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
    Zhang D, Wu Z, Chen J, Gao A, Chen X, Li P, Wang Z, Yang G, Lo B, Yang G-Zet al., 2020,

    Automatic Microsurgical Skill Assessment Based on Cross-Domain Transfer Learning

    , IEEE Robotics and Automation Letters, Vol: 5, Pages: 4148-4155
  • Journal article
    Barbot A, Power M, Seichepine F, Yang G-Zet al.,

    Liquid seal for compact micro-piston actuation at capillary tip

    , Science Advances, ISSN: 2375-2548

    Actuators at the tip of a sub-millimetric catheter could facilitatein vivointer-ventional procedures at cellular scales by enabling tissue biopsy, manipulationor supporting active micro-optics. However the dominance of frictional forcesat this scale makes classical mechanism problematic. In this paper, we reportthe design of a micro-scale piston, with a maximum dimension of 150μm,fabricated with two-photon lithography onto the tip of 140μm diameter cap-illaries. An oil drop method is used to create a seal between the piston andthe cylinder which prevents any leakage below 185 mbar pressure differencewhile providing lubricated friction between moving parts. This piston gener-ates forces that increase linearly with pressure up to 130μN without breakingthe liquid seal. The practical value of the design is demonstrated with its inte-gration with a micro-gripper that can grasp, move and release 50μm micro-spheres. Such a mechanism opens the way to micron-size catheter actuation.

  • Journal article
    Keshavarz M, Kassanos P, Tan B, Venkatakrishnan Ket al., 2020,

    Metal-oxide surface-enhanced Raman biosensor template towards point-of-care EGFR detection and cancer diagnostics

    , NANOSCALE HORIZONS, Vol: 5, Pages: 294-307, ISSN: 2055-6756
  • Journal article
    Zhang Y, Guo Y, Yang P, Chen W, Lo Bet al., 2020,

    Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network

    , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 24, Pages: 465-474, ISSN: 2168-2194
  • Journal article
    Zhang Y, Zhang Y, Lo B, Xu Wet al., 2020,

    Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection

    , Expert Systems, Vol: 37, Pages: 1-13, ISSN: 0266-4720

    Classification of electrocardiogram (ECG) signals is obligatory for the automatic diagnosis of cardiovascular disease. With the recent advancement of low‐cost wearable ECG device, it becomes more feasible to utilize ECG for cardiac arrhythmia classification in daily life. In this paper, we propose a lightweight approach to classify five types of cardiac arrhythmia, namely, normal beat (N), atrial premature contraction (A), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). The combined method of frequency analysis and Shannon entropy is applied to extract appropriate statistical features. Information gain criterion is employed to select features that the results show that 10 highly effective features can obtain performance measures comparable to those obtained by using the complete features. The selected features are then fed to the input of Random Forest, K‐Nearest Neighbour, and J48 for classification. To evaluate classification performance, tenfold cross validation is used to verify the effectiveness of our method. Experimental results show that Random Forest classifier demonstrates significant performance with the highest sensitivity of 98.1%, the specificity of 99.5%, the precision of 98.1%, and the accuracy of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification.

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