The Pervasive Sensing Team from the Hamlyn Centre at Imperial release the Beta version of the BSN Platform.
about complicated sensor node programming is distracting you from the
real purpose of deploying your wireless sensor networks? Fear not, BSNOS
is here to help. Programming BSN applications normally requires an in
depth knowledge of embedded systems, low level programming, wireless
transmission, low power techniques, amongst other skills. Not all
researchers in BSN have these skills and most domain scientists, such as
sport scientists, who have no (or minimal) knowledge of programming,
find it difficult to develop their own BSN applications. The platform consists of the
operating system that runs on BSN nodes and an IDE to facilitate coding
and application download. Due to the popularity of Java, the team have
decided to expose a Java programming environment which encapsulates an
easy to use API (Application Programming Interface) to minimise the
learning curve to begin developing BSN applications.
In order to
provide a Java programming environment, the team conducted an
evaluation of different techniques that would enable a Java runtime
including Interpreters, Java-to-C compilers, Java-to-Native Code
compilers and run-time compilers. BSNOS is built on a run-time compiler
in order to provide an efficient execution platform along with a
platform independent application encoding. Click here
for more information.
|Ultra low power mixed signal ASIC for pervasive sensing |
The BSN ASIC aims to develop a low power generalised analogue
front-end signal processing module for biomedical applications, such
as: Photoplethysmogram (PPG), Electrocardiography (ECG) and so on. It
can be used for both sports in training and health care monitoring.
BSN platform has the following main
Front Ends to interface with transducers.
processing to reduce data dimensionality.
for reconfigurable control, data processing and
The Active Miles project is initiated by the ESPRIT team of the Hamlyn Centre
and the Institute of Global Health Innovation, Imperial College London.
The ESPRIT team consists of a group of multi-disciplinary researchers
with complementary skills in body sensor networks, pervasive computing,
smart textiles, biochemistry, biomechanics, mechanical engineering,
automation, sports performance research and complex system modelling.
ESPRIT BioPatch+ - A generic low-power disposable platform
for biomotion and biochemical sensing (left). A miniature electrochemical sweat
sensor developed jointly with the O-theme showing the microfluidic packaging
and simulated velocity of sweat flow in the microfluidic part (right).
The miniature wireless device (after attachment to skin) can
monitor electrochemical balance of human body by collecting sweat and define pH
and lactate level on it. The device consist on microfluidic package, chemical
membranes, gold electrodes, and electronics which can collect data and transfer
it to a PC
Biomotion+ is a light-weight wearable system for capturing human
motion and movement. Via placing several Body Sensor Network (BSN) nodes on the
human body segments, inertial and magnetic measurements can be collected and
combined to reconstruct segmental orientations of the human body.
This technology provides a
cost-effective solution for real-time capture in daily environment, and it can
be widely used for intelligent human computer interaction, robotic, healthcare
and other biomedical applications.
The image on top features the Biomotion + in action, measuring the gait analysis, running and cycling. Via tracking the movements of
patients, the system can computationally evaluate their performance of doing
sets of reach to touch and reach to grasp tasks, and generate feedbacks during
a task, after a task, and following sets of multiple tasks.
Zhiqiang Zhang, a Research Associate in the
Department of Computing, Imperial College London, has been working on Biomotion
+ together with several other projects in China, Singapore and UK in the
past 7 years. His research is focused on sensor data fusion and bio-motion
In his videos on the subject, Zhiqiang presents Body Sensor Network Bio-motion
Plus for detailed transient trajectory and body movement in gait analysis and
The videos are available here.
|ESPRIT Blackbox and ESPRIT Analytics|
Analytics are integrated sensing platforms for capturing, displaying and analysing real-time
performance indices for rowing.
The ESPRIT Blackbox is
a platform for logging and displaying BSN (Body Sensor Networks) sensor
data. The ESPRIT Blackbox mainly consists of a microprocessor, a BSN
node, a 3G module, and a flash memory storage unit. Through the
embedded BSN node, the Blackbox can manage all the BSN sensors and
capture different parameters, such as GPS positions and user's physical
Further research in Blackbox technology is currently being carried out to
extend the Blackbox with
interfaces with other BSN sensors, such as the Biomotion+ and conduct
trials on the application of the Blackbox on other sports and healthcare
In addition, new research platforms are being developed for ESPRIT Analytics+ to launch the Analytics+
software together with the Blackbox for rowing application.
In 2012 trials have been conducted jointly with GB Rowing to
test the functions of the ESPRIT Blackbox and the ESPRIT Analytic software, and
test the feasibility of using the Blackbox for training.
|e-AR (ear-worn Activity Recognition) Sensor|
The e-AR lite sensor is a bio-inspired design sensor which mimic the human vestibular system. By position the MEMS based sensor on the ear, the e-AR sensor can capture the similar posture and balance information as per the human inner ear.
Accordingly, the miniaturised e-AR sensor can continuously capture and infer the user's activities and movements. From the activity profiles and motion information captured, the energy expenditure of the user can be accurately estimated by the e-AR sensor.
In addition, the highly sensitive e-AR sensor can also capture the shock wave generated by the ground reaction force (GRF). Whenever a foot contacts the ground, there is a reaction force exerts back to the body and generated a shock wave. As the human skeleton is a good conductor for signals, the shock wave will transmit from the foot all the way to the head. The ground reaction force is a commonly used parameters in biomechanical analysis, and which can often be captured in laboratory settings. Being able to capture GRF information continuously, the miniaturised e-AR sensor can enable widespread use of pervasive sensors for quantify gait for sports and healthcare applications.
In 2012, pilot studies were conducted to investigate the application of the e-AR
sensor to capture gait and motion patterns for speed skating and blob skeleton. In addition, patient trials have been conducted to validate the use of the e-AR
sensor to quantify the recovery of patients after total knee replacement
operations. The highlight of the year was the summer exhibition in the Science museum demonstrating how can tiny sensors make a big difference in athlete's performance.
For more details, please refer to the following:
- J. Wiebold, L. Atallah, J, Kelly, D. Shrikrishna, K. Gyi, B. Lo, G-Z. Yang, D. Bilton, M. Polkey and N. Hopkinson, "Effect of acute exacerbations on skeletal muscle strength and physical activity in cystic fibrosis", in the Journal of Cystic Fibrosis, Vol 11 (3), pp.209-215, May 2012 .
- L. Atallah, A. Wiik, G. Jones, B. Lo, J. Cobb, A. Amis and GZ Yang, "Validation of an Ear-Worn Sensor for gait monitoring using a Force-Plate Instrumented Treadmill", in Gait and Posture, vol. 35 (2012), pp. 674-676.
- L. Atallah, G.J. Jones, R. Ali, J. Leong, B. Lo and G-Z. Yang., "Observing Recovery from Knee-Replacement Surgery by using Wearable Sensors", in Proceedings of the International Conference on Wearable and Implantable Body Sensor Networks (BSN 2011) in Dallas, Texas.
- L. Atallah, J. Leong, B. Lo and G-Z. Yang, "Energy expenditure prediction using a miniaturised ear worn sensor", In Medicine and Science in Sport and Exercise, July 2011 - Volume 43 - Issue 7 - pp 1369-1377
|WISDOM - Wheelchair Inertial Sensors for Displacement and Orientation Monitoring|
The WISDOM system is an inertial sensor based wheelchair motion monitoring and tracking system for indoor wheelchair sports. environment. Based on a combined use of 3D microelectromechanical system (MEMS) gyroscopes and 2D MEMS accelerometers, the WISDOM system provides real-time velocity, heading, ground distance covered and motion trajectory of the wheelchair across the sports court.The WISDOM system offers a number of advantages compared to existing platforms in terms of size, weight and ease of installation. Beyond sport applications, it also has important applications for training and rehabilitation for people with disabilities.
Improved wheelchair design in recent years has significantly increased
the mobility of people with disabilities, which has also enhanced the
competitive advantage of wheelchair sports. For the latter, detailed
assessment of biomechanical factors influencing individual performance
and team tactics requires real-time wireless sensing and data modelling.
In this paper, we propose the use of a miniaturized wireless
wheel-mounted inertial sensor for wheelchair motion monitoring and
tracking in an indoor sport environment.
During the research in 2012, the WISDOM system was used to measure the performance indices of
athletes in wheelchair basketball training.
More details can be found via: