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 articleKassanos P, Berthelot M, Kim JA, et al., 2020,
Recent developments in wearable electronics have fueled research into new materials, sensors, and microelectronic technologies for the realization of devices that have increased functionality and performance. This is further enhanced by advances in fabr ication methods and printing techniques, stimulating research on implantables and the advancement of existing medical devices. This article provides an overview of new designs, embodiments, fabrication methods, instrumentation, and informatics as well as the challenges in developing and deploying such devices and clinical applications that can benefit from them. The need for and use of these technologies across the perioperative surgical-care pathway are highlighted, along with a vision for the future and how these tools can be adopted by potential end users and health-care systems.
Journal articleZhang D, Wu Z, Chen J, et al., 2020,
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 paperKassanos P, Seichepine F, Kassanos I, et al., 2020,
Conference paperVarghese RJ, Nguyen A, Burdet E, et al., 2020,
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 articleBarbot A, Power M, Seichepine F, et al., 2020,
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.
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