Title: A cloud data collection platform for canine behavioural prediction using objective sensor data

Authors: Zachary Cleghern; Marc Foster; Sean Mealin; Evan Williams; Timothy Holder; Alper Bozkurt; David L. Roberts

Addresses: Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA ' Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, USA ' Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA ' Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, USA ' Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, USA ' Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, USA ' Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USA

Abstract: Training successful guide dogs is time and resource intensive, requiring copious professional and volunteer labour. Even among the best programs, dogs are released with attrition rates commonly at 50%. Increasing success rates enables non-profits to meet growing demand for dogs and optimise resources. Selecting dogs for training is a crucial task; guide dog schools can benefit from both better selection accuracy and earlier prediction. We present a system aimed at improving analysis and selection of which dogs merit investment of resources using custom sensing hardware and a cloud-hosted data processing platform. To improve behavioural analysis at the early stages, we present results using objective data acquired in puppy behavioural tests and the current status of an IoT-enabled 'smart collar' system to gather data from puppies while being raised by volunteers prior to training. Our goal is to identify both puppies at risk and environmental influences on success as guide dogs.

Keywords: animal computer interaction; wearable computing; machine learning; internet of things; spatial-temporal data mining; canine behaviour.

DOI: 10.1504/IJCC.2021.10041443

International Journal of Cloud Computing, 2021 Vol.10 No.3, pp.247 - 264

Received: 03 Jun 2019
Accepted: 29 Jul 2019

Published online: 24 Sep 2021 *

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