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

Assessing 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,...

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Vydané v:Sensors (Basel, Switzerland) Ročník 24; číslo 18; s. 5955
Hlavní autori: Redmond, Cushla, Smit, Michelle, Draganova, Ina, Corner-Thomas, Rene, Thomas, David, Andrews, Christopher
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 13.09.2024
MDPI
Predmet:
Accelerometers
Accelerometry - instrumentation
Accelerometry - methods
algorithm
Algorithms
Animals
Batteries
Behavior
Behavior, Animal - physiology
behaviour classification
Data collection
Data mining
Dogs
Female
Locomotion - physiology
Machine Learning
Male
Nutrition research
overall activity
random forest
Surveillance
New Zealand
United States > US
random forest
behaviour classification
overall activity
algorithm
ISSN:1424-8220, 1424-8220
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Shrnutí:Assessing 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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24185955