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
    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

  • Journal article
    Teachasrisaksakul K, Zhang Z-Q, Yang G-Z, Lo Bet al., 2015,

    Imitation of Dynamic Walking With BSN for Humanoid Robot

    , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 19, Pages: 794-802, ISSN: 2168-2194
  • Conference paper
    Ravi D, Lo B, Yang G,

    Real-time food intake classification and energy expenditure estimation on a mobile device

    , BSN 2015, Publisher: IEEE

    Assessment of food intake has a wide range ofapplications in public health and life-style related chronic dis-ease management. In this paper, we propose a real-time foodrecognition platform combined with daily activity and energyexpenditure estimation. In the proposed method, food recognitionis based on hierarchical classification using multiple visual cues,supported by efficient software implementation suitable for real-time mobile device execution. A Fischer Vector representationtogether with a set of linear classifiers are used to categorizefood intake. Daily energy expenditure estimation is achieved byusing the built-in inertial motion sensors of the mobile device.The performance of the vision-based food recognition algorithmis compared to the current state-of-the-art, showing improvedaccuracy and high computational efficiency suitable for real-time feedback. Detailed user studies have also been performed todemonstrate the practical value of the software environment.

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