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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Conference paperWang L, Thiemjarus S, Lo B, et al., 2008,
Toward A Mixed-Signal Reconfigurable ASIC for Real-Time Activity Recognition, 5th International Summer School and Symposium on Medical Devices and Biosensors, Publisher: IEEE, Pages: 113-+
Conference paperThiemjarus S, Pansiot J, Mcllwraith D, et al., 2008,
An integrated inferencing framework for context sensing, 5th Int Conference on Information Technol and Applications in Biomedicine in Conjunction with the 2nd Int Symposium and Summer School on Biomedical and Health Engineering, Publisher: IEEE, Pages: 1-5
Conference paperAtallah L, Lo B, Yang G-Z, et al., 2008,
Wirelessly Accessible Sensor Populations (WASP) for Elderly Care Monitoring, 2nd International Conference on Pervasive Computing Technologies for Healthcare, Publisher: IEEE, Pages: 3-+
Conference paperMcIlwraith DG, Pansiot J, Thiemjarus S, et al., 2008,
Probabilistic Decision Level Fusion for Real-Time Correlation of Ambient and Wearable Sensors, 5th International Summer School and Symposium on Medical Devices and Biosensors, Publisher: IEEE, Pages: 256-259
Journal articleLo B, Scarzanella MV, Stoyanov D, et al., 2008,
In minimally invasive surgery, dense 3D surface reconstruction is important for surgical navigation and integrating pre- and intra-operative data. Despite recent developments in 3D tissue deformation techniques, their general applicability is limited by specific constraints and underlying assumptions. The need for accurate and robust tissue deformation recovery has motivated research into fusing multiple visual cues for depth recovery. In this paper, a Markov Random Field (MRF) based Bayesian belief propagation framework has been proposed for the fusion of different depth cues. By using the underlying MRF structure to ensure spatial continuity in an image, the proposed method offers the possibility of inferring surface depth by fusing the posterior node probabilities in a node's Markov blanket together with the monocular and stereo depth maps. Detailed phantom validation and in vivo results are provided to demonstrate the accuracy, robustness, and practical value of the technique.
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