44 research outputs found
Activity recognition in naturalistic environments using body-worn sensors
Phd ThesisThe research presented in this thesis investigates how deep learning and feature learning
can address challenges that arise for activity recognition systems in naturalistic, ecologically
valid surroundings such as the private home. One of the main aims of ubiquitous
computing is the development of automated recognition systems for human activities
and behaviour that are sufficiently robust to be deployed in realistic, in-the-wild environments.
In most cases, the targeted application scenarios are people’s daily lives,
where systems have to abide by practical usability and privacy constraints. We discuss
how these constraints impact data collection and analysis and demonstrate how common
approaches to the analysis of movement data effectively limit the practical use of
activity recognition systems in every-day surroundings. In light of these issues we develop
a novel approach to the representation and modelling of movement data based on
a data-driven methodology that has applications in activity recognition, behaviour imaging,
and skill assessment in ubiquitous computing. A number of case studies illustrate
the suitability of the proposed methods and outline how study design can be adapted
to maximise the benefit of these techniques, which show promising performance for
clinical applications in particular.SiDE research hu