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, ISSN: 2377-3766

    The assessment of microsurgical skills for Robot-Assisted Microsurgery (RAMS) still relies primarily on subjective observations and expert opinions. A general and automated evaluation method is desirable. Deep neural networks can be used for skill assessment through raw kinematic data, which has the advantages of being objective and efficient. However, one of the major issues of deep learning for the analysis of surgical skills is that it requires a large database to train the desired model, and the training process can be time-consuming. This letter presents a transfer learning scheme for training a model with limited RAMS datasets for microsurgical skill assessment. An in-house Microsurgical Robot Research Platform Database (MRRPD) is built with data collected from a microsurgical robot research platform (MRRP). It is used to verify the proposed cross-domain transfer learning for RAMS skill level assessment. The model is fine-tuned after training with the data obtained from the MRRP. Moreover, microsurgical tool tracking is developed to provide visual feedback while task-specific metrics and the other general evaluation metrics are provided to the operator as a reference. The method proposed has shown to offer the potential to guide the operator to achieve a higher level of skills for microsurgical operation.

  • Conference paper
    Kassanos P, Seichepine F, Kassanos I, Yang G-Zet al., 2020,

    Development and Characterization of a PCB-Based Microfluidic YChannel*

    , 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, Publisher: IEEE
  • Conference paper
    Varghese RJ, Nguyen A, Burdet E, Yang G-Z, Lo BPLet al., 2020,

    Nonlinearity compensation in a multi-DoF shoulder sensing exosuit for real-time teleoperation

    , 3rd IEEE International Conference on Soft Robotics (RoboSoft), Publisher: IEEE, Pages: 668-675

    The compliant nature of soft wearable robots makes them ideal for complex multiple degrees of freedom (DoF) joints, but also introduce additional structural nonlinearities. Intuitive control of these wearable robots requires robust sensing to overcome the inherent nonlinearities. This paper presents a joint kinematics estimator for a bio-inspired multi- DoF shoulder exosuit capable of compensating the encountered nonlinearities. To overcome the nonlinearities and hysteresis inherent to the soft and compliant nature of the suit, we developed a deep learning-based method to map the sensor data to the joint space. The experimental results show that the new learning-based framework outperforms recent state-of-the-art methods by a large margin while achieving 12ms inference time using only a GPU-based edge-computing device. The effectiveness of our combined exosuit and learning framework is demonstrated through real-time teleoperation with a simulated NAO humanoid robot.

  • Journal article
    Barbot A, Power M, Seichepine F, Yang G-Zet al., 2020,

    Liquid seal for compact micro-piston actuation at capillary tip

    , Science Advances, Vol: 6, 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
    Kim JA, Wales D, Thompson A, Yang G-Zet al., 2020,

    Fiber-optic SERS probes fabricated using two-photon polymerization for rapid detection of bacteria

    , Advanced Optical Materials, Vol: 8, Pages: 1-12, ISSN: 2195-1071

    This study presents a novel fiber-optic surface-enhanced Raman spectroscopy (SERS) probe (SERS-on-a-tip) fabricated using a simple, two-step protocol based on off-the-shelf components and materials, with a high degree of controllability and repeatability. Two-photon polymerization and subsequent metallization was adopted to fabricate a range of SERS arrays on both planar substrates and end-facets of optical fibers. For the SERS-on-a-tip probes, a limit of detection of 10-7 M (Rhodamine 6G) and analytical enhancement factors of up to 1300 were obtained by optimizing the design, geometry and alignment of the SERS arrays on the tip of the optical fiber. Furthermore, strong repeatability and consistency were achieved for the fabricated SERS arrays, demonstrating that the technique may be suitable for large-scale fabrication procedures in the future. Finally, rapid SERS detection of live Escherichia coli cells was demonstrated using integration times in the milliseconds to seconds range. This result indicates strong potential for in vivo diagnostic use, particularly for detection of infections. Moreover, to the best of our knowledge, this represents the first report of detection of live, unlabeled bacteria using a fiber-optic SERS probe.

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