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, 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.

  • Journal article
    Giataganas P, Hughes M, Payne C, Wisanuvej P, Temelkuran B, Yang GZet al., 2019,

    Intraoperative robotic-assisted large-area high-speed microscopic imaging and intervention

    , IEEE Transactions on Biomedical Engineering, Vol: 66, Pages: 208-216, ISSN: 0018-9294

    IEEE Objective: Probe-based confocal endomicroscopy is an emerging high-magnification optical imaging technique that provides in-vivo and in-situ cellular-level imaging for real-time assessment of tissue pathology. Endomicroscopy could potentially be used for intraoperative surgical guidance, but it is challenging to assess a surgical site using individual microscopic images due to the limited field-of-view and difficulties associated with manually manipulating the probe. Methods: In this paper, a novel robotic device for large-area endomicroscopy imaging is proposed, demonstrating a rapid, but highly accurate, scanning mechanism with image-based motion control which is able to generate histology-like endomicroscopy mosaics. The device also includes, for the first time in robotic-assisted endomicroscopy, the capability to ablate tissue without the need for an additional tool. Results: The device achieves pre-programmed trajectories with positioning accuracy of less than 30um, the image-based approach demonstrated that it can suppress random motion disturbances up to 1.25mm/s. Mosaics are presented from a range of ex-vivo human and animal tissues, over areas of more than 3mm<formula><tex>$^2$</tex></formula>, scanned in approximate 10s. Conclusion: This work demonstrates the potential of the proposed instrument to generate large-area, high-resolution microscopic images for intraoperative tissue identification and margin assessment. Significance: This approach presents an important alternative to current histology techniques, significantly reducing the tissue assessment time, while simultaneously providing the capability to mark and ablate suspicious areas intraoperatively.

  • Conference paper
    Kassanos P, Anastasova S, Yang G-Z, 2018,

    Towards Low-Cost Cell Culturing Platforms with Integrated Sensing Capabilities

    , IEEE Biomedical Circuits and Systems Conference (BioCAS) - Advanced Systems for Enhancing Human Health, Publisher: IEEE, Pages: 327-330, ISSN: 2163-4025
  • Journal article
    Lo FP-W, Sun Y, Qiu J, Lo Bet al., 2018,

    Food volume estimation based on deep learning view synthesis from a single depth map

    , Nutrients, Vol: 10, Pages: 1-20, ISSN: 2072-6643

    An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume is required. For volume estimation, previous research studies mostly focused on using model-based or stereo-based approaches which rely on manual intervention or require users to capture multiple frames from different viewing angles which can be tedious. In this paper, a view synthesis approach based on deep learning is proposed to reconstruct 3D point clouds of food items and estimate the volume from a single depth image. A distinct neural network is designed to use a depth image from one viewing angle to predict another depth image captured from the corresponding opposite viewing angle. The whole 3D point cloud map is then reconstructed by fusing the initial data points with the synthesized points of the object items through the proposed point cloud completion and Iterative Closest Point (ICP) algorithms. Furthermore, a database with depth images of food object items captured from different viewing angles is constructed with image rendering and used to validate the proposed neural network. The methodology is then evaluated by comparing the volume estimated by the synthesized 3D point cloud with the ground truth volume of the object items

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