Case Study 6 : Body Sensor Networks for Healthcare and Sports (BSN)
Submitting Institution
Imperial College LondonUnit of Assessment
Computer Science and InformaticsSummary Impact Type
TechnologicalResearch Subject Area(s)
Mathematical Sciences: Applied Mathematics
Information and Computing Sciences: Artificial Intelligence and Image Processing
Engineering: Electrical and Electronic Engineering
Summary of the impact
    Body Sensor Networks (BSN) research developed novel sensing algorithms
      and technology suitable for on-body pervasive sensing suitable for
      healthcare, well-being and sporting applications. The main impact
      includes:
    
      - Regulatory approval of BSN devices from the Federal Communication
        Commission (FCC) in 2012 and award of the CE mark in 2009.
 
      - Creation of the BSN technology spin-off company Sensixa in 2007 to
        manage licencing and commercialisation of the technology.
 
      - Adoption of the technology for training within Team GB in preparation
        for Winter Olympics 2010, Summer Olympics 2012 in London and other major
        international sport events.
 
      - Established the use of the technology in a clinical setting.
 
    
    Underpinning research
    The concept of Body Sensor Networks (BSN) was introduced to provide
      "ubiquitous" and "pervasive" monitoring of physical, physiological, and
      biochemical parameters without activity restriction or behaviour
      modification [1-2].
    Research in the area has been carried out since 2004 at the Centre for
      Pervasive Sensing led by Professor Yang and his research group in
      collaboration with Lord Darzi's research group, with initial funding from
      [i] and [ii]. From the outset, a system level approach to addressing
      biosensor design was taken with research into materials and
      biocompatibility, low-power application-specific integrated circuit,
      wireless communication, autonomic sensing, as well as distributed
      inferencing and data mining. Major technical hurdles of the BSN technology
      limiting its adoption are related to difficulties of continuous sensing
      and monitoring, long-term stability of the sensors and need for low-power
      operation.
    New algorithms amenable to real-time on-node processing and mapping to
      ultra-low power ASIC (application specific integrated circuit) have been
      developed. The use of a Bayesian feature selection technique for optimal
      sensor placement and feature selection for maximising the robustness and
      information content of the system, whilst minimising the number of sensing
      channels was successfully combined for the first time [4]. This provides
      the guiding principle for practical deployment of wearable sensors. To
      enable sensor miniaturisation and low power operation, a real-time
      neuro-network framework based on Spatio-Temporal Self-Organising Map
      (STSOM) has been implemented for mixed-signal ASIC design [5]. The
      combined use of analogue processing and digital control ensures
      sophisticated classification algorithms can be implemented on the chip
      level with very low power consumption. A reflective photoplethysmography
      sensor was also introduced which enables capturing a user's heart rate
      without resorting to the use of electrodes [3]. This work set the
      foundations for BSN research and facilitated the rapid-growth of the field
      internationally.
    The research above resulted in four patents being granted:
    
      - Patent PCT/GB2007/003861, published as WO2008/047078, filed
        11/10/2007, granted 24/4/2008. Pervasive Sensing — a vision-based sensor
        for smart home application.
 
      - Patent PCT/GB06/000948, published as WO06/097734, filed 16/3/2005,
        granted 9/12/2009. Spatial temporal self-organising map — data analysis
        method which is used partly in the software of the ear sensor.
 
      - Patent PCT/GB07/000358, published as WO07/088374, filed 02/02/2006
        granted 29/1/2009. Ear sensor for gait analysis.
 
      - Patent GB 0705033.9: filed 15/03/2007, granted 6/5/2010.
        Photoplethysmograph heart rate sensing system.
 
    
    These patents form the basis of an ear-worn activity recognition (e-AR)
      device. It emulates the sensory function of the ear vestibule for
      measuring balance, gait, as well as shock-wave transmission through the
      human skeleton. The innovative design of the device, as well as its
      intelligent on-node processing, has won the Medical Futures
        Translational Research Innovation Award (ENT) 2008 and the Bluetooth
        Innovation World Cup 2010 (innovator of the year and winner of the
      healthcare category, among 270 entries worldwide).
    In follow-on research and to support the quest for gold as well as the
      legacy after the London Olympic Games 2012, the GB sports governing bodies
      and research councils supported work in applying the sensor to sports
      training [iii]. One such example was the development of the use of the
      sensor for swimming. By using the sensor to derive the pitch and roll
      angles, it was shown to be possible to detect the type of stroke and the
      wall push-offs. Lap count and split times could be derived. The system
      represented a non-intrusive, practical deployment of wearable sensors for
      swim performance monitoring. It was established that for elite swimmers,
      the development of miniaturised sensors worn on wrists and ankles would
      provide further insights into the biomotion patterns for more detailed
      performance analysis [iii].
    References to the research
    Publications that directly describe the underpinning research
    * References that best indicate quality of underpinning research.
    
[1] *G.-Z. Yang Ed., Body Sensor Networks, London: Springer-Verlag, 2006
      ISBN 978-1-84628-272-0
     
[2] B. Lo, S. Thiemjarus, R. King and G.-Z. Yang, "Body Sensor Network —
      A Wireless Sensor Platform for Pervasive Healthcare Monitoring", Adjunct
      Proceedings of the 3rd International Conference on Pervasive Computing
      (PERVASIVE 2005), pp.77-80, 2005 Available from http://csis.pace.edu/~marchese/CS396x/L3/p077-080.pdf
     
[3] *L. Wang, B. Lo and G.-Z. Yang, "Multichannel Reflective PPG Earpiece
      Sensor with Passive Motion Cancellation", IEEE Transaction on Biomedical
      Circuits and Systems, 1(4): 235-241, 2007.
      http://dx.doi.org/10.1109/TBCAS.2007.910900
     
[5] *S. Thiemjarus, B. Lo and G.-Z. Yang, "A Spatio-Temporal Architecture
      for Context-Aware Sensing", In the IEEE Proceedings of the International
      Workshop on Wearable and Implantable Body Sensor Networks, pp.191-194,
      2006. http://dx.doi.org/10.1109/BSN.2006.5
     
[6] J. Pansiot, B. Lo and G.-Z. Yang, "Swimming Stroke Kinematic Analysis
      with BSN", In the Proceeding of the International Conference on Body
      Sensor Networks (BSN 2010), pp.153-158, 2010. http://dx.doi.org/10.1109/BSN.2010.11
     
Grants that directly funded the underpinning research
    [i] BiosensorNet: Autonomic Biosensor Networks for Pervasive Healthcare —
      EPSRC (EP/C547586/1) £1,403,908, Oct 2005 — Mar 2009. CoI Yang, Darzi
      & others from Imperial College
    [ii] SAPHE (Smart and Aware Pervasive Healthcare Environment) — TSB
      £1,650,248, Mar 2006 — Feb 2009. PI Yang
    [iii] ESPRIT with Pervasive Sensing (Programme Grant). EPSRC
      EP/H009744/1, G.-Z. Yang (PI) October 2009 — September 2014, £6,119,249
    Details of the impact
    The spin-out company, Sensixa (http://www.sensixa.com/),
      was established by Imperial College in 2007 as a company to promote and
      commercialize the BSN technology described in section 2. It currently
      holds the IP for the innovative e-AR sensor. The sensor has been developed
      to allow high volume production via manufacturing facilities in China. The
      sensor was awarded the CE mark in 2009 and received FCC approval in 2012
      which indicate the sensor can be used and sold in both Europe and USA [L].
    Coinciding with the London Olympic Games 2012 and as part of the UK's
      showcase of ICT to the world, BSN technologies developed at Imperial were
      among the few technologies selected by UKTI during its Life Sciences
      Technologies and ICT Technology Enabling the Game events [A]. Our work has
      resulted in innovative training solutions and sports equipment designs to
      secure competitive advantage for GB athletes. It has also contributed to
      obtaining an understanding of the biology of athletic performance to gain
      insights into the human physiological system for improving the health and
      wellbeing of the population at large. Outcomes of the e-AR sensor and its
      associated research have led to improvements in elite sport performance
      monitoring and training for Team GB in the run-up to the London 2012
      Olympics, including Rowing, Bobskeleton, Cycling, Sailing, Canoeing and
      Field Hockey. According to the Head of Sports Science and Research of the
      British Olympic Association this resulted in "tangible impacts for the
      preparation of our athletes for Vancouver 2010 and London 2012" [B]. The
      Head of Research & Innovation at UK Sports state that it has
      "demonstrated practical and commercial value of BSN through its extensive
      trials" [C]. The Chief Coach of Women and Lightweights GB Rowing Team
      states that "providing both athletes and trainers with sport-specific real
      time feedback allows for understanding of training and race analysis and
      performance" [D].
    The sensor was also used in the rugby union and league where it
      facilitated national and international teams to maintain their leading
      ranks. Specifically, work with the Wakefield Trinity Wildcats RL allowed
      them "to best prepare our playing team for the next Super League game
      based on the players recovery and then response to an appropriately loaded
      sessions during the week" [E]. It has been regarded as the "main driving
      force in the area of endocrinology, behaviour and performance" in sports
      underpinned by sensing technologies [F]. By using elite athletes as the
      exemplars, the technology developed has made sport and physical activity
      more enjoyable and rewarding. It promotes community participation in sport
      and physical activity and strengthens the feedback loop between exercise
      and health [G].
    The e-AR sensor also has application in the areas of healthcare as it
      allows one to objectively profile and compare a wide variety of patient
      outcomes post-operatively, and create a platform for remote patient
      surveillance and early detection of complications [H]. The e-AR sensor has
      featured in multiple clinical trials within the Imperial College
      Healthcare NHS Trust, including three trials that have been recognised and
      adopted by the NIHR portfolio for further support. Since 2008, over 150
      patients and numerous clinical collaborators have been involved in the
      on-going development of the e-AR sensor. Examples of this includes:
    
      - In 2008 15 post-operative general surgical patents were remotely
        monitored at home using the e-AR sensor [I].
 
      - Between 2010-2011 60 post-operative knee replacement patients had
        their gait pattern assessed using the e-AR sensor.
 
      - Between 2009 and 2011 14 knee replacement patients had their
        peri-operative mobility profiled at home using the e-AR [J].
 
      - In 2012, 25 patients used the device to supplement the results from
        ambulatory diagnostic tests.
 
      - In 2012-2013, 20 patients who had undergone lower limb reconstruction
        following trauma used the e-AR sensor every 3-months at their follow-up
        appointments, allowing a system to be developed (Hamlyn Mobility Score)
        that provides objective recovery information to patients, surgeons and
        service managers [K].
 
    
    Sources to corroborate the impact 
    [A] B. Lo, L. Atallah, B. Crewther, A.M. Spehar-Deleze, S. Anastasova, A.
      A. West, P. Conway, C. Cook, S. Drawer, P. Vadgama and G.-Z. Yang.
      Pervasive sensing for athletic training, Delivering London 2012: ICT
      Enabling the Games pp. 53-62, IET, 2011. Available from
      http://www.theiet.org/sectors/information-communications/highlights/ict-2012.cfm?type=pdf.
      Archived here
      on 22/10/2013
    [B] Head of Sports Science and Research, The British Olympic Association
      confirming details regarding the use of the BSN technology for Olympics
      Training.
    [C] Head of Research & Innovation, UK Sports confirming details
      regarding the practical and commercial value of the BSN technology for
      sports training.
    [D] Chief Coach, Women and Lightweights, GB Rowing Team confirming the
      value of the BSN technology for the GB Rowing Team.
    [E] Performance Director, Newcastle Knights Rugby Team, Australia,
      previously Head of Sports Science Support at Wakefield Trinity Wildcat
      confirming the impact of the BSN technology for rugby sports training.
    [F] Sport Performance Coordinator, University Centre Wakefield confirming
      the impact of the BSN technology for athlete training.
    [G] C. J. Cook and B. T. Crewther. Changes in salivary testosterone and
      subsequent squat performance following the presentation of short video
      clips. Journal of Hormones and Behaviour, 61:17-22, 2012. http://dx.doi.org/10.1016/j.yhbeh.2011.09.006
    [H] O. Aziz, L. Atallah, B. Lo, E. Gray, T. Athanasiou, A. Darzi and G.Z.
      Yang. Ear-worn Body Sensor Network Device: An Objective Tool for
      Functional Post-operative Home Recovery Monitoring. Journal of the
      American Medical Informatics Association (JAMIA), 18:156-159, 2011.
      http://dx.doi.org/10.1136/jamia.2010.005173
    [I] L. Atallah, O. Aziz, E. Gray, B. Lo and G. Z. Yang. An ear-worn
      sensor for the detection of gait impairment after abdominal surgery.
      Surgical Innovation, 20:86-94, 2013.
      http://dx.doi.org/10.1177/1553350612445639
    [J] R. M. Kwasnicki, R. Ali, S. J. Jordan et al. An Affordable, Objective
      Peri-operative Assessment Tool for Knee Arthroplasty. Associations of
      Surgeons in Training (ASiT) International Surgical Conference, Manchester,
      UK, 5th-7th April 2013 Oral Prize Session. Int J
      Surg (2013)
      http://www.scribd.com/doc/133767897/ASiT-Abstract-Book-2013-Ajb-Jeff-Version-Final-24-March
      pg 44 Copy also available on request.
    [K] R. M. Kwasnicki, S. Hettiaratchy, J. Simmons, C. Nightingale, G. Z.
      Yang and A. Darzi. Personal Motion Sensor Directed Rehabilitation After
      Lower Limb Reconstruction — a New Standard of Care. Plastic &
      Reconstructive Surgery. 132(4S-1): 55-56, 2013.
      http://dx.doi.org/10.1097/01.prs.0000435924.58341.98
    [L] CE and FCC certificates are available on request.