Recognising Routines in Video for Safe Ambient Assisted Living
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 need to provide safe living environments for people with long term conditions, like dementia and autism;
- 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.
A considerable amount of previous AAL work has focused on using various sensors to detect emergency situations, such as falls. In these scenarios, the aim of an AAL system is to recognise an essentially binary condition, such as a tap left open or a person lying on a floor. The system can then alert care staff to intervene. However, an ability to carry out more complex analysis of activities of daily living (ADL), focusing on sequences of steps, is often required to help people with conditions like dementia or autism. Ensuring that steps in common ADL routines are performed in correct and complete sequences is essential for creating a safe independent living environment for these groups of people.
The aim of this project is to develop novel approaches for recognising ADL routines in video data. The work will involve developing models for representing ADL routines and techniques for mapping video data onto routine sequences. The mapping can allow for detection of deviations and determining necessary corrective actions. A system implementing these techniques can be used to provide real-time feedback to people in their home environments. An initial application area for this project is to investigate how these techniques can support people with Autism Spectrum Disorders (ASD).
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 entry (for October 2020 and February 2021 entries)
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
PhD by Publication
External candidates £4,407 p/a
Members of staff £1,680 p/a*
2021/2022 entry (for October 2021 and February 2022 entries)
PhD and MPhil
Home/EU/CI full-time students: £4,407 p/a*
Home/EU/CI part-time students: £2,204 p/a*
International full-time students: £17,600 p/a
International part-time students: £8,800 p/a
All fees are subject to annual increase.
PhD by Publication
External Candidates £4,407 p/a*
Members of Staff £1,720 p/a*
If you are an EU student starting a programme in 2021/22 please visit this page.
*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
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.
How to apply
We’d encourage you to contact Dr Rinat Khusainov (email@example.com) 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 COMP5300220 when applying.