Browsing by Author "Redmond C"

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    Evaluating the Effects of Novel Enrichment Strategies on Dog Behaviour Using Collar-Based Accelerometers
    (MDPI (Basel, Switzerland), 2025-06-03) Redmond C; Draganova I; Corner-Thomas R; Thomas D; Andrews C; Gaunet F
    Environmental enrichment is crucial to improve welfare, reduce stress, and encourage natural behaviours in dogs housed in confined environments. This study aimed to use accelerometery and machine learning to evaluate the effect of different enrichment types on dog behaviour. Three enrichments (food, olfactory, and tactile) were provided to dogs for five consecutive days, with four days between each treatment. Acceleration data were collected using a collar mounted ActiGraph®. Nine behaviours were classified using a validated machine learning model. Behaviour and activity differed significantly among the dogs. Dogs interacted most with the food enrichment, followed by the olfactory and then tactile enrichments. The dogs were least active during the olfactory enrichment, whereas activity was relatively consistent during the food and tactile enrichments. For all enrichments, dogs exhibited the most exploratory/locomotive behaviour during the first hour of each enrichment period, but this declined over the treatment period indicating habituation. For exploratory and locomotive behaviour, food enrichment was the most stimulating for the dogs with longer daily engagement than for both olfactory and tactile enrichments. These results illustrate that accelerometery and machine learning can be used to evaluate enrichment strategies in dogs, but it is important to consider variation among dogs and habituation.
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    The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs (Canis familiaris): A Validation Study
    (MDPI (Basel, Switzerland), 2024-09-13) Redmond C; Smit M; Draganova I; Corner-Thomas R; Thomas D; Andrews C; Fullwood DT; Bowden AE
    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.