Publikation:

How to Accurately Determine the Position on a Known Course in Road Cycling

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2018

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LAMES, Martin, ed., Dietmar SAUPE, ed., Josef WIEMEYER, ed.. Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017). Cham: Springer, 2018, pp. 103-109. Advances in Intelligent Systems and Computing. 663. ISSN 2194-5357. eISSN 2194-5365. ISBN 978-3-319-67845-0. Available under: doi: 10.1007/978-3-319-67846-7_11

Zusammenfassung

With modern cycling computers it is possible to provide cyclists with complex feedback during rides. If the feedback is course-dependent, it is necessary to know the riders current position on the course. Different approaches to estimate the position on the course from common GPS and speed sensors were compared: the direct distance measure derived from the number of rotations of the wheel, GPS coordinates projected onto the course trajectory, and a Kalman filter incorporating speed as well as GPS measurements. To quantify the accuracy of the different methods, an experiment was conducted on a race track where a fixed point on the course was tagged during the ride. The Kalman filter approach was able to overcome certain shortcomings of the other two approaches and achieved a mean error of −0.13m and a root mean square error of 0.97m.

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004 Informatik

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Cycling; GPS; Kalman filter; Road cycling; Feedback

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11th International Symposium of Computer Science in Sports (IACSS 2017), 6. Sept. 2017 - 9. Sept. 2017, Konstanz
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ISO 690WOLF, Stefan, Martin DOBIASCH, Alexander ARTIGA GONZALEZ, Dietmar SAUPE, 2018. How to Accurately Determine the Position on a Known Course in Road Cycling. 11th International Symposium of Computer Science in Sports (IACSS 2017). Konstanz, 6. Sept. 2017 - 9. Sept. 2017. In: LAMES, Martin, ed., Dietmar SAUPE, ed., Josef WIEMEYER, ed.. Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017). Cham: Springer, 2018, pp. 103-109. Advances in Intelligent Systems and Computing. 663. ISSN 2194-5357. eISSN 2194-5365. ISBN 978-3-319-67845-0. Available under: doi: 10.1007/978-3-319-67846-7_11
BibTex
@inproceedings{Wolf2018Accur-41237,
  year={2018},
  doi={10.1007/978-3-319-67846-7_11},
  title={How to Accurately Determine the Position on a Known Course in Road Cycling},
  number={663},
  isbn={978-3-319-67845-0},
  issn={2194-5357},
  publisher={Springer},
  address={Cham},
  series={Advances in Intelligent Systems and Computing},
  booktitle={Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017)},
  pages={103--109},
  editor={Lames, Martin and Saupe, Dietmar and Wiemeyer, Josef},
  author={Wolf, Stefan and Dobiasch, Martin and Artiga Gonzalez, Alexander and Saupe, Dietmar}
}
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    <dcterms:abstract xml:lang="eng">With modern cycling computers it is possible to provide cyclists with complex feedback during rides. If the feedback is course-dependent, it is necessary to know the riders current position on the course. Different approaches to estimate the position on the course from common GPS and speed sensors were compared: the direct distance measure derived from the number of rotations of the wheel, GPS coordinates projected onto the course trajectory, and a Kalman filter incorporating speed as well as GPS measurements. To quantify the accuracy of the different methods, an experiment was conducted on a race track where a fixed point on the course was tagged during the ride. The Kalman filter approach was able to overcome certain shortcomings of the other two approaches and achieved a mean error of −0.13m and a root mean square error of 0.97m.</dcterms:abstract>
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