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
    Dudina A, Seichepine F, Chen Y, Stettler A, Hierlemann A, Frey Uet al., 2019,

    Monolithic CMOS sensor platform featuring an array of 9 ' 216 carbon-nanotube-sensor elements and low-noise, wide-bandwidth and wide-dynamic-range readout circuitry

    , SENSORS AND ACTUATORS B-CHEMICAL, Vol: 279, Pages: 255-266, ISSN: 0925-4005
  • Conference paper
    Dagnino G, Liu J, Abdelaziz M, Chi W, Riga C, Yang Get al., 2019,

    Haptic feedback and dynamic active constraints for robot-assisted endovascular catheterization

    , 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), Publisher: IEEE

    Robotic and computer assistance can bring significant benefits to endovascular procedures in terms of precision and stability, reduced radiation doses, improved comfort and access to difficult and tortuous anatomy.However,the design of current commercially available platforms tends to alter the natural bedside manipulation skills of the operator, so thatthe manually acquired experience and dexterityare not well utilized. Furthermore, most of these systems lackofhaptic feedback, preventing their acceptance and limiting the clinical usability.In this paper a new robotic platform for endovascular catheterization, the CathBot, is presented.It is an ergonomic master-slave system with navigation system and integrated vision-based haptic feedback, designed to maintain the natural bedside skills of the vascular surgeon. Unlike previous work reported in literature, dynamic motion tracking of both the vessel walls the catheter tip is incorporated to create dynamic activeconstraints. The system was evaluated through a combined quantitative and qualitative user study simulating catheterization tasks on a phantom. Forces exerted on the phantom were measured. The results showed a 70% decrease in mean force and 61% decrease in maximum force when force feedback is provided. This research provides the first integration of vision-based dynamic active constraints within an ergonomic robotic catheter manipulator. The technological advances presented here, demonstratesthat vision-based haptic feedback can improve the effectiveness, precision, and safety of robot-assisted endovascular procedures.

  • Conference paper
    Singh RK, Varghese RJ, Liu J, Zhang Z, Lo Bet al., 2019,

    A multi-sensor fusion approach for intention detection

    , 4th International Conference on NeuroRehabilitation (ICNR), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 454-458, ISSN: 2195-3562

    For assistive devices to seamlessly and promptly assist users with activities of daily living (ADL), it is important to understand the user’s intention. Current assistive systems are mostly driven by unimodal sensory input which hinders their accuracy and responses. In this paper, we propose a context-aware sensor fusion framework to detect intention for assistive robotic devices which fuses information from a wearable video camera and wearable inertial measurement unit (IMU) sensors. A Naive Bayes classifier is used to predict the intent to move from IMU data and the object classification results from the video data. The proposed approach can achieve an accuracy of 85.2% in detecting movement intention.

  • Conference paper
    Kassanos P, Seichepine F, Wales D, Yang G-Zet al., 2019,

    Towards a Flexible/Stretchable Multiparametric Sensing Device for Surgical and Wearable Applications

    , IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, ISSN: 2163-4025
  • Conference paper
    Rosa BG, Anastasova-Ivanova S, Yang GZ, 2019,

    A Low-powered and Wearable Device for Monitoring Sleep through Electrical, Chemical and Motion signals recorded over the head

    , IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, ISSN: 2163-4025

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