Predicting the Suitability of Working Dogs using Instrumented Dog Toys
Working dogs are significantly beneficial to society; however, a substantial number of dogs are released from time consuming and expensive training programs because of unsuitability in behavior. Early prediction of successful service dog placement could save time, resources, and funding. Our research focus was to explore whether aspects of canine temperament can be detected from interactions with sensors, and to develop classifiers that correlate sensor data to predict the success (or failure) of assistance dogs in advanced training. In a 2-year longitudinal study, our team tested a cohort of dogs entering advanced training in the Canine Companions for Independence (CCI) Program with 2 instrumented dog toys: a silicone ball and a silicone tug sensor. We then created a logistic model tree classifier to predict service dog success using only 5 features derived from dog-toy interactions. During randomized 10-fold cross validation where 4 of the 40 dogs were kept in an independent test set for each fold, our classifier predicted the dogs’ outcomes with 87.5% average accuracy. We assessed the reliability of our model by performing the testing routine 10 times over 1.5 years for a single suitable working dog, which predicted that the dog would pass each time. We calculated the resource benefit of identifying dogs who would fail early in their training, and the value for a cohort of 40 dogs using our toys and our methods for prediction was over $70,000. With CCI’s 6 training centers, annual savings could be upwards of $5 million per year.
Client DogStar Technologies and CCI
Date August 2015
Skills Development, Prototyping, Design, Experimental Design, Machine Learning, Data Analysis
Like What You See?