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
    Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang Get al.,

    Deep learning for health informatics

    , IEEE Journal of Biomedical and Health Informatics, Vol: 21, Pages: 4-21, ISSN: 2168-2208

    With a massive influx of multimodality data, the roleof data analytics in health informatics has grown rapidly in thelast decade. This has also prompted increasing interests in thegeneration of analytical, data driven models based on machinelearning in health informatics. Deep learning, a technique withits foundation in artificial neural networks, is emerging in recentyears as a powerful tool for machine learning, promising toreshape the future of artificial intelligence. Rapid improvementsin computational power, fast data storage and parallelization havealso contributed to the rapid uptake of the technology in additionto its predictive power and ability to generate automaticallyoptimized high-level features and semantic interpretation fromthe input data. This article presents a comprehensive up-to-datereview of research employing deep learning in health informatics,providing a critical analysis of the relative merit and potentialpitfalls of the technique as well as its future outlook. The papermainly focuses on key applications of deep learning in the fields oftranslational bioinformatics, medical imaging, pervasive sensing,medical informatics and public health.

  • Journal article
    Anastasova S, Crewther B, Bembnowicz P, Curto V, Ip HMD, Rosa B, Yang GZet al., 2016,

    A Wearable Multisensing Patch for Continuous Sweat Monitoring

    , Biosensors and Bioelectronics, Vol: 93, Pages: 139-145, ISSN: 0956-5663

    In sport, exercise and healthcare settings, there is a need for continuous, non-invasive monitoring of biomarkers to assess human performance, health and wellbeing. Here we report the development of a flexible microfluidic platform with fully integrated sensing for on-body testing of human sweat. The system can simultaneously and selectively measure metabolite (e.g. lactate) and electrolytes (e.g. pH, sodium) together with temperature sensing for internal calibration. The construction of the platform is designed such that continuous flow of sweat can pass through an array of flexible microneedle type of sensors (50 µm diameter) incorporated in a microfluidic channel. Potentiometric sodium ion sensors were developed using a polyvinyl chloride (PVC) functional membrane deposited on an electrochemically deposited internal layer of Poly(3,4-ethylenedioxythiophene) (PEDOT) polymer. The pH sensing layer is based on a highly sensitive membrane of iridium oxide (IrOx). The amperometric-based lactate sensor consists of doped enzymes deposited on top of a semipermeable copolymer mebrane and outer polyurethane layers. Real-time data were collected from human subjects during cycle ergometry and treadmill running. A detailed comparison of sodium, lactate and cortisol from saliva is reported, demonstrating the potential of the multi-sensing platform for tracking these outcomes. In summary, a fully integrated sensor for continuous, simultaneous and selective measurement of sweat metabolites, electrolytes and temperature was achieved using a flexible microfluidic platform. This system can also transmit information wirelessly for ease of collection and storage, with the potential for real-time data analytics.

  • Conference paper
    Gil Rosa BM, Yang GZ, 2016,

    Active implantable sensor powered by ultrasounds with application in the monitoring of physiological parameters for soft tissues

    , Body Sensor Network, Publisher: IEEE, ISSN: 2376-8894

    Ultrasound imaging is a proven diagnostic tool to assess a myriad of physiological and pathological conditions in patients. Throughout the years, ultrasounds have been used as a passive recording modality where the backscattered echo arising from the interaction of the sound waves with the acoustic properties of the biological tissues helps to identify them. Apart from a wide range of therapeutic applications, the acoustic beam has not yet been explored to actuate within the biological environment in an active way. In this paper we present an implantable electronic device to be actuated remotely by ultrasounds with capabilities for measuring several physiological parameters of tissues: pH, temperature, electrolyte concentration and biopotentials. The small factory form device (with no attached batteries) harvests energy from the incoming ultrasound waves and uses it to power the embedded electronics. It operates from voltage levels as low as 0.8 V and consuming a total current of 60 μA (or an average power consumption of 84 μW) in the active mode when deployed at a distance of 3 cm from the active source of ultrasounds in vitro, excited by a sinusoid at 400 kHz with power density of 20 mWcm-2. The sensor can be actuated by a specifically-designed readout device (as detailed in this paper) or using the traditional medical probes for ultrasound imaging. The actual device can present an alternative to surpass the limitations of inductive and RF-powered sensors implanted in soft tissues.

  • Conference paper
    Huen D, Liu J, Lo B, 2016,

    An integrated wearable robot for tremor suppression with context aware sensing

    , 13th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 312-317, ISSN: 2376-8886

    Abstract:Tremor is a neurological disorder which can significantly impede the daily functions of patients. The available treatments for patients with tremor are mainly pharmacotherapy and neurosurgery, but these treatments often have side effects. A wearable exoskeleton can potentially provide the assistance needed for patients with Parkinsonian or essential tremor to carry out daily activities and enable independent living. This paper presents the design and development of a 3D printed lightweight tremor suppression wearable exoskeleton. One of the major technical challenges for wearable robot is to maintain long battery life meanwhile miniature in size for practical use. This paper proposes an integrated approach where context aware Body Sensor Networks (BSN) sensors are incorporated to characterize voluntary and tremor movement, and detect activities of daily life (ADL). With the contextual information, the system can determine the intention of the user, optimize its control and minimize its power consumption by providing the necessary suppression only when needed. The preliminary result has shown that the wearable robot prototype can reduce the amplitude of simulated tremor by around 77%, and accurately identify different ADL with accuracy above 70%.

  • Patent
    Lo BPL, Yang GZ, Merrifield R, 2016,

    A SENSING SYSTEM

    The present disclosure relates to a system for detecting challenging behaviors, enabling a customizable learning experience, and providing context-aware, intelligent daily assistance for people with learning disabilities.In addition to general health problems, people with learning disabilities are known to have higher incidents of dementia, respiratory diseases, gastrointestinal cancer, ADHD/hyperkinesis and conduct disorders, epilepsy, physical and sensory impairments, dysphagia, poor oral health, and tend to be prone to injuries, accidents and falls. Often due to the lack of expressive skills, people with learning disabilities are more likely to have undiagnosed long-term conditions and which leads to high risk of premature death. With the aim of improving the care of people with learning disabilities, a new wearable sensing system is provided which comprises a new miniaturized wearable sensor, for example that may be worn either as a wrist worn or ear worn sensor, and a seamlessly integrated mobile app that adapts to individual care needs.

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