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.

Search or filter publications

Filter by type:

Filter by publication type

Filter by year:



  • Showing results for:
  • Reset all filters

Search results

  • Journal article
    Wales DJ, Cao Q, Kastner K, Karjalainen E, Newton GN, Sans Vet al., 2018,

    3D-Printable Photochromic Molecular Materials for Reversible Information Storage

    , ADVANCED MATERIALS, Vol: 30, ISSN: 0935-9648
  • Conference paper
    Sun Y, Lo B, 2018,

    Random number generation using inertial measurement unit signals for on-body IoT devices

    , Living in the Internet of Things: Cybersecurity of the IoT - A PETRAS, IoTUK and IET Event, Publisher: IET

    With increasing popularity of wearable and implantable technologies for medical applications, there is a growing concern on the security and data protection of the on-body Internet-ofThings (IoT) devices. As a solution, cryptographic system is often adopted to encrypt the data, and Random Number Generator (RNG) is of vital importance to such system. This paper proposes a new random number generation method for securing on-body IoT devices based on temporal signal variations of the outputs of the Inertial Measurement Units (IMU) worn by the users while walking. As most new wearable and implantable devices have built-in IMUs and walking gait signals can be extracted from these body sensors, this method can be applied and integrated into the cryptographic systems of these new devices. To generate the random numbers, this method divides IMU signals into gait cycles and generates bits by comparing energy differences between the sensor signals in a gait cycle and the averaged IMU signals in multiple gait cycles. The generated bits are then re-indexed in descending order by the absolute values of the associated energy differences to further randomise the data and generate high-entropy random numbers. Two datasets were used in the studies to generate random numbers, where were rigorously tested and passed four well-known randomness test suites, namely NIST-STS, ENT, Dieharder, and RaBiGeTe.

  • Journal article
    Berthelot M, Lo B, Yang G-Z, Leff Det al., 2018,

    Pilot study: Free flap monitoring using a new tissue oxygen saturation (StO2) device

    , European Journal of Surgical Oncology, Vol: 44, Pages: 900-900, ISSN: 0748-7983
  • Journal article
    Kwasnicki RM, Cross GW, Geoghegan L, Zhang Z, Reilly P, Darzi A, Yang GZ, Emery Ret al., 2018,

    A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study


    BackgroundThe prevalence of self-reported shoulder pain in the UK has been estimated at 16%. This has been linked with significant sleep disturbance. It is possible that this relationship is bidirectional, with both symptoms capable of causing the other. Within the field of sleep monitoring, there is a requirement for a mobile and unobtrusive device capable of monitoring sleep posture and quality. This study investigates the feasibility of a wearable sleep system (WSS) in accurately detecting sleeping posture and physical activity.MethodsSixteen healthy subjects were recruited and fitted with three wearable inertial sensors on the trunk and forearms. Ten participants were entered into a ‘Posture’ protocol; assuming a series of common sleeping postures in a simulated bedroom. Five participants completed an ‘Activity’ protocol, in which a triphasic simulated sleep was performed including awake, sleep and REM phases. A combined sleep posture and activity protocol was then conducted as a ‘Proof of Concept’ model. Data were used to train a posture detection algorithm, and added to activity to predict sleep phase. Classification accuracy of the WSS was measured during the simulations.ResultsThe WSS was found to have an overall accuracy of 99.5% in detection of four major postures, and 92.5% in the detection of eight minor postures. Prediction of sleep phase using activity measurements was accurate in 97.3% of the simulations. The ability of the system to accurately detect both posture and activity enabled the design of a conceptual layout for a user-friendly tablet application.ConclusionsThe study presents a pervasive wearable sensor platform, which can accurately detect both sleeping posture and activity in non-specialised environments. The extent and accuracy of sleep metrics available advances the current state-of-the-art technology. This has potential diagnostic implications in musculoskeletal pathology and with the addition of aler

  • Journal article
    Thompson AJ, Power M, Yang G-Z, 2018,

    A micro-scale fiber-optic force sensor fabricated using direct laser writing and calibrated using machine learning

    , Optics Express, Vol: 26, Pages: 14186-14200, ISSN: 1094-4087

    Fiber-optic sensors have numerous existing and emerging applications spanning areas from industrial process monitoring to medical diagnosis. Two of the most common fiber sensors are based on the fabrication of Bragg gratings or Fabry-Perot etalons. While these techniques offer a large array of sensing targets, their utility can be limited by the difficulties involved in fabricating forward viewing probes (Bragg gratings) and in obtaining sufficient signal-to-noise ratios (Fabry-Perot systems). In this article we present a microscale fiber-optic force sensor produced using direct laser writing (DLW). The fabrication entails a single-step process that can be undertaken in a reliable and repeatable manner using a commercial DLW system. The sensor is made of a series of thin plates (i.e. Fabry-Perot etalons), which are supported by springs that compress under an applied force. At the proximal end of the fiber, the interferometric changes that are induced as the sensor is compressed are read out using reflectance spectroscopy, and the resulting spectral changes are calibrated with respect to applied force. This calibration is performed using either singular value decomposition (SVD) followed by linear regression or artificial neural networks. We describe the design and optimization of this device, with a particular focus on the data analysis required for calibration. Finally, we demonstrate proof-of-concept force sensing over the range 0-50 μN, with a measurement error of approximately 1.5 μN.

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=758&limit=5&page=18&respub-action=search.html Current Millis: 1642773048667 Current Time: Fri Jan 21 13:50:48 GMT 2022