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.

Search or filter publications

Filter by type:

Filter by publication type

Filter by year:



  • Showing results for:
  • Reset all filters

Search results

  • 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
    Zhang K, Chen C-M, Anastasova S, Gil B, Lo B, Assender Het al., 2019,

    Roll-to-roll processable OTFT-based amplifier and application for pH sensing

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

    The prospect of roll-to-roll (R2R) processable Organic Thin Film Transistors (OTFTs) and circuits has attracted attention due to their mechanical flexibility and low cost of manufacture. This work will present a flexible electronics application for pH sensing with flexible and wearable signal processing circuits. A transimpedance amplifier was designed and fabricated on a polyethylene naphthalate (PEN) substrate prototype sheet that consists of 54 transistors. Different types and current ratios of current mirrors were initially created and then a suitable simple 1:3 current mirror (200nA) was selected to present the best performance of the proposed OTFT based transimpedance amplifier (TIA). Finally, this transimpedance amplifier was connected to a customized needle-based pH sensor that was induced as microfluidic collector for potential disease diagnosis and healthcare monitoring.

  • Conference paper
    Lo FP-W, Sun Y, Qiu J, Lo Bet al., 2019,

    A novel vision-based approach for dietary assessment using deep learning view synthesis

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

    Dietary assessment system has proven as an effective tool to evaluate the eating behavior of patients suffering from diabetes and obesity. To assess the dietary intake, the traditional method is to carry out a 24-hour dietary recall (24HR), a structured interview aimed at capturing information on food items and portion size consumed by participants. However, unconscious biases are developed easily due to individual's subjective perception in this self-reporting technique which may lead to inaccuracy. Thus, this paper proposed a novel vision-based approach for estimating the volume of food items based on deep learning view synthesis and depth sensing techniques. In this paper, a point completion network is applied to perform 3D reconstruction of food items using a single depth image captured from any convenient viewing angle. Compared to previous approaches, the proposed method has addressed several key challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity. Experiments have been carried out to examine this approach and showed the feasibility of the algorithm in accurate estimation of food volume.

  • 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.

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://www.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=758&limit=5&page=11&respub-action=search.html Current Millis: 1675756722605 Current Time: Tue Feb 07 07:58:42 GMT 2023