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
    Andreu Perez J, C Y Poon C, Merrifield R, Guang-Zhong Yet al., 2015,

    Big Data for Health

    , IEEE Journal of Biomedical and Health Informatics, Vol: 19, Pages: 1193-1208, ISSN: 2168-2194

    This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

  • Journal article
    Kirby GSJ, Guyver P, Strickland L, Alvand A, Yang G-Z, Hargrove C, Lo BPL, Rees JLet al., 2015,

    Assessing arthroscopic skills using wireless elbow-worn motion sensors

    , Journal of Bone and Joint Surgery-American Volume, Vol: 97A, Pages: 1119-1127, ISSN: 1535-1386

    Background: Assessment of surgical skill is a critical component of surgical training. Approaches to assessment remain predominantly subjective, although more objective measures such as Global Rating Scales are in use. This study aimed to validate the use of elbow-worn, wireless, miniaturized motion sensors to assess the technical skill of trainees performing arthroscopic procedures in a simulated environment.Methods: Thirty participants were divided into three groups on the basis of their surgical experience: novices (n = 15), intermediates (n = 10), and experts (n = 5). All participants performed three standardized tasks on an arthroscopic virtual reality simulator while wearing wireless wrist and elbow motion sensors. Video output was recorded and a validated Global Rating Scale was used to assess performance; dexterity metrics were recorded from the simulator. Finally, live motion data were recorded via Bluetooth from the wireless wrist and elbow motion sensors and custom algorithms produced an arthroscopic performance score.Results: Construct validity was demonstrated for all tasks, with Global Rating Scale scores and virtual reality output metrics showing significant differences between novices, intermediates, and experts (p < 0.001). The correlation of the virtual reality path length to the number of hand movements calculated from the wireless sensors was very high (p < 0.001). A comparison of the arthroscopic performance score levels with virtual reality output metrics also showed highly significant differences (p < 0.01). Comparisons of the arthroscopic performance score levels with the Global Rating Scale scores showed strong and highly significant correlations (p < 0.001) for both sensor locations, but those of the elbow-worn sensors were stronger and more significant (p < 0.001) than those of the wrist-worn sensors.Conclusions: A new wireless assessment of surgical performance system for objective assessment of surgical skills has proven v

  • Journal article
    Gowers SAN, Curto VF, Seneci CA, Wang C, Anastasova S, Vadgama P, Yang G-Z, Boutelle MGet al., 2015,

    A 3D printed microfluidic device with integrated biosensors for online analysis of subcutaneous human microdialysate

    , Analytical Chemistry, Vol: 87, Pages: 7763-7770, ISSN: 1086-4377

    This work presents the design, fabrication, and characterization of a robust 3D printed microfluidic analysis system that integrates with FDA-approved clinical microdialysis probes for continuous monitoring of human tissue metabolite levels. The microfluidic device incorporates removable needle type integrated biosensors for glucose and lactate, which are optimized for high tissue concentrations, housed in novel 3D printed electrode holders. A soft compressible 3D printed elastomer at the base of the holder ensures a good seal with the microfluidic chip. Optimization of the channel size significantly improves the response time of the sensor. As a proof-of-concept study, our microfluidic device was coupled to lab-built wireless potentiostats and used to monitor real-time subcutaneous glucose and lactate levels in cyclists undergoing a training regime.

  • Conference paper
    Gaglione A, Chen S, Lo B, Yang GZet al., 2015,

    A Low-Power Opportunistic Communication Protocol for Wearable Applications

    , 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: To appear

    Recent trends in wearable applications demandflexible architectures being able to monitor people while theymove in free-living environments. Current solutions use eitherstore-download-offline processing or simple communicationschemes with real-time streaming of sensor data. This limits theapplicability of wearable applications to controlled environments(e.g, clinics, homes, or laboratories), because they need tomaintain connectivity with the base station throughout themonitoring process. In this paper, we present the design andimplementation of an opportunistic communication frameworkthat simplifies the general use of wearable devices in free-livingenvironments. It relies on a low-power data collection protocolthat allows the end user to opportunistically, yet seamlesslymanage the transmission of sensor data. We validate thefeasibility of the framework by demonstrating its use forswimming, where the normal wireless communication isconstantly interfered by the environment.

  • Patent
    Lo BPL, Chen CM, Yang GZ, 2015,

    A Multiple PPG sensing platform

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