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
Link To Document :
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