2023 article

Toward In-the-Field Canine Manifold Learning: Data Fusion for Evaluation of Potential Guide Dogs


author keywords: manifold learning; LSTM; autoencoder; KPCA; canine monitoring
UN Sustainable Development Goal Categories
2. Zero Hunger (OpenAlex)
Source: Web Of Science
Added: March 18, 2024

We seek to better classify canine behavior for guide dog training predictions. Dog temperament is a major factor in success rates and current training also has a blind spot when the puppies are with puppy raisers, who are lesser trained volunteers who socialize puppies up to 15 months old. We have used a custom designed smart collar to collect environmental and behavioral data from each puppy individually going through various parts of the guide dog training. We investigate long short-term memory networks (LSTMs), autoencoders (AE), and kernel principal component analysis (KPCA) as methods to identify canine behavior and use multi-sensor data fusion to find the best subset of sensors with the best at classifying temperament. Standard manifold learning experiments take place in controlled environments and translate poorly to real-world applications. This research aims to bridge this gap using guide dog In For Training (IFT) data, which is from a lesser controlled environment and use it to develop a broader data-pattern-to-behavior dictionary for future real-world canine studies.