Postgrad LIVE! Study Fairs


University of Manchester Featured PhD Programmes
FindA University Ltd Featured PhD Programmes
Engineering and Physical Sciences Research Council Featured PhD Programmes
University of West London Featured PhD Programmes
University of Reading Featured PhD Programmes

Ambient Assisted Living with Multiview Low Data Rate Imaging

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Dr R Khusainov
    Dr J Chiverton
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Applications are invited for a fully-funded three year PhD to commence in October 2019.

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

The work on this project will:
-develop novel approaches to ADL recognition using multiple cameras with low overall data rates
-investigate techniques for data fusion from multiple views
-look into adapting existing or researching new methods for recognition from low data rate video

Project description
Ambient Assisted Living is concerned with using various technological solutions and infrastructure to allow people with additional care needs to live independently in their preferred environment. Elderly people are one of the largest target groups for AAL systems. However, AAL is not limited to elderly only and also covers people with long term health conditions. AAL is of great importance for future healthcare services due to the rapid increases in the costs of traditional care models and approaches caused by the growing elderly population. Recognition of Activities of Daily Living (ADL) is one of the main areas for AAL systems. ADL recognition can be used to detect common emergency situations, such as falls. Monitoring ADL has also the potential to help dealing with more complex issues, such as medication, personal care, activity levels, and behaviour 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 located in a person’s home. In general, using environmental rather than wearable sensors is less cumbersome for users and creates less interference with their daily routines. Video is one of the richest information sources compared with other types of sensors, such as audio, motion, pressure, or usage. Human activity recognition from video has been an active area of research with some very promising recent results, such as deep learning-based approaches. However, one of the main requirements for AAL systems is the ability to deploy these techniques in typical residential environments. One of the challenges 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 for any given point of view, caused by room configuration, furniture, and other objects, meaning that a single camera is likely to be ineffective. The need for flexible positioning of possibly multiple cameras raises another set of practical problems: 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 can help dealing with obstructions in typical residential environments and also to cover multiple rooms in a dwelling. Low data rates (e.g. due to low resolution, low frame rates, or likely both) can facilitate the use of battery powered cameras with wireless connectivity. This will enable flexible camera positioning and easy retrofitting into existing housing. The project will investigate techniques for data fusion from multiple views and and will look into adapting existing or researching new methods for recognition from low data rate video.

Entry Requirements

General admissions criteria
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.

Specific candidate requirements
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 ([Email Address Removed]) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form and select ‘Computing’ as the subject area. 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 funded PhD opportunity you must quote project code CCTS4610219 when applying.

Funding Notes

Candidates applying for this project may be eligible to compete for one of a small number of bursaries available. The bursary is available to UK and EU students only and covers tuition fees and an annual maintenance grant in line with the RCUK rate (£14,777 for 2018/19). The Faculty of Technology may fund project costs/consumables up to £1,500 p.a.

FindAPhD. Copyright 2005-2019
All rights reserved.