The Pervasive Sensing Team from the Hamlyn Centre at Imperial release the Beta version of the BSN Platform.  
Worried 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. 
The BSN platform  has the following main modules:
1.Analogue Front Ends to interface with transducers.
2.Analogue/Mixed-Signal processing to reduce data dimensionality.
3.Microprocessor for reconfigurable control, data processing and

Active Miles
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.

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ESPRIT BioPatch+

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 Plus
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 analysis.

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 sports monitoring. The videos are available

ESPRIT Blackbox and ESPRIT Analytics
ESPRIT Blackbox and ESPRIT 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 measurements.
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 application. 
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:

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:

RACKET: Real-time Autonomous Computation of Kinematic Elements in Tennis

The Imperial Visual Sensing Network (VSN) module is an miniaturised intelligent video sensing module. Instead of outputting raw video images, the VSN processes all the video images on the node and outputs only the analysis results. One example application of the VSN is for analyzing the movements of tennis players during training, and the RACKET system is designed based on the VSN module for tennis application. To minimise the cost and setup time of the system, the RACKET sensors (ie the VSN modules) is designed with built-in self calibration mechanisms. To setup, the user can just simply mount the RACKET sensor high up at either end of the court, and the tracking information will be sent wirelessly to the server, and real-time tracking and location can be viewed on a web based display on a mobile phone or laptop. From our validation studies, it has been shown that the RACKET system can accurately track the tennis player on the court during games, and the density map can also be deduced.

More details can be found via:

Swimming Stroke Kinematic Analysis with BSN
A  single unobtrusive head-worn inertial sensor can be used to infer details on specific swimming techniques. The sensor, weighing only seven grams is mounted on the swimmer's goggles, minimising the disturbance to the users. Different type of strokes and basic biomotion indices can be deduced accurately using the sensor. This system represents a non-intrusive, practical deployment of wearable sensors for swimming performance monitoring.

More details can be found via: