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Optimized athlete body sensor networks for simulation-based performance analysis ESPRIT PoC

Swimming data capture. Optimized athlete body sensor networks for simulation-based performance analysis ESPRIT PoC AIJ Forrester, CJ Brooks, SR Turnock, DJ Taunton, DA Hudson, K Takeda, JIR Blake, MP Prince, CWG Phillips,

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Optimized athlete body sensor networks for simulation-based performance analysis ESPRIT PoC

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  1. Swimming data capture Optimized athlete body sensor networks for simulation-based performance analysis ESPRIT PoC AIJ Forrester, CJ Brooks, SR Turnock, DJ Taunton, DA Hudson, K Takeda, JIR Blake, MP Prince, CWG Phillips, AP Webb, J Banks, Faculty of Engineering and the Environment MJ Stokes, M Warner, Faculty of Health Sciences The objective for this PoC is the accurate simulation of swimmer performance in the competition environment. In order to understand the factors that influence performance, we obtain qualitative and quantitative data using specially designed equipment and techniques. Subsequent data analysis enables us to focus on the various components of an individual swimmer’s technique, and provide useful feedback for both coach and athlete. Our data acquisition experience Testing has been conducted on 35 elite athletes in 43 testing sessions, culminating in 575 measurement runs. An intensive feedback environment has been achieved on poolside incorporating force/speed measurement and video analysis. Data from these sessions is stored and used for further post analysis to increase understanding of the swimming problem. Recently flow visualisation of underwater dolphin leg kick has been achieved using a bubble sheet. This enables the flow features of a successful technique to be identified. Figure. Bubble screen to aid flow visualisation Tow rig A tow system has been designed and manufactured allowing swimmers to be towed actively and passively at a range of depths and speeds. Figure. The latest tow system for British Swimming Assistive software The i-DAQ software is able to simultaneously record force and video data. The data is processed and synchronised with the video. Shortly after each run this information is able to be displayed to the athlete and coach. They can step through the video frame by frame and gain an insight into how the force changes through a stroke cycle. Figure. The i-DAQ tow rig data acquisition system. Wireless sensors A practical and efficient wireless Body Sensor Network (BSN) has been developed enabling us to capture body orientation and linear motions in a non-intrusive manner. Data from the onboard inertial sensors on each unit can be streamed via Bluetooth, or logged to a miniSD card for later wireless download, to a host PC. Host software has been developed for wireless operation of the sensor units, and for performing post-processing of the data. A number of metrics, including swimmer pitch and roll, propulsive efficiency, joint angles and linear acceleration are available for use in both musculoskeletal and hydrodynamic simulation, and as a real-time training aid. Figure. Orientation and linear motion from a lower back mounted sensor http://www.soton.ac.uk/engineering/research/groups/ced.page| email: Alexander.Forrester@soton.ac.uk Computational Engineering & Design Group, University of Southampton, SO17 1BJ, U.K.

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