Evaluating the Effects of Novel Enrichment Strategies on Dog Behaviour Using Collar-Based Accelerometers

Abstract

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.