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
    Lo FP-W, Sun Y, Qiu J, Lo Bet al., 2018,

    Food volume estimation based on deep learning view synthesis from a single depth map

    , Nutrients, Vol: 10, Pages: 1-20, ISSN: 2072-6643

    An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume is required. For volume estimation, previous research studies mostly focused on using model-based or stereo-based approaches which rely on manual intervention or require users to capture multiple frames from different viewing angles which can be tedious. In this paper, a view synthesis approach based on deep learning is proposed to reconstruct 3D point clouds of food items and estimate the volume from a single depth image. A distinct neural network is designed to use a depth image from one viewing angle to predict another depth image captured from the corresponding opposite viewing angle. The whole 3D point cloud map is then reconstructed by fusing the initial data points with the synthesized points of the object items through the proposed point cloud completion and Iterative Closest Point (ICP) algorithms. Furthermore, a database with depth images of food object items captured from different viewing angles is constructed with image rendering and used to validate the proposed neural network. The methodology is then evaluated by comparing the volume estimated by the synthesized 3D point cloud with the ground truth volume of the object items

  • Conference paper
    Kassanos P, Anastasova S, Yang G-Z, 2018,

    A Low-Cost Amperometric Glucose Sensor Based on PCB Technology

    , 17th IEEE SENSORS Conference, Publisher: IEEE, Pages: 1031-1034, ISSN: 1930-0395
  • Conference paper
    Bernstein A, Varghese RJ, Liu J, Zhang Z, Lo Bet al., 2018,

    An assistive ankle joint exoskeleton for gait impairment

    , 4th International Conference on NeuroRehabilitation (ICNR), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 658-662, ISSN: 2195-3562

    Motor rehabilitation and assistance post-stroke are becoming a major concern for healthcare services with an increasingly aging population. Wearable robots can be a technological solution to support gait rehabilitation and to provide assistance to enable users to carry out activities of daily living independently. To address the need for long-term assistance for stroke survivors suffering from drop foot, this paper proposes a low-cost, assistive ankle joint exoskeleton for gait assistance. The proposed exoskeleton is designed to provide ankle foot support thus enabling normal walking gait. Baseline gait reading was recorded from two force sensors attached to a custom-built shoe insole of the exoskeleton. From our experiments, the average maximum force during heel-strike (63.95 N) and toe-off (54.84 N) were found, in addition to the average period of a gait cycle (1.45 s). The timing and force data were used to control the actuation of tendons of the exoskeleton to prevent the foot from preemptively hitting the ground during swing phase.

  • Journal article
    Keshavarz M, Tan B, Venkatakrishnan K, 2018,

    Label-Free SERS Quantum Semiconductor Probe for Molecular-Level and in Vitro Cellular Detection: A Noble-Metal-Free Methodology.

    , ACS Appl Mater Interfaces

    Accurate in vitro molecular-level analysis is an essential step prior to in vivo and clinical application for early diagnosis and cancer treatment. Among the diagnostic techniques, surface-enhanced Raman scattering (SERS) biosensing has shown growing potential due to its noninvasive and real-time characterization of the biomolecules. However, the application of SERS biosensing is mostly limited to the plasmonic noble metals, in the form of either nanoparticles or tips and substrates (fixed probe), on which surface plasmon resonance (SPR) is the prominent enhancement principle. The semiconductor quantum particles have been explored in several optoelectronics applications, but have never been reported to be exploited as a means of surface-enhanced Raman scattering (SERS) for molecular-level and intracellular sensing. Here, we report on the new generation of noble-metal-free SERS probe; Si@SiO2 quantum probe (Si@SiO2 Q-probe) whose affinity to functional groups not only imitates a self-driven labeling attribution that enables charge transfer (CT) as an augmented enhancement principle but also its mobile nature in miniaturized scale facilitates endocytosis for in situ live cell biosensing. Moreover, a significant enhancement factor of 106 of rhodamine 6G (R6G) and 107 of glutathione (GSH) at ∼5 × 10-12 pM concentration has been achieved that is comparable to inherently plasmonic noble metals. Our results showed a capability of the Si@SiO2 Q-probe to unveil the "biochemical fingerprint" of substantial components of mammalian and cancerous cervical cells, which leads to diagnosis of cervical cancer. These unique attributions of the Si@SiO2 Q-probe can provide better insight into cell mutation and malignancy.

  • Journal article
    Tudor A, Delaney C, Zhang H, Thompson AJ, Curto VF, Yang GZ, Higgins MJ, Diamond D, Florea Let al., 2018,

    Fabrication of soft, stimulus-responsive structures with sub-micron resolution via two-photon polymerization of poly(ionic liquid)s

    , Materials Today, Vol: 21, Pages: 807-816, ISSN: 1369-7021

    Soft, stimulus-responsive 3D structures created from crosslinked poly(ionic liquid)s (PILs) have been fabricated at unprecedented sub-micron resolution by direct laser writing (DLW). These structures absorb considerable quantities of solvent (e.g., water, alcohol, and acetone) to produce PIL hydrogels that exhibit stimulus-responsive behavior. Due to their flexibility and soft, responsive nature, these structures are much more akin to biological systems than the conventional, highly crosslinked, rigid structures typically produced using 2-photon polymerization (2-PP). These PIL gels expand/contract due to solvent uptake/release, and, by exploiting inherited properties of the ionic liquid monomer (ILM), thermo-responsive gels that exhibit reversible area change (30 ± 3%, n = 40) when the temperature is raised from 20 °C to 70 °C can be created. The effect is very rapid, with the response indistinguishable from the microcontroller heating rate of 7.4 °C s−1. The presence of an endoskeleton-like framework within these structures influences movement arising from expansion/contraction and assists the retention of structural integrity during actuation cycling.

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