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In the past decade, the prevalence of unhealthy lifestyles and globalization have increased the number of people living with cardiovascular disorders, asthma and diabetes, while making them more prone to outbreaks of respiratory diseases. Such health conditions can affect each person’s life and can even shut down key aspects of the economy as we have recently experienced due to the global COVID-19 outbreak. Fortunately, wireless body sensor networks (WBSNs) have emerged in the past decade offering the capability to remotely record and analyse vital body signals, which is essential for the monitoring of each patient’s condition and even for the diagnosis of the onset of critical symptoms. The analysis of the electrocardiograms (ECGs) has been key in the monitoring and early detection of not only heart related disorders but also flu like symptoms. Currently, most of the ECG analysis is limited on time domain due to the high complexity of the power spectral analysis (PSA) of heart-rate in frequency-domain, which is recognized as a powerful tool for evaluating many autonomous nervous system activities. The primary aim of this project is the development of a scalable PSA system of heart-rate variability for health monitoring as well as person’s authentication. The developed PSA algorithm will be able to scale its effort depending on the required vital features that could be associated with the disease type and severity as well as the available hardware resources at the edge/cloud. The new features extracted in the frequency domain will be used to train machine learning based algorithms for the evaluation of patient’s conditions and the detection of critical health conditions such as arrythmias. Finally, the extracted heart features will also be used for authenticating each person and enhancing the efficacy of existing ECG based security algorithms which currently use only time domain information.
The project is interdisciplinary and intersectoral bringing together experts on a) low power biomedical system design from Queen’s ECIT Global Research Institute, b) on diabetes and cardiovascular disease detection and prevention from Queen’s Wellcome-Wolfson Institute for Experimental Medicine as well as c) on Artificial Intelligence and wellness monitoring from B-Secur company.
Employment & Salary:
The researcher will be employed as staff, with the title of Early-Career-Researcher, of Queen’s University, under an enhanced renumeration package of £28925/year! The researcher will have access to a specialized academic and industrial training program and join a team of 20 other ECRs on smart networked societies.
The researcher will join a team with more than 35 publications in the past 3 years on top-tier venues including a recent patent on low complexity bio-signal analysis, a best-paper award at IEEE DATE conference, an ACM SRC prize etc. and must be motivated to pursue publications.
Eligibility Criteria:
Applicants must be in the first four years of their research careers and must not have been awarded a doctoral degree. Researchers shall not have resided or carried out their main activity in United-Kingdom for more than 12 months in the past 3 years.
Desirable Criteria:
- Evidence of exceptional academic achievements
- Evidence of outstanding skills relevant to the position, i.e. embedded-systems, c/c++, python, signal-processing and/or machine learning algorithms, vhdl/verilog, past experience with ASIC/FPGA design flows.
- Have already a publications or eager to publish at top-tier conferences/journals.
Interested applicants that meet the eligibility criteria must apply to the link with a CV and cover letter explaining how they meet the desirable criteria.
Applicants are also strongly encouraged to contact g.karakonstantis@qub.ac.uk with a CV and a short description of their relevant skills to indicate their interest.
