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Developing AI-based Multi-Modal Sensing Platform and Advanced Signal Processing Techniques to Examine the Impact of Falls on Health Parameters/Vital Signs

   Centre for Intelligent Healthcare

  , ,  Thursday, October 20, 2022  Competition Funded PhD Project (Students Worldwide)

About the Project

Coventry University and A*STAR jointly offer a fully-funded PhD studentships to UK/EU and international students as part of the A*STAR Research Attachment Programme (ARAP).

The successful candidates will join the project ‘Developing AI-based Multi-Modal Sensing Platform and Advanced Signal Processing Techniques to Examine the Impact of Falls on Health Parameters/Vital Signs’ led by Syed Aziz Shah (SDR sensing and machine learning for elderly monitoring) and Professor Dingchang Zheng (medical device and technology development with physiological measurements and bio-signal processing) at Coventry University. 

Aging is a natural, progressive and heterogeneous process that affects the functional ability of a person. Ageing has major effects on most of the physiological systems, including the nervous, metabolic and musculoskeletal systems. Ageing effects are more noticeable in the musculoskeletal system, particularly how people move around a space and with what degree of ease. A general physical and functional weakening in locomotion and mobility increases the risk of fall incidents. Falls are among the most hazardous incidents that can effect an elderly person, ranked the second leading cause of unintentional mortality due to sudden change in vital signs due to fall event.

Despite the inevitable risk, falls could be managed, detected, and even prevented to sustain quality of life and independence. One in five adults are currently aged 60 years or older and are vulnerable to fall. Older adults are also often encouraged to go into nursing homes for full-time care which can be frustrating as many still feel able to stay at home, with “check-ins” by family sufficient. Supporting independent living at home for as long as is safe, is important for older adults’ wellbeing and quality of life.

The radio frequency (RF) sensing, specifically software defined radio (SDR) has recently gained particular interest, as components that have been traditionally implemented in hardware are instead implemented by means of software on a personal computer or embedded system. The SDR also provides contactless monitoring capabilities, with no need for the end-users engagement.

The proposed project will leverage multimodal sensor fusion comprising SDR sensing in conjunction with electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG) sensors to detect different types of falls and abrupt changes in vital signs due to these events. We are looking for highly motivated candidates who have a strong interest in developing innovative healthcare technologies and has a broad understanding of falls detection and vital signs monitoring. The PhD project requires working with clinicians, biomedical engineers, and computer scientists at different stages along the development pathway – and will lead to high-quality publications. It will involve clinical data collection and analysis, and a variety of computing work (advanced signal processing, artificial intelligence and machine learning algorithm development).

  • Throughout the programme the candidates will have a joint supervisory team and work in close collaboration with industry stakeholders in both UK and Singapore.
  • The successful candidates will receive comprehensive research training including technical, personal and professional skills at the Doctoral College and Centre for Research Capability and Development at Coventry University
  • The supervisory team offers a substantial track record in successful doctoral supervision and expertise in the thematic areas identified.

Desirable Specifications

  • The potential to engage in innovative research and to complete the PhD within 4 years;
  • Previous experience of RF sensors and wireless sensing technologies;
  • Knowledge of advanced signal processing, software defined radio, machine learning algorithm design, respiratory disorders, cardiac activity, blood pressure, EEG and ECG measurements;
  • Knowledge of related software (Simulink, Labview, MATLAB and Python);
  • A record of presenting papers at conferences and of publishing peer reviewed research

Entry criteria for applicants to PHD 

  • 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. 


  • 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)
  • Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).  
  • All applications require full supporting documentation, CV, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.  

For further details see: 

Funding Notes

bursary plus tuition fees - UK/EU/International

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