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
    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.

  • 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 MILITARY HEALTH, ISSN: 2633-3767
  • 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, Pages: 1-14, ISSN: 0018-9456

    Many commercial benchtop impedance analyzers are incapable of acquiring accurate tetrapolar measurements, when large electrode contact impedances are present, as in bioimpedance measurements using electrodes with micrometer-sized features. External front-end amplifiers can help overcome this issue and provide high common-mode rejection ratio (CMRR) and input impedance. Several discrete component-based topologies are proposed in the literature. In this article, these are compared with new alternatives with regard to their performance in measuring known loads in the presence of electrode contact impedance models, to emulate tetrapolar bioimpedance measurements. These models are derived from bipolar impedance measurements taken from the electrodes of a tetrapolar bioimpedance sensor. Comparison with other electrode models used in the literature established that this is a good and challenging model for bioimpedance front-end amplifier evaluation. Among the examined amplifiers, one of the best performances is achieved with one of the proposed topologies based on a custom front-end with no external resistors (AD8066/AD8130). Under the specific testing conditions, it achieved an uncalibrated worst-case absolute measurement deviation of 4.4% magnitude and 4° at 20 Hz, and 2.2% and 7° at 1 MHz accordingly with loads between 10 Ω and 10 kg. Finally, the practical use of the front-end with the impedance analyzer is demonstrated in the characterization of the bioimpedance sensor, in saline solutions of varying conductivities (2.5-20 mS/cm) to obtain its cell constant. This article serves as a guide for evaluating and choosing front-end amplifiers for tetrapolar bioimpedance measurements both with and without impedance analyzers for practical/clinical applications and material/sensor characterization.

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