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

  • Conference paper
    Kwasnicki RM, Low DA, Wong C, Jarchi D, Lo B, Mathias CJ, Darzi A, Yang GZet al., 2012,

    Investigating the feasibility of using objective motion data to assist the diagnosis and management of cardiovascular autonomic dysfunction

    , Pages: 137-137
  • Journal article
    Aziz O, Atallah L, Lo B, Gray E, Athanasiou T, Darzi A, Yang GZet al., 2011,

    Ear-worn body sensor network device: an objective tool for functional postoperative home recovery monitoring.

    , J Am Med Inform Assoc, Vol: 18, Pages: 156-159

    Patients' functional recovery at home following surgery may be evaluated by monitoring their activities of daily living. Existing tools for assessing these activities are labor-intensive to administer and rely heavily on recall. This study describes the use of a wireless ear-worn activity recognition sensor to monitor postoperative activity levels continuously using a Bayesian activity classification framework. The device was used to monitor the postoperative recovery of five patients following abdominal surgery. Activity was classified into four groups ranging from very low (level 0) to high (level 3). Overall, patients were found to be undertaking a higher proportion of level 0 activities on postoperative day 1 which was gradually replaced by higher-level activities over the next 3 days. This study demonstrates how a pervasive healthcare technology can objectively monitor functional recovery in the unsupervised home setting. This may be a useful adjunct to existing postoperative monitoring systems.

  • Journal article
    Atallah L, Zhang J, Lo BPL, Shrikrishna D, Kelly JL, Jackson A, Polkey MI, Yang G, Hopkinson NSet al., 2010,

    Validation Of An Ear Worn Sensor For Activity Monitoring In COPD

  • Book chapter
    Ballantyne J, Johns E, Valibeik S, Wong C, Yang G-Zet al., 2010,

    Autonomous Navigation for Mobile Robots with Human-Robot Interaction

    , Robot Intelligence, Editors: Liu, Gu, Howlett, Liu, Publisher: Springer London, Pages: 245-268, ISBN: 978-1-84996-328-2
  • Conference paper
    Valibeik S, Ballantyne J, Lo B, Darzi A, Yang GZet al., 2009,

    Establishing affective human robot interaction through contextual information

    , Pages: 867-872

    Determining human intention is a challenging task for establishing affective human robot interaction. The aim of this paper is to provide a vision based framework to achieve a level of understanding about people in an environment before engaging in active communication or interaction. The proposed method combines multiple cues in a Bayesian framework to identify people in the scene and determine potential intentions. To improve the system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. Our results demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment. © 2009 IEEE.

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=6&respub-action=search.html Current Millis: 1579961590633 Current Time: Sat Jan 25 14:13:10 GMT 2020