Biomechanics and Medicine in Swimming XI

BiomechanicsandmedicineinswimmingXi
The graph at the top of Fig. 4 shows the transition of flexion/
extension motion of the forearm described with angular velocity.
The graph in the middle indicates the wavelet transformation
for the same data and the graph at the bottom shows the transition
of depth. Each of them was acquired with the sensor attached on the
forearm in the freestyle stroke. Comparing the graphs at the top and
the bottom, the angular velocity in the stroke phase is almost constant,
while that in the recovery phase decreased rapidly. The Fourier transformation
converts a spectrum space of a certain period into a frequency
space. On the other hand, the wavelet transformation is the time series
data Biomechanics of the momentary and Medicine frequency XI Chapter conversion 2 Biomechanics in the same time space. In
other words the Fourier transformation is less useful in analyzing nonstationary
data, where there is no repetition within the region sampled.
The wavelet transformation allows the components of a non-stationary
signal to be analyzed better. In the middle of Fig. 4, the thick striped
pattern appears in the dominant cycle for about 2 seconds of the respective
movements. The output of wavelet transformation can be sliced in
x and y directions. Figure 5(a) and 5(b) are the sections of Fig. 4 at
Time A and B respectively. These sliced sections show momentary cycle
(=1/frequency) analyses. In these figures, the primary peak indicates the
stroke Fig. cycle. 6. Cycle The sliced secondary section data peak of wavelet appears transform in the cycle output, of Fig.4 S-shaped stroke.
Figure at sliced 6 shows section, cycle Cycle sliced 2.2 sec. section This is data a stroke of cycle wavelet of freestroke transformation out-
in this case. The bottom peak shows the turn phase.
put at Slice Section C in Fig. 4 in Cycle 2.2 sec. This is a stroke cycle of
freestyle in this case. The bottom peak shows the turn phase.
words the Fourier transformation is less useful in analyzing non-stationary data, where
there Thus, is no the repetition wavelet within transformation the region sampled. converts The wavelet motion transformation waves allows to pat- the
tern components designs. of Three a non-stationary acceleration signal waves to be analyzed and three better. angular In the middle velocity of Fig. waves 4, the
thick striped pattern appears in the dominant cycle for about 2 seconds of the respective
acquired with a motion logger indicate six motion patterns. The four
movements. The output of wavelet transformation can be sliced in x and y directions.
swimming Figure 5(a) styles and 5(b) freestyle, are the sections breaststroke, of Fig. 4 backstroke at Time A and and B respectively. butterfly stroke, These
consist sliced sections of 24 pattern show momentary pictures. cycle The (=1/frequency) difference of analyses. picture In patterns these figures, should the
primary peak indicates the stroke cycle. The secondary peak appears in the cycle of S-
lead shaped to another stroke. Figure motion 6 shows analysis cycle with sliced wavelet section transform. data of wavelet Figure transformation 7 shows
output the at transition Slice Section of C flexion/extention in Fig. 4 in Cycle 2.2 motion sec. This of is a the stroke forearm cycle of described freestyle in
this case. The bottom peak shows the turn phase.
with angular Thus, the wavelet velocity, transformation the wavelet converts transform motion for waves the to same pattern data designs. and Three the
transition acceleration of waves depth and in three the butterfly angular velocity stroke. waves Two acquired stripe patterns with a motion appear logger in
indicate six motion patterns. The four swimming styles freestyle, breaststroke,
the backstroke wavelet and transform butterfly stroke, picture. consist Each of 24 stripe pattern shows pictures. the The basic difference stroke of picture cycle
and patterns the cycle should of lead a keyhole to another shaped motion analysis stroke with in the wavelet butterfly transform. stroke. Figure 7 shows
104
Fig. 7 Gyro sensor data of x-direction, wavelet transform for the same wave and
transition of depth acquired in the butterfly stroke.
conclusIons
The motion filming equipment was developed to show the whole
body, in a synthesized swimming image of the underwater and
overwater motion. Furthermore, this synthesized swimming image
was synchronized with the data obtained with the data logger
which samples the 3D acceleration and 3D angular velocity
of the forearm movement.
Wavelet transformation was performed on the motion data and the
various motions were visualized as a new motion analysis method. As
implied by the wavelet transformation, it should be possible to express
the difference in the strokes even in the same event or to express a collapse
of technique by fatigue by showing the motion as picture patterns.
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reFerences
Barbosa, T.M., Lima, F., Portela, A., Novais, D., Machado, L., Colaço,
P., Gonçalves, P., Fernandes, R.J., Keskinen, K.L. & Vilas-Boas. J.P.
(2006) Relationships between energy cost, swimming velocity and
speed fluctuation in competitive swimming strokes. In: Biomechanics
and Medicine in Swimming X. Eds: Vilas-Boas, J.P., Alves, F. and
Marques, A. Portuguese Journal of Sport Sciences 6 (Supl 2), 192-194.
Ito, S. (2003), Swimming form analysis of competitive free style,Proc.
Annual Meeting, Japan Society of Mechanical Engineers, 03-1, 65-
66.
Ohgi, Y. (2006), Pattern matching application for the swimming stroke
recognition, Biomechanics of Medicine in Swimming X, J.P.Vilas-
Boas, F. Alves, A. Marques, eds., Proc. of Xth International Symposium
Biomechanics and Medicine in Swimming, 69-70.
Sakamoto, K.Q., Sato, K., Ishizuka, M., Watanuki, Y., Takahashi, A.,
Daunt, F. & Wanless, S. (2009). Can Ethograms Be Automatically
Generated Using Body Acceleration Data from Free-Ranging Birds?
PLoS ONE 4(4), e5379.