© The Institution of Engineering and Technology
Presented is an accurate swimming velocity estimation method using an inertial measurement unit (IMU) by employing a simple biomechanical constraint of motion along with Gaussian process regression to deal with sensor inherent errors. Experimental validation shows a velocity RMS error of 9.0 cm/s and high linear correlation when compared with a commercial tethered reference system. The results confirm the practicality of the presented method to estimate swimming velocity using a single low-cost, body-worn IMU.
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Y. Ohgi ,
H. Ichikawa ,
C. Miyaji
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Microcomputer-based acceleration sensor device for swimming stroke monitoring.
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4 ,
960 -
966
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M. Bächlin ,
G. Tröster
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Swimming performance and technique evaluation with wearable acceleration sensors.
Pervasive Mob. Comput.
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1 ,
68 -
81
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F. Dadashi ,
F. Crettenand ,
G.P. Millet ,
K. Aminian
.
Front-crawl instantaneous velocity estimation using a wearable inertial measurement unit.
Sensors
,
10 ,
12927 -
12939
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11)
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C.E. Rasmussen ,
C. Williams
.
(2006)
Gaussian processes for machine learning.
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12)
-
E. Ceseracciu ,
Z. Sawacha ,
S. Fantozzi ,
M. Cortes ,
G. Gatta ,
S. Corazza
.
Markerless analysis of front crawl swimming.
J Biomech.
,
12 ,
2236 -
2242
-
13)
-
J. Favre ,
B.M. Jolles ,
O. Siegrist ,
K. Aminian
.
Quaternion-based fusion of gyroscopes and accelerometers to improve 3D angle measurement.
Electron Lett.
,
11 ,
612 -
614
-
14)
-
K. Aminian
.
Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes.
J. Biomech.
,
689 -
699
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2012.3684
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