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 articleZhao T, Deng L, Wang W, et al., 2018,
In this paper, we present a Bayes’ theorem-based high-speed algorithm, to measure the binary transmission matrix of a multimode fiber using a digital micromirror device, in a reference-less multimode fiber imaging system. Based on conditional probability, we define a preset threshold to locate those digital-micromirror-device pixels that can be switched ‘ON’ to form a focused spot at the output. This leads to a binary transmission matrix consisting of ‘0’ and ‘1’ elements. High-enhancement-factor light focusing and raster-scanning at the distal end of the fiber are demonstrated experimentally. The key advantage of our algorithm is its capability for fast calibration of a MMF to form a tightly focused spot. In our experiment, for 5000 input-output pairs, we only need 0.26 s to calibrate one row of the transmission matrix to achieve a focused spot with an enhancement factor of 28. This is more than 10 times faster than the prVBEM algorithm. The proposed Bayes’ theorem-based binary algorithm can be applied not only in multimode optical fiber focusing but also to other disordered media. Particularly, it will be valuable in fast multimode fiber calibration for endoscopic imaging.
Journal articleSaso S, Tziraki M, Clancy NT, et al., 2018,
Aim: Uterine transplantation (UTx) is proposed for treatment of uterine factor infertility. Our aim was to assess whether Endoscopic Laser Speckle Contrast Analysis (eLASCA) could evaluate pelvic blood flow at anastomotic sites required for sheep and rabbit UTx. Results/methodology: eLASCA detected blood flow in rabbit UTx #7 and #9. In sheep UTx #2, #3 and #5, the results allowed us to conclude that blood flow was present in the uterine graft following transplantation; and post-UTx, the animal had heart and respiratory rates, and oxygen saturation compatible with a normal hemodynamic status. Conclusion: These preliminary results establish the potential of Laser Speckle Contrast Analysis as noncontact and real-time tool for observation of spatially-resolved blood flow from which other parameters can be derived.
Conference paperTeachasrisaksakul K, Wu L, Yang G-Z, et al., 2018,
Dyscalculia is a learning difficulty hindering fundamental arithmetical competence. Children with dyscalculia often have difficulties in engaging in lessons taught with traditional teaching methods. In contrast, an educational game is an attractive alternative. Recent educational studies have shown that gestures could have a positive impact in learning. With the recent development of low cost wearable sensors, a gesture based educational game could be used as a tool to improve the learning outcomes particularly for children with dyscalculia. In this paper, two generic gesture recognition methods are proposed for developing an interactive educational game with wearable inertial sensors. The first method is a multilayered perceptron classifier based on the accelerometer and gyroscope readings to recognize hand gestures. As gyroscope is more power demanding and not all low-cost wearable device has a gyroscope, we have simplified the method using a nearest centroid classifier for classifying hand gestures with only the accelerometer readings. The method has been integrated into open-source educational games. Experimental results based on 5 subjects have demonstrated the accuracy of inertial sensor based hand gesture recognitions. The results have shown that both methods can recognize 15 different hand gestures with the accuracy over 93%.
Journal articleConstantinescu MA, Lee S-L, Ernst S, et al., 2018,
Radiofrequency catheter ablation is one of the commonly available therapeutic methods for patients suffering from cardiac arrhythmias. The prerequisite of successful ablation is sufficient energy delivery at the target site. However, cardiac and respiratory motion, coupled with endocardial irregularities, can cause catheter drift and dispersion of the radiofrequency energy, thus prolonging procedure time, damaging adjacent tissue, and leading to electrical reconnection of temporarily ablated regions. Therefore, positional accuracy and stability of the catheter tip during energy delivery is of great importance for the outcome of the procedure. This paper presents an analytical scheme for assessing catheter tip stability, whereby a sequence of catheter tip motion recorded at sparse locations on the endocardium is decomposed. The spatial sliding component along the endocardial wall is extracted from the recording and maximal slippage and its associated probability are computed at each mapping point. Finally, a global map is generated, allowing the assessment of potential areas that are compromised by tip slippage. The proposed framework was applied to 40 retrospective studies of congenital heart disease patients and further validated on phantom data and simulations. The results show a good correlation with other intraoperative factors, such as catheter tip contact force amplitude and orientation, and with clinically documented anatomical areas of high catheter tip instability.
Conference paperSun Y, Lo B, 2018,
With increasing popularity of wearable and implantable technologies for medical applications, there is a growing concern on the security and data protection of the on-body Internet-ofThings (IoT) devices. As a solution, cryptographic system is often adopted to encrypt the data, and Random Number Generator (RNG) is of vital importance to such system. This paper proposes a new random number generation method for securing on-body IoT devices based on temporal signal variations of the outputs of the Inertial Measurement Units (IMU) worn by the users while walking. As most new wearable and implantable devices have built-in IMUs and walking gait signals can be extracted from these body sensors, this method can be applied and integrated into the cryptographic systems of these new devices. To generate the random numbers, this method divides IMU signals into gait cycles and generates bits by comparing energy differences between the sensor signals in a gait cycle and the averaged IMU signals in multiple gait cycles. The generated bits are then re-indexed in descending order by the absolute values of the associated energy differences to further randomise the data and generate high-entropy random numbers. Two datasets were used in the studies to generate random numbers, where were rigorously tested and passed four well-known randomness test suites, namely NIST-STS, ENT, Dieharder, and RaBiGeTe.
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