Ambient Assisted Living with Multiview Low Data Rate Imaging
PhDs and postgraduate research
Self-funded PhD students only
School of Computing
Applications accepted all year round
The PhD will be based in the Faculty of Technology, and will be supervised by a supervision team lead by Dr Rinat Khusainov
The work on this project could involve:
- addressing a crucial societal need to provide better care for elderly and people with long term health conditions;
- applying the latest Artificial Intelligence and computer vision techniques to real life data;
- developing a prototype system that could become a commercial product in future.
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. Recognition of Activities of Daily Living (ADL) is one of the main areas for AAL systems. ADL recognition can be used to detect emergency situations, such as falls, and help dealing with more complex issues, such as medication, personal care, or behavior trends – all of which are essential to creating a safe independent living environment.
Recent work on ADL recognition has focused on using video data from cameras, which create less interference with people’s daily routines compared to wearable sensors. However, a key requirement for AAL systems is the ability to work in typical residential environments. A challenge posed by such environments for video-based recognition is field of view limitations. Even within a single room, there are likely to be obstructed areas caused by room configuration, furniture, and other objects, meaning that a single camera is likely to be ineffective. The need for flexible positioning of multiple cameras raises practical problems of connectivity and power supply.
The aim of this project is to develop novel approaches to ADL recognition using multiple cameras with low overall data rates. The use of multiple cameras helps dealing with obstructions in typical residential environments and also to cover multiple rooms. Low data rates facilitate the use of battery powered cameras with wireless connectivity, enabling flexible camera positioning and easy retrofitting into existing housing. The project will investigate data fusion from multiple views and will look into researching new methods for recognition from low data rate video.
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2020/2021 fees (applicable for October 2020 and February 2021 start)
Home/EU/CI full-time students: £4,407 p/a*
Home/EU/CI part-time students: £2,204 p/a*
International full-time students: £16,400 p/a*
International part-time students: £8,200 p/a*
*All fees are subject to annual increase
- You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in Computer Science or related discipline.
- 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.
We'd welcome applications from candidates with good software development skills, knowledge and/or experience in machine learning or pattern recognition and knowledge and/or experience in computer vision or image processing.
How to apply
We’d encourage you to contact Dr Rinat Khusainov (firstname.lastname@example.org) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. 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.
If you want to be considered for this PhD opportunity you must quote project code COMP5290220 when applying.