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
    Ding X-R, Zhao N, Yang G-Z, Pettigrew RI, Lo B, Miao F, Li Y, Liu J, Zhang Y-Tet al., 2016,

    Continuous blood pressure measurement from invasive to unobtrusive: celebration of 200th birth anniversary of Carl Ludwig

    , IEEE Journal of Biomedical and Health Informatics, Vol: 20, Pages: 1455-1465, ISSN: 2168-2208

    The year 2016 marks the 200th birth anniversary of Carl Friedrich Wilhelm Ludwig (1816-1895). As one of the most remarkable scientists, Ludwig invented the kymograph, which for the first time enabled the recording of continuous blood pressure (BP), opening the door to the modern study of physiology. Almost a century later, intraarterial BP monitoring through an arterial line has been used clinically. Subsequently, arterial tonometry and volume clamp method were developed and applied in continuous BP measurement in a noninvasive way. In the last two decades, additional efforts have been made to transform the method of unobtrusive continuous BP monitoring without the use of a cuff. This review summarizes the key milestones in continuous BP measurement; that is, kymograph, intraarterial BP monitoring, arterial tonometry, volume clamp method, and cuffless BP technologies. Our emphasis is on recent studies of unobtrusive BP measurements as well as on challenges and future directions.

  • Journal article
    Chen S, Lach J, Lo B, Yang G-Zet al., 2016,

    Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review

    , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 20, Pages: 1521-1537, ISSN: 2168-2194
  • 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
    Berthelot M, Chen C-M, Yang G-Z, Lo Bet al., 2016,

    Wireless wearable self-calibrated sensor for perfusion assessment of myocutaneous tissue

    , 13th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 171-176, ISSN: 2376-8886

    Abstract:Blood flow and perfusion monitoring are critical appraisal to ensure survival of tissue flap after reconstructive surgery. Many techniques have been developed over the years: from optical to chemical, invasive or not, they all have limitations in their price, risks and adaptiveness to the patient. A wireless wearable self-calibrated device, based on near infrared spectroscopy (NIRS) was developed for blood flow and perfusion monitoring contingent on tissue oxygen saturation (StO2). The use of such device is particularly relevant in the case of free flap myocutaneous reconstructive surgery; postoperative monitoring of the flap is crucial for a prompt intervention in case of thrombosis. Although failure rate is low, the rate of additional surgery following anastomosis problem is about 50%. NIRS has shown promising results for the monitoring of free flap, however lack of adaptation to its environment (ambient light) and users (body mass index (BMI), skin tone, alcohol and smoking habits or physical activity level) hinders the practical use of this technique. To overcome those limitations, a self-calibrated approach is introduced. Tested with is chaemia and cold water experiments on healthy subjects of different skin tones, its ability to personalize its calibration is demonstrated. Furthermore, using a vascular phantom, it is also able to detect pulses, differentiate venous and arterial coloured-like fluids with distinct clusters and detect significant changes in simulated partial venous occlusion. Placed in the trained classifier, partial occlusion data showed similar results between predicted and true classification. Further analysis from partial occlusion data showed that distinct clusters for 75% and 100% occlusion emerged.

  • Conference paper
    Ravi D, Wong C, Lo B, Yang GZet al., 2016,

    Deep learning for human activity recognition: A resource efficient implementation on low-power devices

    , 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks, Publisher: IEEE, Pages: 71-76, ISSN: 2376-8894

    Human Activity Recognition provides valuable contextual information for wellbeing, healthcare, and sport applications. Over the past decades, many machine learning approaches have been proposed to identify activities from inertial sensor data for specific applications. Most methods, however, are designed for offline processing rather than processing on the sensor node. In this paper, a human activity recognition technique based on a deep learning methodology is designed to enable accurate and real-time classification for low-power wearable devices. To obtain invariance against changes in sensor orientation, sensor placement, and in sensor acquisition rates, we design a feature generation process that is applied to the spectral domain of the inertial data. Specifically, the proposed method uses sums of temporal convolutions of the transformed input. Accuracy of the proposed approach is evaluated against the current state-of-the-art methods using both laboratory and real world activity datasets. A systematic analysis of the feature generation parameters and a comparison of activity recognition computation times on mobile devices and sensor nodes are also presented.

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