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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  • Conference paper
    Guo Y, Sun M, Lo FPW, Lo Bet al., 2019,

    Visual guidance and automatic control for robotic personalized stent graft manufacturing

    , 2019 International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 8740-8746

    Personalized stent graft is designed to treat Abdominal Aortic Aneurysms (AAA). Due to the individual difference in arterial structures, stent graft has to be custom made for each AAA patient. Robotic platforms for autonomous personalized stent graft manufacturing have been proposed in recently which rely upon stereo vision systems for coordinating multiple robots for fabricating customized stent grafts. This paper proposes a novel hybrid vision system for real-time visual-sevoing for personalized stent-graft manufacturing. To coordinate the robotic arms, this system is based on projecting a dynamic stereo microscope coordinate system onto a static wide angle view stereo webcam coordinate system. The multiple stereo camera configuration enables accurate localization of the needle in 3D during the sewing process. The scale-invariant feature transform (SIFT) method and color filtering are implemented for stereo matching and feature identifications for object localization. To maintain the clear view of the sewing process, a visual-servoing system is developed for guiding the stereo microscopes for tracking the needle movements. The deep deterministic policy gradient (DDPG) reinforcement learning algorithm is developed for real-time intelligent robotic control. Experimental results have shown that the robotic arm can learn to reach the desired targets autonomously.

  • Journal article
    Modi HN, Singh H, Fiorentino F, Orihuela-Espina F, Athanasiou T, Yang G-Z, Darzi A, Leff DRet al., 2019,

    Association of residents' neural signatures with stress resilience during surgery

    , JAMA Surgery, Vol: 154, ISSN: 2168-6254

    Importance: Intraoperative stressors may compound cognitive load, prompting performance decline and threatening patient safety. However, not all surgeons cope equally well with stress, and the disparity between performance stability and decline under high cognitive demand may be characterized by differences in activation within brain areas associated with attention and concentration such as the prefrontal cortex (PFC). Objective: To compare PFC activation between surgeons demonstrating stable performance under temporal stress with those exhibiting stress-related performance decline. Design, Setting, and Participants: Cohort study conducted from July 2015 to September 2016 at the Imperial College Healthcare National Health Service Trust, England. One hundred two surgical residents (postgraduate year 1 and greater) were invited to participate, of which 33 agreed to partake. Exposures: Participants performed a laparoscopic suturing task under 2 conditions: self-paced (SP; without time-per-knot restrictions), and time pressure (TP; 2-minute per knot time restriction). Main Outcomes and Measures: A composite deterioration score was computed based on between-condition differences in task performance metrics (task progression score [arbitrary units], error score [millimeters], leak volume [milliliters], and knot tensile strength [newtons]). Based on the composite score, quartiles were computed reflecting performance stability (quartile 1 [Q1]) and decline (quartile 4 [Q4]). Changes in PFC oxygenated hemoglobin concentration (HbO2) measured at 24 different locations using functional near-infrared spectroscopy were compared between Q1 and Q4. Secondary outcomes included subjective workload (Surgical Task Load Index) and heart rate. Results: Of the 33 participants, the median age was 33 years, the range was 29 to 56 years, and 27 were men (82%). The Q1 residents demonstrated task-induced increases in HbO2 across the bilateral ventrolateral PFC (VLPFC) and right dorsolateral P

  • Conference paper
    Rosa BG, Anastasova-Ivanova S, Lo B, Yang GZet al., 2019,

    Towards a fully automatic food intake recognition system using acoustic, image capturing and glucose measurements

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

    Food intake is a major healthcare issue in developed countries that has become an economic and social burden across all sectors of society. Bad food intake habits lead to increased risk for development of obesity in children, young people and adults, with the latter more prone to suffer from health diseases such as diabetes, shortening the life expectancy. Environmental, cultural and behavioural factors have been appointed to be responsible for altering the balance between energy intake and expenditure, resulting in excess body weight. Methods to counteract the food intake problem are vast and include self-reported food questionnaires, body-worn sensors that record the sound, pressure or movements in the mouth and GI tract or image-based approaches that recognize the different types of food being ingested. In this paper we present an ear-worn device to track food intake habits by recording the acoustic signal produced by the chewing movements as well as the glucose level amperiometrically. Combined with a small camera on a future version of the device, we hope to deliver a complete system to control dietary habits with caloric intake estimation during satiation and deficit during satiety periods, which can be adapted to the physiology of each user.

  • Conference paper
    Chen S, Kang L, Lu Y, Wang N, Lu Y, Lo B, Yang G-Zet al., 2019,

    Discriminative information added by wearable sensors for early screening - a case study on diabetic peripheral neuropathy

    , IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 1-4, ISSN: 2376-8886

    Wearable inertial sensors have demonstrated their potential to screen for various neuropathies and neurological disorders. Most such research has been based on classification algorithms that differentiate the control group from the pathological group, using biomarkers extracted from wearable data as predictors. However, such methods often lack quantitative evaluation of how much information provided by the wearable biomarkers contributes to the overall prediction. Despite promising results from internal cross validation, their utility in clinical practice remains unclear. In this paper, we highlight in a case study - early screening for diabetic peripheral neuropathy (DPN) - evaluation methods for quantifying the contribution of wearable inertial sensors. Using a quick-to-deploy wearable sensor system, we collected 106 in-hospital diabetic patients' gait data and developed logistic regression models to predict the risk of a diabetic patient having DPN. Adopting various metrics, we evaluated the discriminative information added by gait biomarkers and how much it improved screening. The results show that the proposed wearable system added useful information significantly to the existing clinical standards, and boosted the C-index significantly from 0.75 to 0.84, surpassing the current survey-based screening methods used in clinics.

  • Conference paper
    Sun Y, Lo FP-W, Lo B, 2019,

    A deep learning approach on gender and age recognition using a single inertial sensor

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

    Extracting human attributes, such as gender and age, from biometrics have received much attention in recent years. Gender and age recognition can provide crucial information for applications such as security, healthcare, and gaming. In this paper, a novel deep learning approach on gender and age recognition using a single inertial sensors is proposed. The proposed approach is tested using the largest available inertial sensor-based gait database with data collected from more than 700 subjects. To demonstrate the robustness and effectiveness of the proposed approach, 10 trials of inter-subject Monte-Carlo cross validation were conducted, and the results show that the proposed approach can achieve an averaged accuracy of 86.6%±2.4% for distinguishing two age groups: teen and adult, and recognizing gender with averaged accuracies of 88.6%±2.5% and 73.9%±2.8% for adults and teens respectively.

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