Case Study 6 : Body Sensor Networks for Healthcare and Sports (BSN)
Submitting InstitutionImperial College London
Unit of AssessmentComputer Science and Informatics
Summary Impact TypeTechnological
Research 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
- 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.
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
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
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 . 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 . 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 . This work set the
foundations for BSN research and facilitated the rapid-growth of the field
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.
 *G.-Z. Yang Ed., Body Sensor Networks, London: Springer-Verlag, 2006
 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
 *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.
 *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,
 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
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
[B] Head of Sports Science and Research, The British Olympic Association
confirming details regarding the use of the BSN technology for Olympics
[C] Head of Research & Innovation, UK Sports confirming details
regarding the practical and commercial value of the BSN technology for
[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.
[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.
[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
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.
[L] CE and FCC certificates are available on request.