Configuration and algorithms for physiological and mental healthcare monitoring systems with wearable sensors


An opportunity to apply for a full-time PhD in the Faculty of Environment and Technology. The studentship will be funded/part-funded by the Faculty of Environment and Technology: Ref 2022-JAN-FET03.

The expected start date of this studentship is January 2022.

The closing date for applications is 14 October 2021.

About the studentship

Smart wearable devices have started playing an important roles for monitoring of the status of health and wellbeing of people worldwide. These roles will be extended to medical diagnostics and advisory services while the heath data measured by wearable devices can be engineered with advanced configurations and processed by artificial intelligence technology. Accurate and automatic measurements of vital health data by wearable sensors creates a foundation for developments of healthcare roles for wearable devices and determines acceptance of the functionalities by healthcare and medical authorities, service providers and market.

Wearable sensors for vital data measurements include Photoplethysmography (PPG) and Electrocardiogram (ECG). PPG measures changes of pulse and blood volume and ECG can be used to measure how the heart is functioning. PPG and ECG can either separately and together be used to monitor, for example, heart rate and rhythm, Sp02, respiration rate. PPG can be set by a wearable device to automatically taking measurements while ECG offers a higher accuracy for various measurements but requires a closed loop circuit, normally using a finger of another hand to press an ECG electrode, to complete a measurement.

This project will explore the configuration and algorithms to model and integrate the advantages of PPG and ECG sensors offering a higher accuracy than single PPG and automatic measurements. The algorithms should enable a wearable device with PPG and ECG sensors installed to work in an automatic mode to form various physiological and mental healthcare monitoring systems, to empower remote and digital healthcare and diagnostic functions, and achieve higher market acceptance. There are two main research objectives:

  1. Configuration and algorithms
    It is anticipated that the outcomes of the PhD project will be used to develop a more sophisticated distributed wearable healthcare system. The distributed wearable healthcare system will be integrated by a smart gateway watch and a number of different wearable sensors distributed in various places of a human body and working together to achieve automatic and continuous measurements of multiple healthcare data with improved accuracies. The system, together with additional configurations, algorithms and architecture of structure, hardware and software developed can be incorporated for a wide range of digital healthcare and digital doctor services.

  2. Digital and remote healthcare
    It is also anticipated that the outcomes of the PhD project will be to develop a smart wearable healthcare Internet of Things (IoT) healthcare system. The smart wearable IoT system will be a cloud-based system with comprehensive capability for data driven processing by artificial intelligence technology and models. A smart wearable healthcare IoT system, with the healthcare algorithms processed with artificial intelligence and big data machine learning approaches, will become a powerful system for physiological and mental healthcare and chronical disease monitoring. A smart wearable IoT system, if working together with a healthcare robotic can bring a revolution for healthcare and medical industry achieving significantly higher quality of healthcare and disease treatment services.

Candidate requirements

Applicants must hold/achieve a minimum of a Masters degree (or international equivalent) in a relevant discipline.

Basic skills and knowledge required


Excellent analytical skills and Artificial Intelligence (AI) expertise with a background understanding in one or more of the following:

  • system modelling and identification
  • medical/healthcare ethics
  • software and systems
  • sensor and measurement
  • for international students, minimum IELTS 6.5 or equivalent


  • experience of interdisciplinary working

For an informal discussion about the studentship, please email Professor Quan Min Zhu PhD, CEng, FIET, FHEA (


The studentship is available from January 2022 for a period of three years, subject to satisfactory progress and includes a tax exempt stipend, which is currently £15,609 per annum.

In addition, full-time tuition fees will be covered for up to three years.


Applicants must have a good first degree or, ideally, a Masters. Students from under-represented groups are particularly encouraged to apply. The studentship is available both for UK and overseas applicants.

A recognised English language qualification is required.

How to apply

Please submit your application online. When prompted, use the reference number 2022-JAN-FET03.

Applicants have the opportunity to discuss their studentship with Directors of Studies via a webinar to take place on Wednesday 8 September 2021 from 10:00 to 12:00 BST. Please use the registration form to reserve your place at this webinar.

Supporting documentation: You will need to upload your research proposal, all your degree certificates and transcripts and your proof of English language proficiency as attachments to your application, so please have these available when you complete the application form. 

References: You will need to provide details of two referees as part of your application. At least one referee must be an academic referee from the institution that conferred your highest degree. Your referee will be asked for a reference at the time you submit your application, so please ensure that your nominated referees are willing and able to provide references within 14 days of your application being submitted.

Closing date

The closing date for applications is 14 October 2021.

Further information

It is expected that interviews will take place on the weeks commencing 1 and 8 November 2021. If you have not heard from us by 28 October 2021, we thank you for your application but, on this occasion, you have not been successful.

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