Biomechanics and Medicine in Swimming XI

BiomechanicsandmedicineinswimmingXi
After a standard warm up (25-30 min), swimmers performed a 25
m front crawl at maximum speed with water start. A reproducibility test
was performed for this protocol by taking two individual measurements
with a 48-h interval between them.
Biomechanics Swimming acceleration and Medicine was in Swimming obtained XI from the Chapter position–time 2 Biomechanics data b
recorded using a position transducer (SignalFrame, SportMetrics, Valencia,
Spain), recording at 1 kHz. The position signal of the velocity
was derived twice to obtain the corresponding acceleration signal (m·s- 21
2 ). METHODS The apparatus consisted in a resistive sensor (i.e. which produced a
resistance After having of 250 signed g) with an informed a coiled cable consent, that all was the fastened procedures to the described swim- in this study
fulfilled the requirements listed in the Helsinki Declaration of 1975 and its later
mers’ amendment waists at in the October height 2000, of second seventy-nine and third regional lumbar and vertebrae national by front means crawl swimmers
of (mean a belt. All ± standard the pre- and error post-test of the data mean were (SEM) registered age 16.89±0.367 and converted years; weight
from 63.172±1.3373; analogical to height digital 172.54±1.142 (12-bit; DAQCard–700; cm) took part National in the experiments. Instrument, The swimmers
Austin, neither USA). suffered The musculoskeletal data were stored pathologies on a hard nor disk restrictions, for subsequent which analy- may have hindered
their performance during events.
ses. Synchronized After a standard to the warm position up (25-30 signal, min), several swimmers full stroke performed cycles a 25 were m front crawl at
recorded maximum using speed an with underwater water start. video A reproducibility camera, perpendicular test was performed to the swimfor
this protocol
mer’s by taking plane two of displacement individual measurements (the signal with was registered a 48-h interval at 50 between Hz). them.
To Swimming analyze the acceleration was signals, obtained a from specific the program position–time was data written recorded using a
position transducer (SignalFrame, SportMetrics, Valencia, Spain), recording at 1 kHz.
and run in Matlab 7.1 (R14) (Mathworks Inc., Natick, USA). The accel-
The position signal of the velocity was derived twice to obtain the corresponding
eration acceleration signal was signal filtered (m·s to preserve only those frequencies of interest
for the study. A Butterworth fourth-order digital filter was used for this
purpose with a band-pass of 1–20 Hz. This signal was then analyzed in
both the time and frequency domains. Given the fact that the size of the
acceleration is unstable in the first and final seconds for each swimming
set, the eight central seconds were selected in each trial to analyze the
acceleration signal (Caty et al., 2007). The signal amplitude was examined
in the time domain with a root mean square (RMS), and processed
in 100 ms-sized blocks. In the case of a set of n values {x1 ,x2 ,…,xn }, the
RMS value is given by the following formula:
(1)
The frequency spectrum amplitude was analyzed with the periodogram
method (Pollock, 1999), which permits to discover the hidden frequencies
in a signal. The periodogram considers all the frequencies and correlates
each frequency with the data of the series in order to estimate
the importance of a particular frequency in the series. It assigns to each
frequency a value called intensity of frequency denoted by:
-2 ). The apparatus consisted in a resistive sensor (i.e. which
produced a resistance of 250 g) with a coiled cable that was fastened to the swimmers’
waists at the height of second and third lumbar vertebrae by means of a belt. All the preand
post-test data were registered and converted from analogical to digital (12-bit;
DAQCard–700; National Instrument, Austin, USA). The data were stored on a hard
disk for subsequent analyses. Synchronized to the position signal, several full stroke
cycles were recorded using an underwater video camera, perpendicular to the
swimmer’s plane of displacement (the signal was registered at 50 Hz).
To analyze the acceleration signals, a specific program was written and run in
Matlab 7.1 (R14) (Mathworks Inc., Natick, USA). The acceleration signal was filtered
to preserve only those frequencies of interest for the study. A Butterworth fourth-order
digital filter was used for this purpose with a band-pass of 1–20 Hz. This signal was
then analyzed in both the time and frequency domains. Given the fact that the size of
the acceleration is unstable in the first and final seconds for each swimming set, the
eight central seconds were selected in each trial to analyze the acceleration signal (Caty
et al., 2007). The signal amplitude was examined in the time domain with a root mean
square (RMS), and processed in 100 ms-sized blocks. In the case of a set of n values
{x1,x2,…,xn}, the RMS value is given by the following formula:
(1)
The frequency spectrum amplitude was analyzed with the periodogram method
(Pollock, 1999), which permits to discover the hidden frequencies in a signal. The
periodogram considers all the frequencies and correlates each frequency with the data of
the series in order to estimate the importance of a particular frequency in the series. It
assigns to each frequency a value called intensity of frequency denoted by:
I(w)=[a(w)] 2 + [b(w)] 2 performed through Bonferroni post- hoc tests, because of its control
over the Type I error (error rate) and its strength when the number of
comparisons is small. All differences with p