Predicting Guide Dog Temperament Evaluation Outcomes Using Raw ECG Signals

Abstract

Training a guide dog is a long and expensive process which involves experts with years of experience. At Guiding Eyes for the Blind, a large national guide dog school, a factor in the decision for whether a dog is suitable to continue training are numeric scores based on a subjective judgement during observation of the dog as it undergoes formal evaluations. As a step towards a more objective system, we outfitted dogs undergoing these evaluations with a data collection system capable of collecting electrocardiography and other data. Using both a prototype network and an optimized network, we show that electrocardiography data can be used to predict 29 behavioral scores with approximately 92% accuracy over 11 distinct tasks during the evaluation. Additionally, we show that each of the 11 tasks can predict any of the scores, indicating that the most predictive features in the data may be task agnostic.