Postgraduate Research Opportunities

Intelligent wellness and strength training using sensor fusion based approaches

Details

Title: SCH41: Intelligent wellness and strength training using sensor fusion based approaches

Funding: A full Home/EU fee waiver or equivalent fee discount for overseas students  (£5,103 in 2020-21) (further fee details - international students will need to pay the balance of their fees) plus a doctoral stipend equivalent to the RCUK Minimum Doctoral Stipend (£15,285 in 2020-21).

Application deadline: Tuesday 31 March 2020

Start date: October 2020

Duration: 3 years (full time)

Location: Colchester Campus

Based in: School of Computer Science and Electronic Engineering (in collaboration with School of Sport, Rehabilitation and Exercise Sciences)

Overview

The current technologies of exercise training are limited to measurement of exercise outcomes using motion sensors or physiological parameters using optical sensors.

This studentship will research intelligent approaches to exercise training using wearable sensors and digital technologies that will help active individuals, athletes and people living with long term conditions to maximum their training and capacity over time.

The project

Integration of multiple simultaneous sensors including optical sensors and accelerometers as wearable to determine exercise capacity and effect of breathing techniques will be the novel major aspect of the project.

This will be achieved by applying advanced signal processing and machine learning approaches to measure energy expenditure level while measuring respiratory based parameters in a controlled way to guide the subjects to alter their breathing patterns.

The developed system will be comprehensively validated using control groups, athletes and people with long term respiratory conditions.

The facilities at intelligent dormitory 2 (iSpace) and the SRES Human Performance Unit labs and equipment will be used for subject recruitment.

The project ultimately aims to provide intelligent rehabilitation units for patients with chronic obstructive pulmonary disease (COPD) during and after pulmonary rehabilitation interventions in an attempt to enhance their activity levels and therefore quality of life.

Funding

The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,285 in 2020-21), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.

Supervisors

 Izzie Easton

Co-supervisor

School of Sport, Rehabilitation and Exercise Sciences, University of Essex

Izzie Easton is an experienced specialist respiratory physiotherapist and Senior Lecturer in SRES. She is currently involved in a variety of research projects involving patients living with chronic respiratory disease, pulmonary rehabilitation and other healthcare interventions.

Dr Faiyaz Doctor

Co-supervisor

School of Computer Science and Electronic Engineering, University of Essex

Dr Faiyaz Doctor is a Senior Lecturer at CSEE. His interests lie in the design and realisation of ambient intelligence through pervasive informatics and computational intelligence techniques to facilitate awareness, recognition, interaction and adaptation for context sensitive human-centred systems and environments. He is also interested in developing and applying hybrid methodologies using fuzzy logic with other nature inspired and machine learning and optimization techniques.

Dr Xiaojun Zhai

Co-supervisor

School of Computer Science and Electronic Engineering, University of Essex

Dr Xiaojun Zhai is a Lecturer in CSEE, and part of the EIS laboratory. He has been part of several EPSRC or EU funded projects for developing Embedded System and System-on-Chip (SoC) solutions for healthcare and machine learning applications. He has published more than 60 peer-reviewed papers in the area of connected health applications, machine learning, Internet of Things, image processing and their accelerations using multi-core and FPGAs/GPUs based systems during his career to date.

Professor Klaus McDonald-Maier

Co-supervisor

School of Computer Science and Electronic Engineering, University of Essex

Prof. Klaus D. McDonald-Maier is currently the Head of the Embedded and Intelligent Systems Laboratory, University of Essex. He has authored / co-authored over 200 scientific publications, and 15 patents. His current research interests include embedded systems and system-on-chip design, security, development support and technology, parallel and energy-efficient architectures, computer vision, data analytics, and the application of soft computing and image processing techniques for real-world problems.

Criteria

Ideally we are looking for candidates with an MSc in Computer Science, Electrical Engineering, Mathematics or any related subject.

Essential

  • At a minimum, a good honours BSc degree in computer science, electronic engineering or other related subjects.
  • Strong analytical and mathematical skills are essential, as well as good programming skills in one or more programming languages e.g. Python, C/C++ and Java.
  • Good understanding of signal processing and machine-learning algorithms is essential.
  • Proficiency in spoken and written English and strong communication skills are essential.
  • Experience or interest in conducting healthcare related experiments, working with people and data collection from wearable sensors are essential.

Desirable

  • An MSc with Distinction or Merit is desirable.
  • Practical skills with integrating embedded systems and sensors relevant for wearable systems.
  • Experience of using the most recent and advanced AI, fuzzy logic and deep learning techniques.
  • Strong motivation to be involved in a project with a clinical background and data analysis skills.

How to apply

You can apply for this postgraduate research opportunity online.

Please include your CV, covering letter, personal statement, and transcripts of UG and Masters degrees in your application.

The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.

Instruction to applicants

When you apply online you will be prompted to fill out several boxes in the form:

  • For "Course title" please put one of the following:
    • PhD Computer Science and Electronics
    • PhD Computer Science
    • PhD Electronic Systems Engineering.
  • For "Proposed research topic or area of research" please put the title of this studentship (SCH41: Intelligent wellness and strength training using sensor fusion based approaches)
  • For "If you have contacted a potential supervisor..." please put the name of the lead supervisor (Dr Delaram Jarchi).

If you have any informal queries about this opportunity please email the lead supervisor, Dr Delaram Jarchi (delaram.jarchi@essex.ac.uk)

You can find the terms and conditions of this studentship here (PDF).

Student and academic working at a board together
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