| Autor: |
Cleghern, Zachary, Foster, Marc, Mealin, Sean, Williams, Evan, Holder, Timothy, Bozkurt, Alper, Roberts, David L. |
| Zdroj: |
International Journal of Cloud Computing; 2021, Vol. 10 Issue: 3 p247-264, 18p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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