Using GPS and Accelerometer Data for Rowing Race Tracking

Hermsen, H.C. (2013) Using GPS and Accelerometer Data for Rowing Race Tracking. Master's Thesis / Essay, Artificial Intelligence.

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Abstract

Rowing is one of the oldest Olympic Sports and still internationally popular, especially among students. Races are often held side by side on a track over a distance of 2000 meters in six lanes, where spectators are positioned near the finish line. Due to the length of the track only the last 30 seconds of the race can be physically seen and the preceding battle is lost. In this thesis a solution is presented which uses small, cheap and easily available commercial electronic components to be able to present a live tracker of the full race to the audience along the track and at home. The solution consist of an onboard device with GPS and accelerometer sensors, a microcontroller and a ZigBee communication module, and a land based receiver which interacts with the data processing and visualization software. Communication over 2000 meters distance is achieved using mesh networking, which allows for roving nodes. To minimize network traffic and save on battery capacity, a computationally cheap algorithm is presented which can be executed on the onboard microprocessor and applies knowledge about the anatomy of the rowing stroke to obtain orientation invariant accelerometer measurements. Because of this the onboard device can be placed on the boat regardless of the orientation, making the solution as unobtrusive to the athletes as possible. Two types of peak detection methods are compared to perform stroke detection in real-time on the microcontroller. Furthermore sensor fusion methods are examined to improve GPS location accuracy by incorporating data from a static GPS receiver and the onboard accelerometer. It is shown that an onboard device can be made for under € 100,- and can report location, speed and stroke rate in real-time.

Item Type:	Thesis (Master's Thesis / Essay)
Degree programme:	Artificial Intelligence
Thesis type:	Master's Thesis / Essay
Language:	English
Date Deposited:	15 Feb 2018 07:55
Last Modified:	15 Feb 2018 07:55
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/11391

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