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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  • 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,


    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.

  • Journal article
    Akay M, Coatrieux G, Hao Y, Fotiadis DI, Laine A, Lo B, Nikita KS, Noury N, Rodrigues J, Wang MDet al., 2016,

    Guest Editorial: MobiHealth 2014, IEEE HealthCom 2014, and IEEE BHI 2014

    , IEEE Journal of Biomedical and Health Informatics, Vol: 20, Pages: 731-732, ISSN: 2168-2208

    The papers in this special section were presented at three well-known conferences organized in 2014: EAI Mobihealth, IEEE HealthCom, and IEEE Biomedical and Health Informatics. EAI Mobihealth is an annually organized conference, which started in 2010, to address the demands of the rapidly evolving disciplines of wireless communications, mobile computing, and sensing technologies in healthcare. The IEEE-Healthcom is held every year since 1999 in different countries in Asia, Europe, and in America. It aims at bringing together interested parties working in the field of healthcare to exchange ideas, discuss innovative and emerging solutions, and develop collaborations. The IEEE Biomedical Health Informatics Conference started in 2013 and is organized every year providing the forum to showcase enabling technologies of computing, devices, imaging, sensors, and systems that optimize the acquisition, transmission, processing, storage, retrieval, visualization, and analysis of medical data. The aim of this special section is to present an overview of recent advances in sensing technologies, monitoring of patients, security and privacy of data transfer, provision of collaborative environments, data gathering and analysis from various sources, and predictive models, which all finally target the best strategy for patient monitoring and treatment.

  • Journal article
    Andreu-Perez J, Leff DR, Shetty K, Darzi A, Yang GZet al., 2016,

    Disparity in frontal lobe connectivity on a complex bimanual motor task aids in classification of operator skill level.

    , Brain Connectivity, Vol: 6, Pages: 375-388, ISSN: 2158-0022

    Objective metrics of technical performance (e.g., dexterity, time, and path length) are insufficient to fully characterize operator skill level, which may be encoded deep within neural function. Unlike reports that capture plasticity across days or weeks, this articles studies long-term plasticity in functional connectivity that occurs over years of professional task practice. Optical neuroimaging data are acquired from professional surgeons of varying experience on a complex bimanual coordination task with the aim of investigating learning-related disparity in frontal lobe functional connectivity that arises as a consequence of motor skill level. The results suggest that prefrontal and premotor seed connectivity is more critical during naïve versus expert performance. Given learning-related differences in connectivity, a least-squares support vector machine with a radial basis function kernel is employed to evaluate skill level using connectivity data. The results demonstrate discrimination of operator skill level with accuracy ≥0.82 and Multiclass Matthew's Correlation Coefficient ≥0.70. Furthermore, these indices are improved when local (i.e., within-region) rather than inter-regional (i.e., between-region) frontal connectivity is considered (p = 0.002). The results suggest that it is possible to classify operator skill level with good accuracy from functional connectivity data, upon which objective assessment and neurofeedback may be used to improve operator performance during technical skill training.

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