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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    Sun Y, Wong C, Yang GZ, Lo Bet al., 2017,

    Secure key generation using gait features for Body Sensor Networks

    , IEEE EMBS Annual International Body Sensor Networks Conference, Publisher: IEEE, Pages: 206-210

    With increasing popularity of wearable and Body Sensor Networks technologies, there is a growing concern on the security and data protection of such low-power pervasive devices. With very limited computational power, BSN sensors often cannot provide the necessary data protection to collect and process sensitive personal information. Since conventional network security schemes are too computationally demanding for miniaturized BSN sensors, new methods of securing BSNs have proposed, in which Biometric Cryptosystem (BCS) appears to be an effective solution. With regards to BCS security solutions, physiological traits, such as an individual's face, iris, fingerprint, electrocardiogram (ECG), and photoplethysmogram (PPG) have been widely exploited. However, behavioural traits such as gait are rarely studied. In this paper, a novel lightweight symmetric key generation scheme based on the timing information of gait is proposed. By extracting similar timing information from gait acceleration signals simultaneously from body worn sensors, symmetric keys can be generated on all the sensor nodes at the same time. Based on the characteristics of generated keys and BSNs, a fuzzy commitment based key distribution scheme is also developed to distribute the keys amongst the sensor nodes.

    Ravi D, Wong C, Lo B, Yang Get al., 2016,

    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: 2168-2208

    The increasing popularity of wearable devices inrecent years means that a diverse range of physiological and functionaldata can now be captured continuously for applicationsin sports, wellbeing, and healthcare. This wealth of informationrequires efficient methods of classification and analysis wheredeep learning is a promising technique for large-scale data analytics.Whilst deep learning has been successful in implementationsthat utilize high performance computing platforms, its use onlow-power wearable devices is limited by resource constraints.In this paper, we propose a deep learning methodology, whichcombines features learnt from inertial sensor data together withcomplementary information from a set of shallow features toenable accurate and real-time activity classification. The design ofthis combined method aims to overcome some of the limitationspresent in a typical deep learning framework where on-nodecomputation is required. To optimize the proposed method forreal-time on-node computation, spectral domain pre-processingis used before the data is passed onto the deep learning framework.The classification accuracy of our proposed deep learningapproach is evaluated against state-of-the-art methods using bothlaboratory and real world activity datasets. Our results show thevalidity of the approach on different human activity datasets,outperforming other methods, including the two methods usedwithin our combined pipeline. We also demonstrate that thecomputation times for the proposed method are consistent withthe constraints of real-time on-node processing on smartphonesand a wearable sensor platform.

    Wijayasingha L, Lo BPL, 2016,

    A Wearable Sensing Framework for Improving Personal and Oral Hygiene for People with Developmental Disabilities

    , IEEE Wireless Health 2016, Publisher: IEEE

    People with developmental disabilities often facedifficulties in coping with daily activities and many requireconstant support. One of the major health issues for peoplewith developmental disabilities is personal hygiene. Many lackthe ability, poor memory or lack of attention to carry outnormal daily activities like brushing teeth and washing hands.Poor personal hygiene may result in increased susceptibility toinfection and other health issues. To enable independent livingand improve the quality of care for people with developmentalabilities, this paper proposes a new wearable sensingframework to monitoring personal hygiene. Based on asmartwatch, this framework is designed as a pervasivemonitoring and learning tool to provide detailed evaluation andfeedback to the user on hand washing and tooth brushing. Apreliminary study was conducted to assess the performance ofthe approach, and the results showed the reliability androbustness of the framework in quantifying and assessing handwashing and tooth brushing activities.

    Zhang Y, Berthelot M, Lo BPL, 2016,

    Wireless Wearable Photoplethysmography Sensors for ContinuousBlood Pressure Monitoring

    , IEEE Wireless Health 2016, Publisher: IEEE

    Blood Pressure (BP) is a crucial vital sign takeninto consideration for the general assessment of patient’s condition:patients with hypertension or hypotension are advisedto record their BP routinely. Particularly, hypertension isemphasized by stress, diabetic neuropathy and coronary heartdiseases and could lead to stroke. Therefore, routine andlong-term monitoring can enable early detection of symptomsand prevent life-threatening events. The gold standard methodfor measuring BP is the use of a stethoscope and sphygmomanometerto detect systolic and diastolic pressures. However,only discrete measurements are taken. To enable pervasiveand continuous monitoring of BP, recent methods have beenproposed: pulse arrival time (PAT) or PAT difference (PATD)between different body parts are based on the combinationof electrocardiogram (ECG) and photoplethysmography (PPG)sensors. Nevertheless, this technique could be quite obtrusiveas in addition to at least two contacts/electrodes to measurethe differential voltage across the left arm/leg/chest and theright arm/leg/chest, ECG measurements are easily corruptedby motion artefacts. Although such devices are small, wearableand relatively convenient to use, most devices are not designedfor continuous BP measurements. This paper introduces anovel PPG-based pervasive sensing platform for continuousmeasurements of BP. Based on the principle of using PAT toestimate BP, two PPG sensors are used to measure the PATDbetween the earlobe and the wrist to measure BP. The device iscompared with a gold standard PPG sensor and validation ofthe concept is conducted with a preliminary study involving 9healthy subjects. Results show that the mean BP and PATD arecorrelated with a 0.3 factor. This preliminary study shows thefeasibility of continuous monitoring of BP using a pair of PPGplaced on the ear lobe and wrist with PATD measurements ispossible.

    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.

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