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Intelligent wellness and strength training using sensor fusion based approaches

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  • Full or part time
    Dr D Jarchi
    Ms I Easton
    Dr F Doctor
    Dr X Zhai
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

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.


Dr Delaram Jarchi – Lead Supervisor
Izzie Easton – Co-Supervisor
Dr Faiyaz Doctor – Co-Supervisor
Dr Xiaojun Zhai – Co-Supervisor
Professor Klaus McDonald-Maier – Co-Supervisor


- 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.

- 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.

Find out more about this studentship and information on how to apply on our website (

Funding Notes

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 (£15,009 in 2019-20, stipend for 2020-21 tbc).

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