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
    Qiu J, Lo FP-W, Lo B, 2019,

    Assessing individual dietary intake in food sharing scenarios with a 360 camera and deep learning

    , IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, ISSN: 2376-8886

    A novel vision-based approach for estimating individual dietary intake in food sharing scenarios is proposed in this paper, which incorporates food detection, face recognition and hand tracking techniques. The method is validated using panoramic videos which capture subjects' eating episodes. The results demonstrate that the proposed approach is able to reliably estimate food intake of each individual as well as the food eating sequence. To identify the food items ingested by the subject, a transfer learning approach is designed. 4, 200 food images with segmentation masks, among which 1,500 are newly annotated, are used to fine-tune the deep neural network for the targeted food intake application. In addition, a method for associating detected hands with subjects is developed and the outcomes of face recognition are refined to enable the quantification of individual dietary intake in communal eating settings.

  • Conference paper
    Sun Y, Lo FP-W, Lo B, 2019,

    A deep learning approach on gender and age recognition using a single inertial sensor

    , IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, ISSN: 2376-8886

    Extracting human attributes, such as gender and age, from biometrics have received much attention in recent years. Gender and age recognition can provide crucial information for applications such as security, healthcare, and gaming. In this paper, a novel deep learning approach on gender and age recognition using a single inertial sensors is proposed. The proposed approach is tested using the largest available inertial sensor-based gait database with data collected from more than 700 subjects. To demonstrate the robustness and effectiveness of the proposed approach, 10 trials of inter-subject Monte-Carlo cross validation were conducted, and the results show that the proposed approach can achieve an averaged accuracy of 86.6%±2.4% for distinguishing two age groups: teen and adult, and recognizing gender with averaged accuracies of 88.6%±2.5% and 73.9%±2.8% for adults and teens respectively.

  • Conference paper
    Chen S, Kang L, Lu Y, Wang N, Lu Y, Lo B, Yang G-Zet al., 2019,

    Discriminative information added by wearable sensors for early screening - a case study on diabetic peripheral neuropathy

    , IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 1-4, ISSN: 2376-8886

    Wearable inertial sensors have demonstrated their potential to screen for various neuropathies and neurological disorders. Most such research has been based on classification algorithms that differentiate the control group from the pathological group, using biomarkers extracted from wearable data as predictors. However, such methods often lack quantitative evaluation of how much information provided by the wearable biomarkers contributes to the overall prediction. Despite promising results from internal cross validation, their utility in clinical practice remains unclear. In this paper, we highlight in a case study - early screening for diabetic peripheral neuropathy (DPN) - evaluation methods for quantifying the contribution of wearable inertial sensors. Using a quick-to-deploy wearable sensor system, we collected 106 in-hospital diabetic patients' gait data and developed logistic regression models to predict the risk of a diabetic patient having DPN. Adopting various metrics, we evaluated the discriminative information added by gait biomarkers and how much it improved screening. The results show that the proposed wearable system added useful information significantly to the existing clinical standards, and boosted the C-index significantly from 0.75 to 0.84, surpassing the current survey-based screening methods used in clinics.

  • Journal article
    Sunny AI, Rahman M, Koutsoupidou M, Cano-Garcia H, Thanou M, Rafique W, Lipscombe O, Kassanos P, Triantis I, Kallos E, Kosmas Pet al., 2019,

    Feasibility Experiments to Detect Skin Hydration Using a Bio-Impedance Sensor

    , 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Pages: 6032-6035, ISSN: 1557-170X
  • 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.

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