Hidden Markov Models for Analyzing Stress Levels in Working Dogs: A Comparative Study of Data Collection Devices and Frequencies

This research project aims to investigate and analyse the stress levels of working dogs using Hidden Markov Models (HMMs). Working dogs, such as search and rescue dogs, police dogs, and service dogs, often encounter high-stress situations during their duties. Therefore, understanding and monitoring their stress levels is crucial for their well-being and performance.

In the research component of this project, we aim to find the optimal data collection devices and frequencies for accurately monitoring and measuring the working dogs’ stress levels with HMMs. We also aim to identify different levels of stress from the recorded ECG, Temperature and Respiratory Stress data that are consistent with the type of activity that the dogs are engaging in.

Thomas Hanyang Zheng

The University of Sydney

Thomas is currently studying for a Bachelor of Science at the University of Sydney, majoring in Mathematics and Statistics. He is particularly interested in problems of the applied type, like predictive modelling and variational approaches to Bayesian statistics.

Thomas is also a product designer in the University’s Formula Student team. From this, he gained a fascination with the theory of finite element methods for solving partial differential equations and their implementation in structural analysis and topology optimisation.

Thomas wishes to pursue further study in the area of applied mathematics and statistics, and is grateful for the chance provided by the AMSI SRS program to gain a taste of research. Outside of University, Thomas enjoys roaming through the beautiful Australian bush.

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