Personalisation of Machine Learning Models for Human Activity Recognition

Abstract

Emerging mobile and IoT technologies, in conjunction with the progress which has been made in the field of Artificial Intelligence, are paving the way to novel smart technology based applications capable of delivering innovative e-health solutions. Despite the level of maturity reached by these solutions to date both in simulated and controlled environments, several studies have highlighted their inability to adapt to the new conditions they are faced with when they are deployed in a free-living uncontrolled environment, and in particular when dealing with new subjects.
Within this context, the main contributions of this Thesis can be summarised as follows. Firstly, a flexible software architecture supporting the in situ adaptation of models has been proposed. Consequently, personalisation methods have been investigated in an attempt to identify a viable approach to implementing a model's adaptation based on the characteristics of a final target subject. As a result of this investigation, a personalisation method has been proposed and evaluated on a large publicly available real-world dataset. Results obtained on the dataset including data from 57 subjects confirmed that the personalised model outperforms its generic equivalent improving the recognition balanced accuracy value by almost 20%, from 55.60% to 74.81%.
Finally, the proposed elements within this body of research have been assembled with the aim of building a flexible software architecture capable of supporting the application of smart technologies within a behaviour change intervention framework for the monitoring of physical activities in older adults.

Date of Award	Jul 2020
Original language	English
Supervisor	Ian Cleland (Supervisor), Christopher Nugent (Supervisor) & Paul Mc Cullagh (Supervisor)