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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    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.

    Andreu-Perez J, Leff DR, Shetty K, Darzi A, Yang G-Zet al., 2016,

    Disparity in Frontal Lobe Connectivity on a Complex Bimanual Motor Task Aids in Classification of Operator Skill Level.

    , Brain Connect, Vol: 6, Pages: 375-388

    Objective metrics of technical performance (e.g., dexterity, time, and path length) are insufficient to fully characterize operator skill level, which may be encoded deep within neural function. Unlike reports that capture plasticity across days or weeks, this articles studies long-term plasticity in functional connectivity that occurs over years of professional task practice. Optical neuroimaging data are acquired from professional surgeons of varying experience on a complex bimanual coordination task with the aim of investigating learning-related disparity in frontal lobe functional connectivity that arises as a consequence of motor skill level. The results suggest that prefrontal and premotor seed connectivity is more critical during naïve versus expert performance. Given learning-related differences in connectivity, a least-squares support vector machine with a radial basis function kernel is employed to evaluate skill level using connectivity data. The results demonstrate discrimination of operator skill level with accuracy ≥0.82 and Multiclass Matthew's Correlation Coefficient ≥0.70. Furthermore, these indices are improved when local (i.e., within-region) rather than inter-regional (i.e., between-region) frontal connectivity is considered (p = 0.002). The results suggest that it is possible to classify operator skill level with good accuracy from functional connectivity data, upon which objective assessment and neurofeedback may be used to improve operator performance during technical skill training.

    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
    Berthelot M, Chen C-M, Yang G-Z, Lo Bet al., 2016,

    Wireless Wearable Self-calibrated Sensor for Perfusion Assessment of Myocutaneous Tissue

    , 13th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 171-176, ISSN: 2376-8886
    Chen S, Lach J, Lo B, Yang G-Zet al., 2016,

    Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review

    , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 20, Pages: 1521-1537, ISSN: 2168-2194

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