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.

Some of our latest works were exhibited at the 2015 Royal Society Summer Science Exhibition.


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  • Conference paper
    Kassanos P, Seichepine F, Yang G-Z, 2019,

    Characterization and Modeling of a Flexible Tetrapolar Bioimpedance Sensor and Measurements of Intestinal Tissues

    , 19th Annual IEEE International Conference on Bioinformatics and Bioengineering (BIBE), Publisher: IEEE, Pages: 686-690, ISSN: 2471-7819
  • Conference paper
    Lo FP-W, Sun Y, Lo B, 2019,

    Depth estimation based on a single close-up image with volumetric annotations in the wild: a pilot study

    , IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Publisher: IEEE, Pages: 513-518, ISSN: 2159-6255

    A novel depth estimation technique based on a single close-up image is proposed in this paper for better understanding of the geometry of an unknown scene. Previous works focus mainly on depth estimation from global view information. Our technique, which is designed based on a deep neural network framework, utilizes monocular color images with volumetric annotations to train a two-stage neural network to estimate the depth information from close-up images. RGBVOL, a database of RGB images with volumetric annotations, has also been constructed by our group to validate the proposed methodology. Compared to previous depth estimation techniques, our method improves the accuracy of depth estimation under the condition that global cues of the scene are not available due to viewing angle and distance constraints.

  • 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

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