Exploring the modelling of behaviour and context using deep learning under constrained computing platforms with applications to Digital Health

This project falls within the ESPRC Artificial Intelligence Technologies research area.

The central question of the study is the effective modelling of consumer health to determine user behaviour and context using data-mining methods.
Today's forms of mobile sensing, ranging from phone apps to wearable devices, typically monitor relatively simple dimensions of behaviour and context; for instance, sleep duration and step counts. However, advances in areas like deep learning are demonstrating computational models are possible for much more complex phenomena (e.g., user emotion, social interactions), at a level of robustness that they can be useful in real-world environments. Simultaneously, advances in the computational power of constrained devices (e.g., low-power GPUs, small-form-factor hardware accelerators) are increasing the sophistication of algorithms that are feasible to execute on these platforms.

Data mining has widespread applications as a useful process for extracting meaningful information from large datasets. In particular, its application in the modelling of health data on mobile devices has generated considerable interest. Such interest is chiefly motivated by breakthroughs in both software and hardware, namely deep learning methods and device computational power.

This research will involve an examination of current models and a subsequent software innovation to produce efficient models suited for constrained computing platforms. Current usage of data mining models often involves a trade-off between performance and efficiency. A prudent research question would be to tackle algorithmic redundancies and innovate for methods with relevance to constrained computing platforms such as wearables.

The main objectives to be achieved through this project include, but are not limited to, the following:
- modelling sensor data from constrained platforms using deep learning principles and
algorithms such that the interpretation of user behaviour and context reaches greater breadth and accuracy;
- developing new system resource-efficient deep learning methods suited to
constrained computing platforms (such as wearable devices and embedded platforms);
- investigating potential efficiency gains in deep learning methods through
software/algorithmic innovation or novel hardware/processor directions.

The novelty of the research lies on the potential solutions that might result from experimenting with varying machine learning architectures.

Finally, this project also aligns to EPSRC's strategy in delivering intelligent technologies and systems. The project also adheres to broader Cross-ICT priorities since it seeks to look at real healthcare data, such that the project is ICT-centric but not necessarily solely related to ICT.