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 articleHooshmand S, Kargozar S, Ghorbani A, et al., 2020,
Biomedical Waste Management by Using Nanophotocatalysts: The Need for New Options, MATERIALS, Vol: 13
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- Citations: 12
Journal articleGao A, Liu N, Shen M, et al., 2020,
Laser-profiled continuum robot with integrated tension sensing for simultaneous shape and tip force estimation, Soft Robotics, Vol: 7, Pages: 421-443, ISSN: 2169-5172
The development of miniaturized continuum robots has a wide range of applications in minimally invasive endoluminal interventions. To navigate inside tortuous lumens without impinging on the vessel wall and causing tissue damage or the risk of perforation, it is necessary to have simultaneous shape sensing of the continuum robot and its tip contact force sensing with the surrounding environment. Miniaturization and size constraint of the device have precluded the use of conventional sensing hardware and embodiment schemes. In this study, we propose the use of optical fibers for both actuation and tension/shape/force sensing. It uses a model-based method with structural compensation, allowing direct measurement of the cable tension near the base of the manipulator without increasing the dimensions. It further structurally filters out disturbances from the flexible shaft. In addition, a model is built by considering segment differences, cable interactions/cross talks, and external forces. The proposed model-based method can simultaneously estimate the shape of the manipulator and external force applied onto the robot tip. Detailed modeling and validation results demonstrate the accuracy and reliability of the proposed method for the miniaturized continuum robot for endoluminal intervention.
Journal articleKeshavarz M, Wales DJ, Seichepine F, et al., 2020,
Induced neural stem cell differentiation on a drawn fiber scaffold-toward peripheral nerve regeneration, Biomedical Materials, Vol: 15, ISSN: 1748-6041
To achieve regeneration of long sections of damaged nerves, restoration methods such as direct suturing or autologous grafting can be inefficient. Solutions involving biohybrid implants, where neural stem cells are grown in vitro on an active support before implantation, have attracted attention. Using such an approach, combined with recent advancements in microfabrication technology, the chemical and physical environment of cells can be tailored in order to control their behaviors. Herein, a neural stem cell polycarbonate fiber scaffold, fabricated by 3D printing and thermal drawing, is presented. The combined effect of surface microstructure and chemical functionalization using poly-ʟ-ornithine (PLO) and double-walled carbon nanotubes (DWCNTs) on the biocompatibility of the scaffold, induced differentiation of the neural stem cells (NSCs) and channeling of the neural cells was investigated. Upon treatment of the fiber scaffold with a suspension of DWCNTs in PLO (0.039 gL-1) and without recombinants a high degree of differentiation of NSCs into neuronal cells was confirmed by using nestin, galactocerebroside (GalC) and doublecortin (Dcx) immunoassays. These findings illuminate the potential use of this biohybrid approach for the realization of future nerve regenerative implants.
Journal articleKassanos P, Berthelot M, Kim JA, et al., 2020,
Smart sensing for surgery from tethered devices to wearables and implantables, IEEE Systems Man and Cybernetics Magazine, Vol: 6, Pages: 39-48, ISSN: 2333-942X
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,
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.
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