Predicting guide dog temperament evaluation outcomes using raW ECG signals

Sean Mealin, Zach Cleghern, Marc Foster, Alper Bozkurt, David L. Roberts

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationProceedings of the Sixth International Conference on Animal-Computer Interaction
PublisherACM
ISBN (Print)9781450376938
DOIs
StatePublished - Jan 10 2020
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-30
Acknowledgements: This material is based upon work supported by the National Science Foundation (NSF) under Grant No. 1329738, 1554367 and ECC-1160483. The work is also supported in part by two faculty awards from IBM and a grant from KAUST. Any opinion, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the NSF, IBM, or GEB.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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