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Using Inertial Measurement Unit Data for Objective Evaluations of Potential Guide Dogs

Authors:

David L. RobertsAuthors Info & Claims

ACI '20: Proceedings of the Seventh International Conference on Animal-Computer Interaction

Article No.: 2, Pages 1 - 11

https://doi.org/10.1145/3446002.3446064

Published: 24 March 2021 Publication History

Abstract

As puppies progress through the training process to become guide dogs, they regularly undergo standard evaluations to assess their temperament. These evaluations consist of exposing the dogs to several stimuli while a highly-experienced observer scores them in several categories. In this paper, we present a neural network capable of generating the same scores as the human observer by using accelerometer and gyroscope data recorded during the evaluation. We define a baseline accuracy and compare the performance of the network, ultimately showing that it can achieve an accuracy score of approximately 93%.

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Cited By

Marcato MTedesco SO’Mahony CO’Flynn BGalvin P(2023)Machine learning based canine posture estimation using inertial dataPLOS ONE10.1371/journal.pone.028631118:6(e0286311)Online publication date: 21-Jun-2023
https://doi.org/10.1371/journal.pone.0286311
Byrne CStarner TJackson M(2022)Quantifying canine interactions with smart toys assesses suitability for service dog workFrontiers in Veterinary Science10.3389/fvets.2022.8869419Online publication date: 2-Sep-2022
https://doi.org/10.3389/fvets.2022.886941
Marcato MKenny JO’Riordan RO’Mahony CO’Flynn BGalvin P(2022)Assistance dog selection and performance assessment methods using behavioural and physiological tools and devicesApplied Animal Behaviour Science10.1016/j.applanim.2022.105691254(105691)Online publication date: Sep-2022
https://doi.org/10.1016/j.applanim.2022.105691
Show More Cited By

Index Terms

Using Inertial Measurement Unit Data for Objective Evaluations of Potential Guide Dogs
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Other conferences

ACI '20: Proceedings of the Seventh International Conference on Animal-Computer Interaction

November 2020

163 pages

ISBN:9781450375740

DOI:10.1145/3446002

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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OU: The Open University

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New York, NY, United States

Publication History

Published: 24 March 2021

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National Science Foundation

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ACI'2020

ACI'2020: Seventh International Conference on Animal-Computer Interaction

November 10 - 12, 2020

Milton Keynes, United Kingdom

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Cited By

Marcato MTedesco SO’Mahony CO’Flynn BGalvin P(2023)Machine learning based canine posture estimation using inertial dataPLOS ONE10.1371/journal.pone.028631118:6(e0286311)Online publication date: 21-Jun-2023
https://doi.org/10.1371/journal.pone.0286311
Byrne CStarner TJackson M(2022)Quantifying canine interactions with smart toys assesses suitability for service dog workFrontiers in Veterinary Science10.3389/fvets.2022.8869419Online publication date: 2-Sep-2022
https://doi.org/10.3389/fvets.2022.886941
Marcato MKenny JO’Riordan RO’Mahony CO’Flynn BGalvin P(2022)Assistance dog selection and performance assessment methods using behavioural and physiological tools and devicesApplied Animal Behaviour Science10.1016/j.applanim.2022.105691254(105691)Online publication date: Sep-2022
https://doi.org/10.1016/j.applanim.2022.105691
Chen FZimmermann MHekman JLord KLogan BRussenberger JLeighton EKarlsson E(2021)Advancing Genetic Selection and Behavioral Genomics of Working Dogs Through Collaborative ScienceFrontiers in Veterinary Science10.3389/fvets.2021.6624298Online publication date: 6-Sep-2021
https://doi.org/10.3389/fvets.2021.662429
Farhat N(2021)Objective Assessment of Movement for Canine NeurologyProceedings of the Eight International Conference on Animal-Computer Interaction10.1145/3493842.3493898(1-4)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1145/3493842.3493898

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