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
    Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang G-Zet al., 2017,

    Deep Learning for Health Informatics

    , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 21, Pages: 4-21, ISSN: 2168-2194
  • JOURNAL ARTICLE
    Ravi D, Wong C, Lo B, Yang G-Zet al., 2017,

    A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices

    , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 21, Pages: 56-64, ISSN: 2168-2194
  • CONFERENCE PAPER
    Sun Y, Wong C, Yang G-Z, Lo Bet al., 2017,

    Secure Key Generation Using Gait Features for Body Sensor Networks

    , 14th Annual IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 206-210, ISSN: 2376-8886
  • JOURNAL ARTICLE
    Andreu Perez J, Cao F, Hagras H, Yang Get al., 2016,

    A self-adaptive online brain machine interface of a humanoid robot through a general type-2 fuzzy inference system

    , IEEE Transactions on Fuzzy Systems, ISSN: 1941-0034

    This paper presents a self-adaptive general type-2 fuzzy inference system (GT2 FIS) for online motor imagery (MI) decoding to build a brain-machine interface (BMI) and navigate a bi-pedal humanoid robot in a real experiment, using EEG brain recordings only. GT2 FISs are applied to BMI for the first time in this study. We also account for several constraints commonly associated with BMI in real practice: 1) maximum number ofelectroencephalography (EEG) channels is limited and fixed, 2) no possibility of performing repeated user training sessions, and 3) desirable use of unsupervised and low complexity features extraction methods. The novel learning method presented in this paper consists of a self-adaptive GT2 FIS that can both incrementally update its parameters and evolve (a.k.a. self-adapt) its structure via creation, fusion and scaling of the fuzzy system rules in an online BMI experiment with a real robot. The structureidentification is based on an online GT2 Gath-Geva algorithm where every MI decoding class can be represented by multiple fuzzy rules (models). The effectiveness of the proposed method is demonstrated in a detailed BMI experiment where 15 untrained users were able to accurately interface with a humanoid robot, in a single thirty-minute experiment, using signals from six EEG electrodes only.

  • JOURNAL ARTICLE
    Andreu-Perez J, Solnais C, Sriskandarajah K, 2016,

    EALab (Eye Activity Lab): a MATLAB Toolbox for Variable Extraction, Multivariate Analysis and Classification of Eye-Movement Data

    , NEUROINFORMATICS, Vol: 14, Pages: 51-67, ISSN: 1539-2791

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