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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  • Journal article
    Kong L, Evans C, Su L, Elson DS, Wei Xet al., 2022,

    Special issue on translational biophotonics

    , Journal of Physics D: Applied Physics, Vol: 55, ISSN: 0022-3727

    This special issue on 'Translational Biophotonics' was initiated when COVID-19 started to spread worldwide in early 2020, with the aim of introducing the advances in optical tools that have the ability to transform clinical diagnostics, surgical guidance, and therapeutic approaches that together can have a profound impact on global health. This issue achieves this goal comprehensively, covering various topics including optical techniques for clinical diagnostics, monitoring and treatment, in addition to fundamental studies in biomedicine.

  • Journal article
    Lam K, Chen J, Wang Z, Iqbal F, Darzi A, Lo B, Purkayastha S, Kinross Jet al., 2022,

    Machine learning for technical skill assessment in surgery: a systematic review

    , npj Digital Medicine, Vol: 5, ISSN: 2398-6352

    Accurate and objective performance assessment is essential for both trainees and certified surgeons. However, existing methods can be time consuming, labor intensive and subject to bias. Machine learning (ML) has the potential to provide rapid, automated and reproducible feedback without the need for expert reviewers. We aimed to systematically review the literature and determine the ML techniques used for technical surgical skill assessment and identify challenges and barriers in the field. A systematic literature search, in accordance with the PRISMA statement, was performed to identify studies detailing the use of ML for technical skill assessment in surgery. Of the 1896 studies that were retrieved, 66 studies were included. The most common ML methods used were Hidden Markov Models (HMM, 14/66), Support Vector Machines (SVM, 17/66) and Artificial Neural Networks (ANN, 17/66). 40/66 studies used kinematic data, 19/66 used video or image data, and 7/66 used both. Studies assessed performance of benchtop tasks (48/66), simulator tasks (10/66), and real-life surgery (8/66). Accuracy rates of over 80% were achieved, although tasks and participants varied between studies. Barriers to progress in the field included a focus on basic tasks, lack of standardization between studies, and lack of datasets. ML has the potential to produce accurate and objective surgical skill assessment through the use of methods including HMM, SVM, and ANN. Future ML-based assessment tools should move beyond the assessment ofbasic tasks and towards real-life surgery and provide interpretable feedback with clinical value for the surgeon.

  • Journal article
    Gkouzionis I, Nazarian S, Kawka M, Darzi A, Patel N, Peters C, Elson Det al., 2022,

    Real-time tracking of a diffuse reflectance spectroscopy probe used to aid histological validation of margin assessment in upper gastrointestinal cancer resection surgery

    , Journal of Biomedical Optics, Vol: 27, ISSN: 1083-3668

    Significance: Diffuse reflectance spectroscopy (DRS) allows discrimination of tissue type. Its application is limited by the inability to mark the scanned tissue and the lack of real-time measurements.Aim: This study aimed to develop a real-time tracking system to enable localization of a DRS probe to aid the classification of tumor and non-tumor tissue.Approach: A green-colored marker attached to the DRS probe was detected using hue-saturation-value (HSV) segmentation. A live, augmented view of tracked optical biopsy sites was recorded in real time. Supervised classifiers were evaluated in terms of sensitivity, specificity, and overall accuracy. A developed software was used for data collection, processing, and statistical analysis.Results: The measured root mean square error (RMSE) of DRS probe tip tracking was 1.18  ±  0.58  mm and 1.05  ±  0.28  mm for the x and y dimensions, respectively. The diagnostic accuracy of the system to classify tumor and non-tumor tissue in real time was 94% for stomach and 96% for the esophagus.Conclusions: We have successfully developed a real-time tracking and classification system for a DRS probe. When used on stomach and esophageal tissue for tumor detection, the accuracy derived demonstrates the strength and clinical value of the technique to aid margin assessment in cancer resection surgery.

  • Journal article
    Thompson A, Bourke C, Robertson R, Shivakumar N, Edwards C, Preston T, Holmes E, Paul K, Gary F, Douglas Met al., 2021,

    Understanding the role of the gut in undernutrition: what can technology tell us?

    , Gut, Vol: 70, Pages: 1580-1594, ISSN: 0017-5749

    Gut function remains largely underinvestigated in undernutrition, despite its critical role in essential nutrient digestion, absorption and assimilation. In areas of high enteropathogen burden, alterations in gut barrier function and subsequent inflammatory effects are observable but remain poorly characterised. Environmental enteropathy (EE)—a condition that affects both gut morphology and function and is characterised by blunted villi, inflammation and increased permeability—is thought to play a role in impaired linear growth (stunting) and severe acute malnutrition. However, the lack of tools to quantitatively characterise gut functional capacity has hampered both our understanding of gut pathogenesis in undernutrition and evaluation of gut-targeted therapies to accelerate nutritional recovery. Here we survey the technology landscape for potential solutions to improve assessment of gut function, focussing on devices that could be deployed at point-of-care in low-income and middle-income countries (LMICs). We assess the potential for technological innovation to assess gut morphology, function, barrier integrity and immune response in undernutrition, and highlight the approaches that are currently most suitable for deployment and development. This article focuses on EE and undernutrition in LMICs, but many of these technologies may also become useful in monitoring of other gut pathologies.

  • Conference paper
    Hu M, Kassanos P, Keshavarz M, Yeatman E, Lo Bet al., 2021,

    Electrical and Mechanical Characterization of Carbon-Based Elastomeric Composites for Printed Sensors and Electronics

    Printing technologies have attracted significant interest in recent years, particularly for the development of flexible and stretchable electronics and sensors. Conductive elastomeric composites are a popular choice for these new generations of devices. This paper examines the electrical and mechanical properties of elastomeric composites of polydimethylsiloxane (PDMS), an insulating elastomer, with carbon-based fillers (graphite powder and various types of carbon black, CB), as a function of their composition. The results can direct the choice of material composition to address specific device and application requirements. Molding and stencil printing are used to demonstrate their use.

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