Non-Invasive Sensor-fusion for Detecting and Monitoring COVID-19 Disease Symptoms

Non-Invasive Sensor-fusion for Detecting and Monitoring COVID-19 Disease Symptoms

Eligibility: UK/EU graduates with the required entry requirements

PhD funding award: Bursary plus tuition fees (UK/EU)

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: 14th August 2020

Interview date: Will be confirmed to shortlisted candidates

Start date: September 2020

For further details contact: Dr Syed Aziz Shah 


Introduction

The coronavirus disease (COVID-19) emerged towards the end of last year, which causes severe breathing problems has affected millions of people across the globe. The total number of people infected by the COVID – 19 is rising exponentially. Until today, more than 6 million cases have been reported around the world that resulted in approximately 300k deaths in over 200 countries and territories, with fatality rate of ~5% according to World Health Organization (WHO). Non-invasive health monitoring has become an essential tool for our well-being, and as a result it has emerged as a key area of interest in recent years. Public-sector hospitals face a dire shortage of equipment, facilities, and trained staff to deal with COVID – 19 disease. Consequently, detecting this particular disease symptoms has become extremely challenging in prevailing circumstances. Through the this project, we aim to use the recent advances in radio frequency (RF) wireless sensing to design, develop, and deploy a ubiquitous wireless sensing system in indoor settings (hospitals, care-centers and quarantine centers) that will be primarily targeted to detect symptoms of COVID – 19 such as abnormal respiratory rate, coughing and rise in body temperature  servicing several patients at a time. More specifically, the proposed wireless sensing system will attempt to use ambient radio signals in the environment coupled with artificial intelligent (AI) and machine learning to detect COVID – 19 symptoms (contrary to camera’s to overcome privacy concern. In addition to generating cutting-edge research, the project aims to develop technology that has a potential for significant societal impact in the health sector for improving quality of life.

Details of the project

The objective of this project is to articulate an innovative solution to detect COVID symptoms and protect their health by timely detecting anomalies such as abnormal respiratory rate, coughing and rise in body temperature. The proposed system will consist of three layers. The first layer consists of a gateway solution that will enable the care-givers or nurses to automatically detect and monitor abnormal vital signs to back-end connectivity with the hospital, care-centers or quarantine centers with data collected by USRP wired with a computer enabled with cloud computing to store data . The gateway should only work with designated and authenticated devices such as orthogonal frequency division multiplexing data carriers, software defined radios (USRP in this case), must provide access control by preserving privacy of the patients, and must be able to filter unsuitable personally identifiable information.

Benefits

Training and Development

The successful candidate will receive comprehensive research training including technical, personal and professional skills.

All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities. 

Candidate specification 

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average. 
    PLUS 
    the potential to engage in innovative research and to complete the PhD within a 3.5 years
  • a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)

How to apply

All applications require full supporting documentation, a covering letter, plus an up to 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project. 

To find out more about the project please contact Dr Syed Aziz Shah

Apply to Coventry University