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

    Disparity in frontal lobe connectivity on a complex bimanual motor task aids in classification of operator skill level.

    , Brain Connectivity, Vol: 6, Pages: 375-388, ISSN: 2158-0022

    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.

  • Journal article
    Lo BPL, Ip H, Yang G-Z, 2016,

    Transforming health care: body sensor networks, wearables, and the Internet of Things

    , IEEE Pulse, Vol: 7, Pages: 4-8, ISSN: 2154-2287

    This paper talks about body sensor networks, wearables, and the Internet of Things.

  • Conference paper
    Jarchi D, Peters A, Lo B, Kalliolia E, Di Giulio I, Limousin P, Day BL, Yang GZet al., 2016,

    Assessment of the e-AR sensor for gait analysis of Parkinson;s Disease patients

    , 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 1-6, ISSN: 2376-8886

    This paper analyses gait patterns of patients with Parkinson;s Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency as a gait parameter derived from the estimated heel-contacts is calculated and validated using the CODA motion-capture system. Intersession variability of step-frequency for each patient and the overall variability across patients demonstrate a good agreement between estimations from the e-AR and CODA systems.

  • Journal article
    Bergmann JHM, Goodier H, Spulber I, Anastasova S, Georgiou P, McGregor AHet al., 2015,

    The "Wear and Measure" Approach: Linking Joint Stability Measurements from a Smart Clothing System to Optical Tracking

    , Journal of Sensors, Vol: 2015, ISSN: 1687-7268

    Joint stability is essential for maintaining normal everyday function. However, assessment of stability often still relies on subjective or obtrusive methods. An unobtrusive approach would be to have our clothes assess our joint stability. Methods. A new application consisting of an attachable clothing sensing system (ACSS), constructed from a flexible carbon black and polyurethane composite film, was tested against an optical tracking system to assess if the ACSS placed across the knee could provide stability results that correlate with the optical tracking outcomes. Stability was challenged by reducing the base of support and by removing vision generating different experimental conditions. Results. Bland and Altman plots indicated a general proportional error between the measurement systems within each stability condition. However, across all conditions a Spearman correlation coefficient of 0.81 () was found between the displacement values and ACSS, showing a good association between stability measurements. Electromyography (EMG) also indicated that joint stability was challenged between the different conditions. The ACSS was experienced by users as comfortable and hardly noticeable. Conclusions. This study indicates that smart clothing can measure important physiological parameters in an unobtrusive manner. This “wear and measure” approach might change how we gather relevant clinical data in the future.

  • Journal article
    Andreu-Perez J, Solnais C, Sriskandarajah K, 2015,

    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

    Recent advances in the reliability of the eye-tracking methodology as well as the increasing availability of affordable non-intrusive technology have opened the door to new research opportunities in a variety of areas and applications. This has raised increasing interest within disciplines such as medicine, business and education for analysing human perceptual and psychological processes based on eye-tracking data. However, most of the currently available software requires programming skills and focuses on the analysis of a limited set of eye-movement measures (e.g., saccades and fixations), thus excluding other measures of interest to the classification of a determined state or condition. This paper describes ‘EALab’, a MATLAB toolbox aimed at easing the extraction, multivariate analysis and classification stages of eye-activity data collected from commercial and independent eye trackers. The processing implemented in this toolbox enables to evaluate variables extracted from a wide range of measures including saccades, fixations, blinks, pupil diameter and glissades. Using EALab does not require any programming and the analysis can be performed through a user-friendly graphical user interface (GUI) consisting of three processing modules: 1) eye-activity measure extraction interface, 2) variable selection and analysis interface, and 3) classification interface.

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