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 article
    Sun Y, Lo FPW, Lo B, 2019,

    EEG-based user identification system using 1D-convolutional long short-term memory neural networks

    , Expert Systems with Applications, Vol: 125, Pages: 259-267, ISSN: 0957-4174

    Electroencephalographic (EEG) signals have been widely used in medical applications, yet the use of EEG signals as user identification systems for healthcare and Internet of Things (IoT) systems has only gained interests in the last few years. The advantages of EEG-based user identification systems lie in its dynamic property and uniqueness among different individuals. However, it is for this reason that manually designed features are not always adapted to the needs. Therefore, a novel approach based on 1D Convolutional Long Short-term Memory Neural Network (1D-Convolutional LSTM) for EEG-based user identification system is proposed in this paper. The performance of the proposed approach was validated with a public database consists of EEG data of 109 subjects. The experimental results showed that the proposed network has a very high averaged accuracy of 99.58%, when using only 16 channels of EEG signals, which outperforms the state-of-the-art EEG-based user identification methods. The combined use of CNNs and LSTMs in the proposed 1D-Convolutional LSTM can greatly improve the accuracy of user identification systems by utilizing the spatiotemporal features of the EEG signals with LSTM, and lowering cost of the systems by reducing the number of EEG electrodes used in the systems.

  • Journal article
    Berthelot M, Henry FP, Hunter J, Leff D, Wood S, Jallali N, Dex E, Ladislava L, Lo B, Yang GZet al., 2019,

    Pervasive wearable device for free tissue transfer monitoring based on advanced data analysis: clinical study report

    , Journal of Biomedical Optics, Vol: 24, Pages: 067001-1-067001-8, ISSN: 1083-3668

    Free tissue transfer (FTT) surgery for breast reconstruction following mastectomy has become a routineoperation with high success rates. Although failure is low, it can have a devastating impact on patient recovery,prognosis and psychological well-being. Continuous and objective monitoring of tissue oxygen saturation (StO2) hasshown to reduce failure rates through rapid detection time of postoperative vascular complications. We have developeda pervasive wearable wireless device that employs near infrared spectroscopy (NIRS) to continuously monitor FTTviaStO2measurement. Previously tested on different models, this paper introduces the results of a clinical study. Thegoal of the study is to demonstrate the developed device can reliably detectStO2variations in a clinical setting: 14patients were recruited. Advanced data analysis were performed on theStO2variations, the relativeStO2gradientchange, and, the classification of theStO2within different clusters of blood occlusion level (from 0% to 100% at 25%step) based on previous studies made on a vascular phantom and animals. The outcomes of the clinical study concurwith previous experimental results and the expected biological responses. This suggests the device is able to correctlydetect perfusion changes and provide real-time assessment on the viability of the FTT in a clinical setting.

  • Journal article
    McCrory M, Sun M, Sazonov E, Frost G, Anderson A, Jia W, Jobarteh ML, Maitland K, Steiner-Asiedu M, Ghosh T, Higgins JA, Baranowski T, Lo Bet al., 2019,

    Methodology for objective, passive, image- and sensor-based assessment of dietary intake, meal-timing, and food-related activity in Ghana and Kenya (P13-028-19).

    , Current Developments in Nutrition, Vol: 3, Pages: 1247-1247, ISSN: 2475-2991

    Objectives: Herein we describe a new system we have developed for assessment of dietary intake, meal timing, and food-related activities, adapted for use in low- and middle-income countries. Methods: System components include one or more wearable cameras (the Automatic Ingestion Monitor-2 (AIM), an eyeglasses-mounted wearable chewing sensor and micro-camera; ear-worn camera; the eButton, a camera attached to clothes; and eHat, a camera attached to a visor worn by the mother when feeding infants and toddlers), and custom software for evaluation of dietary intake from food-based images and sensor-detected food intake. General protocol: The primary caregiver of the family uses one or more wearable cameras during all waking hours. The cameras aim directly in front of the participant and capture images every few seconds, thereby providing multiple images of all food-related activities throughout the day. The camera may be temporarily removed for short periods to preserve privacy, such as during bathing and personal care. For analysis, images and sensor signals are processed by the study team in custom software. The images are time-stamped, arranged in chronological order, and linked with sensor-detected eating occasions. The software also incorporates food composition databases of choice such as the West African Foods Database, a Kenyan Foods Database, and the USDA Food Composition Database, allowing for image-based dietary assessment by trained nutritionists. Images can be linked with nutritional analysis and tagged with an activity label (e.g., food shopping, child feeding, cooking, eating). Assessment of food-related activities such as food-shopping, food gathering from gardens, cooking, and feeding of other family members by the primary caregiver can help provide context for dietary intake and additional information to increase accuracy of dietary assessment and analysis of eating behavior. Examples of the latter include assessment of specific ingredients in prepared

  • Journal article
    Sun Y, Lo B, 2019,

    An artificial neural network framework for gait based biometrics

    , IEEE Journal of Biomedical and Health Informatics, Vol: 23, Pages: 987-998, ISSN: 2168-2194

    OAPA As the popularity of wearable and implantable Body Sensor Network (BSN) devices increases, there is a growing concern regarding the data security of such power-constrained miniaturized medical devices. With limited computational power, BSN devices are often not able to provide strong security mechanisms to protect sensitive personal and health information, such as one's physiological data. Consequently, many new methods of securing Wireless Body Area Networks (WBANs) have been proposed recently. One effective solution is the Biometric Cryptosystem (BCS) approach. BCS exploits physiological and behavioral biometric traits, including face, iris, fingerprints, Electrocardiogram (ECG), and Photoplethysmography (PPG). In this paper, we propose a new BCS approach for securing wireless communications for wearable and implantable healthcare devices using gait signal energy variations and an Artificial Neural Network (ANN) framework. By simultaneously extracting similar features from BSN sensors using our approach, binary keys can be generated on demand without user intervention. Through an extensive analysis on our BCS approach using a gait dataset, the results have shown that the binary keys generated using our approach have high entropy for all subjects. The keys can pass both NIST and Dieharder statistical tests with high efficiency. The experimental results also show the robustness of the proposed approach in terms of the similarity of intra-class keys and the discriminability of the inter-class keys.

  • Conference paper
    Adshead J, Oldfield F, Hadaschik B, Everaerts W, Mestre-Fusco A, Newbery M, Elson D, Grootendorst M, Vyas K, Fumado L, Harke Net al., 2019,

    A pelvic phantom and porcine model study to evaluate the usability and technical feasibility of a tethered laparoscopic gamma probe for radioguided surgery in prostate cancer

    , Annual Meeting of the Society-of-Nuclear-Medicine-and-Molecular-Imaging (SNMMI), Publisher: SOC NUCLEAR MEDICINE INC, ISSN: 0161-5505

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