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Edge Computing for Ambient Assisted Living


   School of Computing

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  Dr Rinat Khusainov, Dr Richard Curry  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Applications are invited for a three year full-time (or up to six year part-time) PhD to commence in October 2023.

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 edge computing paradigms and machine learning techniques, including edge and fog computing, federated learning, and 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.

AAL relies on using various sensors, such as optical, sound, contact, or proximity sensors to recognise people’s daily activities and emergency situations and use that information to ensure occupants’ safety and wellbeing. As the computing power on individual sensor nodes keeps increasing, this opens opportunities for performing significant amounts of data analysis within sensors, essentially turning an AAL system into an edge computing environment. Using edge computing in AAL can have significant benefits in improving performance and reliability, as well as protecting people’s privacy (as sensitive data can be processed securely within sensors).

The aim of this project is to investigate applications of edge computing architectures and technologies in AAL, as well as novel approaches to adapting data processing methods, such as federated learning and other decentralised AI methods.

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, edge computing, 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 Computing 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:COMP7530423


Funding Notes

Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these may provide tuition fee discounts and/or a stipend. This project is also available to students who are self-funded or hold (or likely to hold) scholarships from other organisations or governments.
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