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 articleBai W, Cursi F, Guo X, et al., 2022,
Task-Based LSTM Kinematic Modeling for a Tendon-Driven Flexible Surgical Robot, IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, Vol: 4, Pages: 339-342
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- Citations: 2
Journal articleKong L, Evans C, Su L, et 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 articleLam K, Chen J, Wang Z, et 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 articleGkouzionis I, Nazarian S, Kawka M, et 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.
Conference paperHan J, Gu X, Lo B, 2021,
Semi-supervised contrastive learning for generalizable motor imagery eeg classification, 17th IEEE International Conference on Wearable and Implantable Body Sensor Networks, Publisher: IEEE
Electroencephalography (EEG) is one of the most widely used brain-activity recording methods in non-invasive brain-machine interfaces (BCIs). However, EEG data is highly nonlinear, and its datasets often suffer from issues such as data heterogeneity, label uncertainty and data/label scarcity. To address these, we propose a domain independent, end-to-end semi-supervised learning framework with contrastive learning and adversarial training strategies. Our method was evaluated in experiments with different amounts of labels and an ablation study in a motor imagery EEG dataset. The experiments demonstrate that the proposed framework with two different backbone deep neural networks show improved performance over their supervised counterparts under the same condition.
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