Sensing

A primary motivation of our research is the monitoring of physical, physiological, and biochemical parameters - in any environment and without activity restriction and behaviour modification - through using miniaturised, wireless Body Sensor Networks (BSN). Key research issues that are currently being addressed include novel sensor designs, ultra-low power microprocessor and wireless platforms, energy scavenging, biocompatibility, system integration and miniaturisation, processing-on-node technologies combined with novel ASIC design, autonomic sensor networks and light-weight communication protocols. Our research is aimed at addressing the future needs of life-long health, wellbeing and healthcare, particularly those related to demographic changes associated with an ageing population and patients with chronic illnesses. This research theme is therefore closely aligned with the IGHI’s vision of providing safe, effective and accessible technologies for both developed and developing countries.

2 column colour block - Research areas

Body Sensor Networks

Body Sensor Networks

The last decade has witnessed a rapid surge of interest in new sensing and monitoring devices for healthcare and the use of wireless wearable devices for clinical applications. One key development in this area is implantable in vivo monitoring and intervention devices.
Surgical Implants

Surgical Implants

One of the major focuses of the Hamlyn Centre is the development of miniaturised sensors embodied either as smart surgical appliances or implants to enhance the safety of surgical procedures, enhance patient care, and also the patient's quality of life.

2 column colour block - Research areas 2

ESPRIT

IoT and Smart Environments

Our main research in IoT and smart environments is focussed on the challenges of ambient sensing through the introduction of novel sensing platforms, self-configuration approaches, self-adaptive techniques, and seamless integration with wearable sensors.
Smart Environments

ESPRIT

The vision of ESPRIT is to position UK at the forefront of pervasive sensing in elite sports and promote its wider application in public life-long health, wellbeing and healthcare. Our work addresses some of the key challenges related to miniaturised wearable sensors for sports.

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  • Conference paper
    Elson D, 2019,

    Optical theranostics: image-guided cancer thermal therapy using light (invited)

    , Computer Assisted Radiology and Surgery (Europe's Got Talent)
  • Conference paper
    Adshead J, Oldfield F, Hadaschik B, Everaerts W, Mestre-Fusco A, Newbery M, Elson D, Grootendorst M, Vyas K, Fumado L, Harke Net al., 2019,

    Usability and technical feasibility evaluation of a tethered laparoscopic gamma probe for radioguided surgery in prostate cancer: a pelvic phantom and porcine model study (19-1271)

    , Annual Meeting of the American Urological Association Education and Research Inc.
  • Conference paper
    Elson D, Adshead J, Oldfield F, Hadaschik B, Everaerts W, Mestre A, Newbery M, Grootendorst M, Vyas K, Fumado L, Harke Net al., 2019,

    A tethered laparoscopic gamma probe for radioguided surgery in prostate cancer – usability and technical feasibility evaluation in a pelvic phantom and porcine model (best poster - New Technologies)

    , 34th Annual European Association of Urology Congress
  • Conference paper
    Kassanos P, Seichepine F, Keshavarz M, Yang G-Zet al., 2019,

    Towards a Flexible Wrist-Worn Thermotherapy and Thermoregulation Device

    , 19th Annual IEEE International Conference on Bioinformatics and Bioengineering (BIBE), Publisher: IEEE, Pages: 644-648, ISSN: 2471-7819
  • Conference paper
    Rother R, Sun Y, Lo B, 2019,

    Internet of things based pervasive sensing of psychological anxiety via wearable devices under naturalistic settings

    Psychological anxiety is highly prevalent in dementia patients, and reduces the quality of life of the afflicted and their caregivers. Still, not much technological research has been conducted to address this issue in the field. This study aimed to develop a wearable system which could detect anxiety in dementia patients under naturalistic settings, and alert caregivers via a web application. The wearable system designed included an accelerometer, pulse sensor, skin conductivity sensor, and a camera. The readings would be fed into a machine learning model to output an anxiety state prediction. One participant was trialled under both controlled and naturalistic settings. The model achieved classification accuracy of 0.95 under controlled settings and 0.82 under naturalistic settings. Implications of the study include achieving relatively high classification accuracy under naturalistic settings, and the novel discovery of movement as a potential predictor of anxiety states.

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