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
    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
    Han 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.

  • Journal article
    Smith M, Withnall R, Anastasova S, Gil-Rosa B, Blackadder-Coward J, Taylor Net al., 2021,

    Developing a multimodal biosensor for remote physiological monitoring.

    , BMJ Mil Health

    INTRODUCTION: Several UK military expeditions have successfully used physiological sensors to monitor participant's physiological responses to challenging environmental conditions. This article describes the development and trial of a multimodal wearable biosensor that was used during the first all-female unassisted ski crossing of the Antarctic land mass. The project successfully transmitted remote real-time physiological data back to the UK. The ergonomic and technical lessons identified have informed recommendations for future wearable devices. METHOD: The biosensor devices were designed to be continuously worn against the skin and capture: HR, ECG, body surface temperature, bioimpedance, perspiration pH, sodium, lactate and glucose. The data were transmitted from the devices to an android smartphone using near-field technology. A custom-built App running on an android smartphone managed the secure transmission of the data to a UK research centre, using a commercially available satellite transceiver. RESULTS: Real-time physiological data, captured by the multimodal device, was successfully transmitted back to a UK research control centre on 6 occasions. Postexpedition feedback from the participants has contributed to the ergonomic and technical refinement of the next generation of devices. CONCLUSION: The future success of wearable technologies lies in establishing clinical confidence in the quality of the measured data and the accurate interpretation of those data in the context of the individual, the environment and activity being undertaken. In the near future, wearable physiological monitoring could improve point-of-care diagnostic accuracy and inform critical medical and command decisions.

  • Journal article
    Gu X, Guo Y, Deligianni F, Lo B, Yang G-Zet al., 2021,

    Cross-subject and cross-modal transfer for generalized abnormal gait pattern recognition

    , IEEE Transactions on Neural Networks and Learning Systems, Vol: 32, Pages: 546-560, ISSN: 1045-9227

    For abnormal gait recognition, pattern-specific features indicating abnormalities are interleaved with the subject-specific differences representing biometric traits. Deep representations are, therefore, prone to overfitting, and the models derived cannot generalize well to new subjects. Furthermore, there is limited availability of abnormal gait data obtained from precise Motion Capture (Mocap) systems because of regulatory issues and slow adaptation of new technologies in health care. On the other hand, data captured from markerless vision sensors or wearable sensors can be obtained in home environments, but noises from such devices may prevent the effective extraction of relevant features. To address these challenges, we propose a cascade of deep architectures that can encode cross-modal and cross-subject transfer for abnormal gait recognition. Cross-modal transfer maps noisy data obtained from RGBD and wearable sensors to accurate 4-D representations of the lower limb and joints obtained from the Mocap system. Subsequently, cross-subject transfer allows disentangling subject-specific from abnormal pattern-specific gait features based on a multiencoder autoencoder architecture. To validate the proposed methodology, we obtained multimodal gait data based on a multicamera motion capture system along with synchronized recordings of electromyography (EMG) data and 4-D skeleton data extracted from a single RGBD camera. Classification accuracy was improved significantly in both Mocap and noisy modalities.

  • Journal article
    Kassanos P, Seichepine F, Yang G-Z, 2021,

    A Comparison of Front-End Amplifiers for Tetrapolar Bioimpedance Measurements

    , IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, Vol: 70, ISSN: 0018-9456

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