Working dogs perform a variety of essential services for their human partners, from assisting people with disabilities, to Search and Rescue, police, and military work. Recent interest in the nascent field of Animal-Computer Interaction has prompted research in computer-mediated technology for communication between working dogs and their handlers. Haptic (touch) interfaces in the form of vibrating motors are a promising approach for handler-to-dog communication. Haptic interfaces can provide a silent, long-range method of sending commands to a dog, when voice or hand signals are inappropriate or impossible. However, evaluating haptic interfaces for dogs, who cannot self-report sensations, creates interesting challenges. This study draws on human-computer interaction concepts, such as Just Noticeable Difference, to explore methods and issues in evaluating haptic interfaces for working dogs. We created a haptic system and developed an evaluation method, reporting results for ten dogs of widely varying breeds, sizes, and coat types.
Working dogs1 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 is 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 create 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 predicts the dogs' outcomes with 87.5% average accuracy. We assess the reliability of our model by performing the testing routine 10 times over 1.5 years for a single suitable working dog, which predicts that the dog would pass each time. We calculate the resource benefit of identifying dogs who will fail early in their training, and the value for a cohort of 40 dogs using our toys and our methods for prediction is over $70,000. With CCI's 6 training centers, annual savings could be upwards of $5 million per year.
There are approximately a half million active service dogs in the United States, providing life-changing assistance and independence to people with a wide range of disabilities. The tremendous value of service dogs creates significant demand, which service dog providers struggle to meet. Breeding, raising, and training service dogs is an expensive, time-consuming endeavor which is exacerbated by expending resources on dogs who ultimately will prove to be unsuitable for service dog work because of temperament issues. Quantifying behavior and temperament through sensor-instrumented dog toys can provide a way to predict which dogs will be suitable for service dog work, allowing resources to be focused on the dogs likely to succeed. In a 2-year study, we tested dogs in advanced training at Canine Companions for Independence with instrumented toys, and we discovered that a measure of average bite duration is significantly correlated with a dog's placement success as a service dog [Adjusted OR = 0.12, Pr(>|z|) = 0.00666]. Applying instrumented toy interactions to current behavioral assessments could yield more accurate measures for predicting successful placement of service dogs while reducing the workload of the trainers.
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