Static models of human activity are typically inefficient due to their required effort in development and inability to cope with changing concepts during deployment.
The aim of this project is the development, implementation, and evaluation of methods for minimally supervised, iterative 'self training' of context recognition systems based on small numbers of s
This project will investigate a novel concept for wearable cameras in activity recognition, specifically to use an optical sensors with a small field of view placed at strategic locations to refine
