• DocumentCode
    484964
  • Title

    An automatic parameter extraction method for the 7Ã\x9750m Stroke Efficiency Test

  • Author

    Bächlin, Marc ; Förster, Kilian ; Schumm, Johannes ; Breu, Dominik ; Germann, Jürg ; Troster, Gerhard

  • Author_Institution
    Wearable Comput. Lab., ETH Zurich, Zurich
  • Volume
    1
  • fYear
    2008
  • fDate
    6-8 Oct. 2008
  • Firstpage
    442
  • Lastpage
    447
  • Abstract
    We developed an automatic method to extract the parameters of the 7 times 50 m Stroke Efficiency Test for swimming based on a wrist worn acceleration sensor device. In the wrist acceleration signal we detect characteristic swim events such as wall push-offs, wall-strikes and strokes. Based on this information we compute the distance per stroke and the swimming velocity. The upper error bounds of our automatic method are 1.67% for the velocity and 1.33% for the time per stroke. The velocity measurement accuracy is of comparable order to the manual accuracy. The automatic method clearly outperforms the manual measurement for the time per stroke extraction.
  • Keywords
    acceleration measurement; biomechanics; feature extraction; signal detection; velocity measurement; automatic parameter extraction method; signal detection; stroke efficiency test; swimming; velocity measurement; wrist worn acceleration sensor; Acceleration; Automatic testing; Data mining; Event detection; Life estimation; Parameter extraction; Sensor phenomena and characterization; Signal detection; Wearable sensors; Wrist;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pervasive Computing and Applications, 2008. ICPCA 2008. Third International Conference on
  • Conference_Location
    Alexandria
  • Print_ISBN
    978-1-4244-2020-9
  • Electronic_ISBN
    978-1-4244-2021-6
  • Type

    conf

  • DOI
    10.1109/ICPCA.2008.4783628
  • Filename
    4783628