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
    Zhang Y, Zhang Y, Lo B, Xu Wet al., 2020,

    Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection

    , Expert Systems, Vol: 37, Pages: 1-13, ISSN: 0266-4720

    Classification of electrocardiogram (ECG) signals is obligatory for the automatic diagnosis of cardiovascular disease. With the recent advancement of low‐cost wearable ECG device, it becomes more feasible to utilize ECG for cardiac arrhythmia classification in daily life. In this paper, we propose a lightweight approach to classify five types of cardiac arrhythmia, namely, normal beat (N), atrial premature contraction (A), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). The combined method of frequency analysis and Shannon entropy is applied to extract appropriate statistical features. Information gain criterion is employed to select features that the results show that 10 highly effective features can obtain performance measures comparable to those obtained by using the complete features. The selected features are then fed to the input of Random Forest, K‐Nearest Neighbour, and J48 for classification. To evaluate classification performance, tenfold cross validation is used to verify the effectiveness of our method. Experimental results show that Random Forest classifier demonstrates significant performance with the highest sensitivity of 98.1%, the specificity of 99.5%, the precision of 98.1%, and the accuracy of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification.

  • Journal article
    Lo FP-W, Sun Y, Qiu J, Lo BPLet al., 2020,

    Point2Volume: A vision-based dietary assessment approach using view synthesis

    , IEEE Transactions on Industrial Informatics, Vol: 16, Pages: 577-586, ISSN: 1551-3203

    Dietary assessment is an important tool for nutritional epidemiology studies. To assess the dietary intake, the common approach is to carry out 24-h dietary recall (24HR), a structured interview conducted by experienced dietitians. Due to the unconscious biases in such self-reporting methods, many research works have proposed the use of vision-based approaches to provide accurate and objective assessments. In this article, a novel vision-based method based on real-time three-dimensional (3-D) reconstruction and deep learning view synthesis is proposed to enable accurate portion size estimation of food items consumed. A point completion neural network is developed to complete partial point cloud of food items based on a single depth image or video captured from any convenient viewing position. Once 3-D models of food items are reconstructed, the food volume can be estimated through meshing. Compared to previous methods, our method has addressed several major challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity, and it outperforms previous approaches in accurate portion size estimation.

  • Journal article
    Zheng Y, Ghovanloo M, Lo BPL, Atef M, Jiang Het al., 2019,

    Introduction to the special issue on wearable and flexible integrated sensors for screening, diagnostics, and treatment

    , IEEE Transactions on Biomedical Circuits and Systems, Vol: 13, Pages: 1300-1303, ISSN: 1932-4545

    The papers in this special issue present a selection of high quality research papers on wearable and flexible integrated sensors for screening, diagnostics, and treatment. Emerging flexible and wearable physical sensing devices create huge potential for many vital healthcare and biomedical applications including artificial electronic skins, physiological monitoring and assessment systems, therapeutic and drug delivery platforms, etc. Monitoring of vital physiological parameters in hospital and/or home environments has been of tremendous interests to healthcare practitioners for a long time. Robust and reliable sensors with excellent flexibility and stretchability are essential in the development of pervasive health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous discomfort and invasiveness.

  • Journal article
    Rosa BMG, Anastasova-Ivanova S, Yang GZ, 2019,

    NFC-Powered Flexible Chest Patch for Fast Assessment of Cardiac, Hemodynamic, and Endocrine Parameters

  • Journal article
    Lo B, Zhang Y, Inan OT, Ellul Jet al., 2019,

    Guest editorial: special issue on pervasive sensing and machine learning for mental health

    , IEEE Journal of Biomedical and Health Informatics, Vol: 23, Pages: 2245-2246, ISSN: 2168-2194

    The seven papers included in this special section focus on machine learning applications for the mental health industry. Mental health is one of the major global health issues affecting substantially more people than other noncommunicable diseases. Much research has been focused on developing novel technologies for tackling this global health challenge, including the development of advanced analytical techniques based on extensive datasets and multimodal acquisition for early detection and treatment of mental illnesses. The papers in this issue are dedicated to cover the related topics on technological advancements for mental health care and diagnosis with a focus on pervasive sensing and machine learning.

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