Data fusion and multimodal learning for Ambient Assisted Living

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Rinat Khusainov and Dr Richard Curry.

The work on this project could involve:

• addressing the need to provide safe living environments for people with care needs and help families look after their loved ones
• applying the latest machine learning and data science techniques, including ensemble learning and multimodal deep learning to real life data
• experimenting with various sensor technologies in realistic deployment environments

Project description

Ambient Assisted Living (AAL) is concerned with using various technological solutions to allow people with additional care needs live independently in their preferred environment. AAL is of great importance for future healthcare services due to increases in the costs of traditional care models, caused by the growing elderly population and the number of people with long-term health conditions. There has been a considerable interest in AAL technologies recently from the government and industry alike.

Previous work in AAL has focused on using various environmental sensors, such as cameras, motion sensors, or microphones, to recognise people’s daily activities and emergency situations and use that information to ensure occupants’ safety and wellbeing. A challenge posed for such approaches in typical residential environments is that the detection field for an individual sensor is often occluded by room configurations, objects, and the occupant themselves, limiting system performance.

The aim of this project is to develop novel approaches to utilise information from multiple sensors simultaneously to improve performance of AAL systems. These approaches can include using multiple sensors of the same type (e.g. views from different cameras) as well as information from sensors of different modalities. The project will investigate applications of various data fusion and multimodal machine learning techniques.

The successful candidate will work within a team of academics and researchers with a track record in AAL, including links with care and housing providers and charities, such as Autism Hampshire. The project will utilise a bespoke research facility consisting of a fully instrumented residential house providing a real-world environment for experimentation with various technologies and collection of research data. The School also boasts excellent computing facilities including an IBM PowerAI Vision platform for image and video analysis, and a vibrant and supportive research environment.

General admissions criteria

You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements

Good numeracy and programming skills are an advantage. Knowledge of machine learning, data science, computer vision techniques, and sensors is helpful.

How to Apply

We encourage you to contact Dr Rinat Khusainov ([Email Address Removed]) to discuss your interest before you apply, quoting the project code below.

When you are ready to apply, please follow the 'Apply now' link on the Health Informatics PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code:COMP7520423