by remote and automated devices such as accelerometers. This study, therefore, aimed to validate the ActiGraph® accelerometer as a tool for behavioural classification. This study used a machine learning method that identified nine dog behaviours with an overall accuracy of 74% (range for each behaviour was 54 to 93%). In addition, overall body dynamic acceleration was found to be correlated with the amount of time spent exhibiting active behaviours (barking, locomotion, scratching, sniffing, and standing; R2 = 0.91, p < 0.001). Machine learning was an effective method to build a model to classify behaviours such as barking, defecating, drinking, eating, locomotion, resting-asleep, resting-alert, sniffing, and standing with high overall accuracy whilst maintaining a large behavioural repertoire.
dc.description.confidentialfalse
dc.edition.editionSeptember 2024
dc.format.pagination5955-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/39338701
dc.identifier.citationRedmond C, Smit M, Draganova I, Corner-Thomas R, Thomas D, Andrews C. (2024). The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs (Canis familiaris): A Validation Study.. Sensors (Basel). 24. 18. (pp. 5955-).
dc.identifier.doi10.3390/s24185955
dc.identifier.eissn1424-8220
dc.identifier.elements-typejournal-article
dc.identifier.issn1424-8220
dc.identifier.number5955
dc.identifier.piis24185955
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71628
dc.languageeng
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/1424-8220/24/18/5955
dc.relation.isPartOfSensors (Basel)
dc.rights(c) 2024 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectalgorithm
dc.subjectbehaviour classification
dc.subjectoverall activity
dc.subjectrandom forest
dc.subjectAnimals
dc.subjectDogs
dc.subjectMachine Learning
dc.subjectBehavior, Animal
dc.subjectAccelerometry
dc.subjectAlgorithms
dc.subjectLocomotion
dc.subjectMale
dc.subjectFemale
dc.titleThe Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs (Canis familiaris): A Validation Study
dc.typeJournal article
pubs.elements-id491753
pubs.organisational-groupOther
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