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 paper
    Guo Y, Zhang Y, Mursalin M, Xu W, Lo BPLet al., 2018,

    Automated epileptic seizure detection by analyzing wearable EEG signals using extended correlation-based feature selection

    , IEEE BSN 2018, Publisher: IEEE, Pages: 66-69

    Electroencephalogram (EEG) that measures the electrical activity of the brain has been widely employed for diagnosing epilepsy which is one kind of brain abnormalities. With the advancement of low-cost wearable brain-computer interface devices, it is possible to monitor EEG for epileptic seizure detection in daily use. However, it is still challenging to develop seizure classification algorithms with a considerable higher accuracy and lower complexity. In this study, we propose a lightweight method which can reduce the number of features for a multiclass classification to identify three different seizure statuses (i.e., Healthy, Interictal and Epileptic seizure) through EEG signals with a wearable EEG sensors using Extended Correlation-Based Feature Selection (ECFS). More specifically, there are three steps in our proposed approach. Firstly, the EEG signals were segmented into five frequency bands and secondly, we extract the features while the unnecessary feature space was eliminated by developing the ECFS method. Finally, the features were fed into five different classification algorithms, including Random Forest, Support Vector Machine, Logistic Model Trees, RBF Network and Multilayer Perceptron. Experimental results have shown that Logistic Model Trees provides the highest accuracy of 97.6% comparing to other classifiers.

  • Conference paper
    Yang GZ, Rosa BMG, 2018,

    A wearable and battery-less device for assessing skin hydration level under direct sunlight exposure with ultraviolet index calculation

    , 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 201-204

    Skin cancer is a medical condition that is becoming more common in many countries as a result of excessive exposure of individuals to sunlight. The ultraviolet range of the electromagnetic radiation is responsible for 90% of the cases involving the development of melanomas. Additional factors like the skin tone and texture can increase the risk of radiation exposure when the water content retained by the skin starts to drop dramatically. In this paper we present a small, batteryless wearable device that combines the computation of sunlight exposure with the measurement of the impedance of the skin and temperature, at any time of the day and independently of the location of the person wearing the sensor. Results have shown a good performance in tracking the ultraviolet index and the variation of impedance for different levels of skin hydration.

  • Conference paper
    Berthelot M, Yang G-Z, Lo B, 2018,

    Tomographic probe for perfusion analysis in deep layer tissue

    , 15th International Conference on Biomedical and Health Informatics (BHI) and Wearable and Implantable Body Sensor Networks (BSN) of the IEEE-Engineering-in-Medicine-and-Biology-Society, Publisher: IEEE, Pages: 86-89, ISSN: 2376-8886

    Continuous buried soft tissue free flap postoperative monitoring is crucial to detect flap failure and enable early intervention. In this case, clinical assessment is challenging as the flap is buried and only implantable or hand held devices can be used for regular monitoring. These devices have limitations in their price, usability and specificity. Near-infrared spectroscopy (NIRS) has shown promising results for superficial free flap postoperative monitoring, but it has not been considered for buried free flap, mainly due to the limited penetration depth of conventional approaches. A wearable wireless tomographic probe has been developed for continuous monitoring of tissue perfusion at different depths. Using the NIRS method, blood flow can be continuously measured at different tissue depths. This device has been designed following conclusions of extensive computerised simulations and it has been validated using a vascular phantom.

  • Conference paper
    Gu X, Deligianni F, Lo B, Chen W, Yang GZet al., 2018,

    Markerless gait analysis based on a single RGB camera

    , Pages: 42-45

    Gait analysis is an important tool for monitoring and preventing injuries as well as to quantify functional decline in neurological diseases and elderly people. In most cases, it is more meaningful to monitor patients in natural living environments with low-end equipment such as cameras and wearable sensors. However, inertial sensors cannot provide enough details on angular dynamics. This paper presents a method that uses a single RGB camera to track the 2D joint coordinates with state-of-the-art vision algorithms. Reconstruction of the 3D trajectories uses sparse representation of an active shape model. Subsequently, we extract gait features and validate our results in comparison with a state-of-the-art commercial multi-camera tracking system. Our results are comparable to those from the current literature based on depth cameras and optical markers to extract gait characteristics.

  • Book chapter
    Thompson AJ, Yang G-Z, 2018,

    Tethered and Implantable Optical Sensors

    , Implantable Sensors and Systems, Editors: Yang, Publisher: Springer, Pages: 439-505, ISBN: 978-3-319-69747-5

    Optical imaging and sensing modalities have been used in medical diagnosis for many years. An obvious example is endoscopy, which allows remote wide-field imaging of internal tissues using optical fibers and/or miniature charge-coupled device (CCD) cameras. While techniques such as endoscopy provide useful tools for clinicians, they do not typically allow a complete diagnosis to be made. Instead, physical biopsies may be required to confirm or refute the presence of disease. Furthermore, endoscopic procedures are both invasive and time-consuming. As such, much research is currently directed toward the development of devices that can provide a complete in vivo diagnosis without the requirement for a physical biopsy. Ideally, such devices should also be minimally or non-invasive, and they should provide immediate identification of disease at the point of care. Additionally, there is significant interest in the development of implantable diagnostic devices that can be left within patients’ bodies for extended periods of time (for several days or longer). Such systems could be used for automated disease diagnosis, and example applications include the detection of post-surgical infections as well as monitoring of the health status of patients undergoing chemotherapy. This chapter focuses on the development of optical instruments that can provide in situ diagnosis at the point of care, with an emphasis on progress towards miniature devices that may function as implants in the future.

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