Biomechanics of Competitive Swimming Strokes
1. Introduction
Competitive
swimming is one of the most challenging sports to perform scientific
research. Not only the research of human movement is quite complex,
because human beings are not so determinists as other (bio)mechanical
systems; but also, assessing human beings in aquatic environment becomes
even more as this is not their natural environment and other physical
principles have to be considered.
On regular
basis, for human movement analysis, including the ones made on aquatic
environments, experimental and numerical methods are used. Experimental
methods are characterized by attaching bio-sensors to the subjects being
analyzed, acquiring the bio-signal and thereafter processing it.
Numerical methods are characterized by the introduction of selected
input data, processing data according to given mechanical equations and
thereafter collecting the output data. Both methods groups aim to
perform kinematics analysis, kinetics analysis, neuromuscular analysis
and anthropometrical/inertial analysis.
These
method groups are also used for biomechanical analysis of competitive
swimming. A swimming event can be decomposed in four moments or phases:
(i) the starting phase; (ii) the swimming phase; (iii) the turning phase
and; (iv) the finishing phase. During any swimming event, a swimmer
spends most of his/her absolute or relative time in the swimming phase.
Therefore, the swimming phase is the most (but not the only one)
determinant moment of the swimming performance. In this sense, a large
part of the biomechanical analysis of competitive swimming is dedicated
to the four competitive swimming strokes: (i) the Front Crawl; (ii) the
Backstroke; (iii) the Breaststroke and; (iv) the Butterfly stroke.
The
aim of this chapter has two folds: (i): to perform a biomechanical
characterization of the four competitive swimming strokes, based on the
kinematics, kinetics and neuromuscular analysis; (ii) to report the
relationships established between all the domains and how it might
influence the swimming performance.
2. Competitive swimming strokes kinematics
Consistent
swimming research started in the seventies. There is a significant
increase on the scientific production about competitive swimming
throughout the 1971-2006 period of time (Barbosa et al., 2010a)
that continuous nowadays. A large part of the swimming research is
dedicated to the swimming strokes kinematics. It can be considered that
some topics are assessed on regular basis: (i) stroke cycle kinematics;
(ii) limbs kinematics; (iii) hip and centre of mass kinematics.
2.1. Stroke cycle kinematics
Velocity
(v) is the best variable to assess swimming performance. For a given
distance, Front Crawl is considered the fastest swim stroke, followed by
Butterfly, Backstroke and Breaststroke (Craig et al., 1985; Chengalur & Brown, 1992).
Swimming
velocity can be described by its independent variables: stroke length
(SL) and stroke frequency (SF). SL is defined as being the horizontal
distance that the body travels during a full stroke cycle. SF is defined
as being the number of full stroke cycles performed within a unit of
time (strokes.min-1) or Hertz (Hz). Increases or decreases in v are determined by combined increases or decreases in SF and SL, respectively (Tousaint et al., 2006; Craig et al., 1985; Kjendlie et al., 2006). Those are polynomial relationships for all swim strokes (Keskinen & Komi, 1988; Pendergast et al., 2006) (Fig. 1). For Craig and Pendergast (1979)
the Front Crawl has the greatest SL and SF in comparison to remaining
swimming techniques. Authors suggested similar behavior for the
Backstroke except that at a given SF, the SL and v were less than for
the Front Crawl. At Butterfly stroke, increases of the v were related
almost entirely to increases in SF, except at the highest v. At
Breaststroke increasing v was also associated with increasing in SF, but
the SL decreased more than in the other swim strokes (Craig an Pendergast, 1979).
Throughout an event, the decrease of v is mainly related to the decrease of SL in all swim strokes (Hay & Guimarães, 1983). There is a “zig-zag” pattern for SF during inter-lap. The maximum SF on regular basis happens at the final lap (Letzelter & Freitag, 1983).
Comparing
the swim strokes by distance, there is a trend for SF and v decrease
and a slightly maintenance of SL with increasing distances (Jesus et
al., 2011; Chollet et al., 1996). Swimmer must have a high SL and, therefore, v should be manipulated changing the SF (Craig & Pendergast, 1979).
One
other variable often used to assess the stroke cycle kinematics is the
stroke index (SI). SI is considered as an estimator for overall swimming
efficiency (Costill et al., 1985).
This index assumes that, at a given v, the swimmer with greater SL has
the most efficient swimming technique. Regarding all the swimming
strokes, Front Crawl is the one with the highest SI, followed by
Backstroke, Butterfly and Breaststroke (Sánchez & Arellando, 2002). Analyzing it according to the distance, literature it not completely consensual. Sánchez and Arellano (2002)
reported a trend for SI decrease from the 50 to the 400 m events,
except at Breaststroke. On the other hand, Jesus et al. (2011) showed
not so obvious decrease in SI from shorter to longer distances in the
World Championships finalists. There was only a significant effect of
distance in SI for the female swimmers.
2.2. Limbs kinematics
Stroke
mechanics variables, including the SF and the SL are dependent from the
limb’s kinematics. That is the reason why some effort is done to
understand the contribution of the limb’s behavior. For instance, at
Front Crawl, Deschodt et al. (1996)
observed a significant relationship between the hip velocity and the
horizontal and vertical motion of the upper limbs. As the upper limb’s
velocity increased, the horizontal velocity of the swimmers increased as
well. Therefore, it can be argued that upper limbs velocity has a major
influence in swimming performance. Indeed, Hollander et al. (1988) found a small contribution of the legs to propulsion (approximately 10%) at Front Crawl. However, Deschodt et al. (1999)
reported a relative contribution of about 15%. To the best of our
knowledge there is no study about the partial contribution of upper and
lower limb’s to total swim velocity in the remaining strokes.
At
Front Crawl another issue is the contribution of the body roll to the
upper limb’s kinematics and therefore to swim performance. Some
researchers, such as Psycharakis and Sanders (2010),
suggest a high contribution of the body roll and its relationship to
breathing patterns to the limb’s kinematics. A better body roll imposes a
pronounced hand’s “S” shape trajectory that increases the thrust.
At Backstroke the body roll is also a main issue. Good level swimmers should have a better streamlined position (Maglischo, 2003); plus a large body roll and a higher emphasis in the kicking actions (Cappaert et al., 1996). The “S” shape of the hand’s path is also related to a higher thrust than other kind of trajectories (Ito, 2008).
At
Breaststroke, the timing between the upper and lower limbs is a major
concern. A significant relationship between upper and lower limbs
coordination with swim velocity was verified (Chollet et al., 1999). Tourny et al. (1992)
suggested that higher velocities might be achieved reducing the gliding
phase. Nowadays, the total time gap between arms and legs propulsive
actions is assessed on regular basis to understand this phenomenon (Seifert & Chollet, 2008).
At
Butterfly stroke, main kinematic aspects are the trunk angle, the arm’s
full extension during the upsweep and the emphasis in the second kick.
Higher trunk angle with horizontal plane will increase the projected
surface area and the drag force. To decrease it some butterfliers
breathe to the side (Barbosa et al., 1999) and others adopt a specific breathing pattern with no breathing in some cycles (Alves et al., 1999; Barbosa et al., 2003).
Butterfliers
with increased velocities present a higher extension of the elbow at
the upsweep, in order to increase the duration of this propulsive phase (Togashi & Nomura, 1992).
Considering the lower limbs kinematics, the reduction of the kick
amplitude plus the increase of kick frequency, combined with the
increase of the knee’s angle during the downbeat, seems to be the best
way to increase the swimmer’s velocity (Arellano et al., 2003). Barbosa et al (2008a)
found that a high segmental velocity of the legs during the downbeats,
specially the second one, will decrease the speed fluctuation.
For
all swim techniques, several manuscripts had demonstrated the
importance of the last phases of the underwater stroke cycle for
propulsion (Schleihauf, 1979;
Schleihauf et al., 1988). So, higher swim velocities are achieved
increasing the partial duration and the propulsive force during the
final actions of the underwater curvilinear trajectories (Fig. 2).
2.3. Hip and centre of mass kinematics
Hip
and/or centre of mass are considered as a way to analyze the body’s
kinematics. However, the hip is not validated as an appropriate
estimator of the centre of mass kinematics (Mason et al., 1992; Barbosa et al., 2003; Psycharakis & Sanders 2009).
The hip intra-cyclic velocity presents more variations than the centre
of mass. Besides, the peaks and troughs do not temporally coincide
throughout the stroke cycle. Inter-limbs actions during the stroke cycle
constantly change the centre of mass position (Psycharakis & Sanders, 2009).
The hip is not able to represent such variations since it is a fixed
anatomical landmark. Although this bias, the assessment of the
anatomical landmark is still an option for some research groups.
The
most often assessed variable related to the hip and/or the centre of
mass is the intra-cyclic variation of the horizontal velocity (dV).
Throughout the stroke cycle, the body‘s velocity is not uniform. There
are increases and decreases of the body’s velocity due to the limb’s
actions. Indeed, the dV has been considered as one of the most important
biomechanical variables to be assessed in competitive swimming
(Komolgorov & Duplisheva, 1992).
From a
mathematical point of view, the dV is described with non-linear
functions. Nevertheless, determination coefficients from those models
are moderate, since swimmers present different individual dV curves.
Individual curve present some changes in comparison to mean curves from
several subjects, expressing his/her interpretation of the swim
technique (Barbosa et al., 2010b).
At Front Crawl, dV has a multi-model profile (Barbosa et al., 2010c) (Fig. 3,
panel A). Higher peaks are related to arm’s actions and lower peaks to
leg’s actions. For some individual curve it can be noticed two higher
peaks with different velocities. Those peaks are related to the most
propulsive phases of each arm. Moreover, it seems that there is for some
subjects an asymmetrical application of propulsive force from both
arms. A similar trend can be verified for the Backstroke dV’s (Fig. 3, panel B).
At Breaststroke, dV is characterized by a bi-modal profile (Barbosa et al., 2010c) (Fig. 3,
panel C). One peak is related to arm’s actions and the other to the
leg’s action. Both peaks should be more or less even, but with a higher
value for the leg’s peak followed. After that peak, the gliding phase
happens with a v decrease. Indeed, the gliding phase is another issue to
consider regarding the Breaststroke dV. Subjects should know the exact
moment to start a new stroke cycle, avoiding a major decrease of the
instantaneous v (Capitão et al., 2006).
At Butterfly stroke, dV presents a tri-modal profile (Barbosa et al., 2003) (Fig. 3,
panel D). The first peak is due to the leg’s first downbeat, a second
peak related to the arm’s insweep, a last and highest peak during the
arm’s upsweep. The arm’s recovery is a phase when the instantaneous
velocity rapidly decreases.
Figure 3.
The
intra-cyclic variation of the horizontal velocity at Front Crawl (panel
A), Backstroke (panel B), Breaststroke (panel C) and Butterfly stroke
(panel D).
There is a
relationship between dV and v, as well as, between dV and the swimming
energy cost. There is a polynomial relationship between dV and v in the
four competitive swim strokes (Barbosa et al., 2006).
The dV increases to a given point with increasing v and then starts to
decrease. So, high velocities seem to impose a lower dV. Added to that,
increasing dV will lead to an increase in the energy cost of swimming,
even controlling the effect of the v (Barbosa et al., 2005; 2006).
In this sense, in all the four competitive strokes, a low dV leads to
higher swim efficiency. For instance, at Breaststroke more pronounced
body waving imposed a decreased dV (Persyn et al., 1992; Sanders et al., 1998; Silva et al., 2002).
At Butterfly stroke, a low velocity during hand’s entry, a high hand’s
velocity during the upsweep and a high velocity of the second downbeat
will decrease the dV (Barbosa et al., 2008).
So, some specific limb’s actions in each swim stroke are able to
decrease the dV and, therefore, to increase the swim efficiency and by
this way enhancing performance.
3. Competitive swimming strokes kinetics
For
a long time kinetic assessment was made adopting experimental research
designs. Since the beginning of the XXth century some research was done
to estimate the drag submitted and the propulsion produced by a swimmer.
Houssay in 1912, Cureton in 1930 and Karpovich and Pestrecov in 1939
are considered the pioneers in this kind of research (Lewillie, 1983).
One hundred years later, in the beginning of the XXIth century, new
research trends, based on computational simulation techniques (Bixler & Riewald, 2002; Bixler et al., 2007; Marinho et al., 2008) and particle image velocimetry (Kamata et al., 2006) have started.
Kinetics
analysis in swimming has addressed to understand two main topics of
interest: (i) the propulsive force generated by the propelling segments
and; (ii) the drag forces resisting forward motion, since the
interaction between both forces will influence the swimmer’s speed.
3.1. Propulsive force
The
swimmers’ performance is limited by their ability to produce effective
propulsive force (the component of the total propulsive force acting in
the direction of moving). The measurement of the propulsive forces
generated by a swimmer has been of interest to sports biomechanics for
many years. Despite the task of directly measuring the propulsive forces
acting on a freely swimming subject is practically impossible, Hollander et al. (1986)
developed a system for measuring active drag (MAD system) by
determining the propulsive force applied to underwater push-off pads by a
swimmer performing the Front Crawl arm action only. However, the
intrusive nature of the device disables its use during competition and
reduces its ecological validity (Payton & Bartlett, 1995). A non-intrusive method of estimating propulsive hand forces during free swimming was developed by Schleihauf (1979) and was the basis of several studies (Berger et al., 1995; Sanders, 1999).
In this method the instantaneous propulsive forces are estimated
according to vectorial analysis of forces combination’s acting on model
hands in an open-water channel and the recordings of underwater pulling
action of a swimmer. Using a plastic resin model of an adult human hand,
Schleihauf (1979)
measured forces for known orientations to a constant water flow,
determining drag and lift coefficients for specific orientations. These
data were then used together with digitized kinematic data of the hand
to estimate the lift, drag and resultant force vectors produced during
the stroke cycle of the swimmers.
The
relative contribution of drag and lift forces to overall propulsion is
one of the most discussed issues in swimming hydrodynamics research.
Regarding the water channel analysis, Schleihauf (1979)
reported that lift coefficient values increased up to an attack angle
around 40º and then decreased, although some differences with respect to
the sweepback angle were observed. Drag coefficient values increased
with increasing the attack angle and were less sensitive to sweepback
angle changes. In a more detail analysis, Bixler and Riewald (2002)
evaluated the steady flow around a swimmer’s hand and forearm at
various angles of attack and sweep back angles. Force coefficients
measured as a function of angle of attack showed that forearm drag was
essentially constant and forearm lift was almost zero (Figs. 4 and 5).
Moreover, hand drag presented the minimum value near angles of attack
of 0º and 180º and the maximum value was obtained near 90º, when the
model is nearly perpendicular to the flow. Hand lift was almost null at
95º and peaked near 60º and 150º.
Figure 4.
Drag coefficient vs. angle of attack for the digital model of the hand, forearm and hand/forearm (Sweep back angle = 0º). Adapted from Bixler and Riewald (2002).
When
the sweep back angle is considered, it is interesting to notice that
more lift force is generated when the little finger leads the motion
than when the thumb leads (Bixler & Riewald, 2002; Silva et al., 2008).
Another
important issue is related to the contribution of arms and legs to
propulsion. It is almost consensual that most of propulsion is generated
by the arms’ actions. In Front Crawl swimming, it was found (Hollander et al., 1988; Deschodt, 1999)
that about 85 to 90% of propulsion is produced by the arms’ movements.
Accordingly, the majority of the research under this scope is performed
on arm’s movements. Nevertheless, leg’s propulsion should not be
disregarded and future studies under this field should be addressed,
helping swimmers to enhance performance. Regarding arms’ actions, a
large inter-subject range of fingers relative position can be observed
during training and competition, regarding thumb position and finger
spreading. Although some differences in the results of different studies
(Schleihauf, 1979; Takagi et al., 2001; Marinho et al., 2009),
main results seemed to indicate that when the thumb leads the motion
(sweep back angle of 0º) a hand position with the thumb abducted would
be preferable to an adducted thumb position. Additionally, Marinho et al. (2009)
found, for a sweep back angle of 0º, that the position with the thumb
abducted presented higher values than the positions with the thumb
partially abducted and adducted at angles of attack of 0º and 45º. At an
angle of attack of 90º, the position with the thumb adducted presented
the highest value of resultant force.
Figure 5.
Lift coefficient vs. angle of attack for the digital model of the hand, forearm and hand/forearm (Sweep back angle = 0º). Adapted from Bixler and Riewald (2002).
When considering different finger spreading, Marinho et al. (2010a),
using a numerical analysis, studied the hand with: (i) fingers close
together, (ii) fingers with little distance spread (a mean intra finger
distance of 0.32 cm, tip to tip), and (iii) fingers with large distance
spread (0.64 cm, tip to tip), following the same procedure of Schleihauf (1979) research. Marinho et al. (2010a)
found that for attack angles higher than 30º, the model with little
distance between fingers presented higher values of drag coefficient
when compared with the models with fingers closed and with large finger
spread. For attack angles of 0º, 15º and 30º, the values of drag
coefficient were very similar in the three models of the swimmer’s hand.
Moreover, the lift coefficient seemed to be independent of the finger
spreading, presenting little differences between the three models.
Nevertheless, Marinho et al. (2010a)
were able to note slightly lower values of lift coefficient for the
position with larger distance between fingers. These results suggested
that swimmers to create more propulsive force could use fingers slightly
spread.
However, these studies were
conducted only under steady state flow conditions and as mentioned above
one knows (Schleihauf et al., 1988) that swimmers do not move their
arms/hands under constant velocity and direction motions. Therefore,
some authors (e.g., Sanders, 1999; Bixler & Riewald, 2002; Sato & Hino, 2003; Rouboa et al., 2006) referred that it is important to consider unsteady effects when swimming propulsion is analysed. For instance, Sato and Hino (2003)
using also numerical and experimental data showed that the hydrodynamic
forces acting on the accelerating hand was much higher than with a
steady flow situation and these forces amplifies as acceleration
increases.
3.2. Drag force
Regarding
the hydrodynamic drag, this force can be defined as an external force
that acts in the swimmer’s body parallel but in the opposite direction
of his movement direction. This resistive force is depending on the
anthropometric characteristics of the swimmer, on the characteristics of
the equipment used by the swimmers, on the physical characteristics of
the water field, and on the swimming technique.
The hydrodynamic drag resisting forward motion (D) can be expressed by Newton’s equation:
Where ρ represents the fluid density, CD represents the drag coefficient, S represents the projection surface of the swimmer and v represents the swimming velocity.
The
evaluation of the intensity of the hydrodynamic drag during swimming
represents an important aim in swimming biomechanics. Drag determined by
towing a non-swimming subject through the water (passive drag) has been
studied for a long time (Karpovich, 1933).
However, passive drag analysis does not consider the drag that the
swimmer creates when he produces thrust to overcome the drag, i.e.,
during actual swimming (active drag). Thus, one of the most important
parameters in the swimming hydrodynamics scope is to determine the drag
of a body that is actively swimming. This assumption resulted in
attempts to determine the drag of a person who is actively swimming.
Indeed, passive drag is lower than active drag for the same subject (Kjendlie & Stallman, 2008).
Aiming
to achieve this goal, techniques to assess active drag were developed
by several research groups in the 70s, based on interpolation techniques
(e.g., Clarys & Jiskoot, 1975; di Prampero et al., 1974).
These methods involved indirect calculations based upon changes in
oxygen consumption, as additional loads were placed on the swimmer (Marinho et al., 2010b). Later on, Hollander et al. (1986)
developed the MAD-system (measurement of active drag), relying on the
direct measurement of the push-off forces while swimming the Front Crawl
stroke only with arms. In the 90s, Kolmogorov and Duplishcheva (1992)
designed another method to determine the active drag: the velocity
perturbation method, also known as the method of small perturbations. In
this approach, subjects swim a lap twice at maximal effort: (i) free
swimming; and (ii) swimming while towing a hydrodynamic body that
creates a known additional drag. For both trials, the average velocity
is calculated. Under the assumption that in both swims the power output
to overcome drag is maximal and constant, drag force can be determined
considering the difference in swimming velocity. In contrast to the
interpolation techniques and the MAD-system, that required heavy and
costly experimental procedures, the velocity perturbation method just
required the use of the hydrodynamic body device and a chronometer to
assess active drag. Additionally, this approach can be applied to
measure active drag in the four competitive strokes. Other methods can
only be applied to the Front Crawl (e.g., the MAD-system, Hollander et al., 1986)
and the swimmer presents some segmental constrains, since legs are not
taken into account, as they are hold by a pull buoy. Using this approach
several studies has been conducted to evaluate active drag in swimming
(e.g., Kjendlie & Stallman, 2008; Marinho et al., 2010b). Kjendlie and Stallman (2008)
found that active drag in adults was significantly higher than in
children. This difference between adults and children was mostly due to
the different size and velocity during swimming. Marinho et al. (2010b)
also studied active drag comparing boys and girls, reporting that there
were no differences between boys and girls. A possible explanation may
be related to the similar values of body mass and height in boys and
girls found in this study. However, girls tended to have lower drag
values than boys, which can be also related to the lower velocities
achieved by the first ones.
The total drag
consists of the frictional, form and wave drag components. Frictional
drag is depending on water viscosity and generates shear stress in the
boundary layer. The intensity of this component is mainly due to the
wetted surface area of the body, the characteristics of this surface and
the flow conditions inside the boundary layer. Form drag is the result
of a pressure differential between the front and the rear of the
swimmer, depending on the velocity, the density of water and the cross
sectional area of the swimmer. Near the water surface, due to the
interface between two fluids of different densities, the swimmer is
constrained by the formation of surface waves leading to wave drag (Toussaint & Truijens, 2005).
The
contribution of form, friction and wave drag components to total drag
during swimming is an interesting topic in sports biomechanics. Data
available from several experimental studies show some difficulties
involved in the evaluation of the contribution of each drag component (Bixler et al., 2007).
It is mostly accepted that frictional drag is the smallest component of
total drag, especially at higher swimming velocities, although this
drag component should not be disregarded in elite level swimmers. Bixler et al. (2007)
using numerical simulation techniques found that friction drag
represented about 25% of total drag when the swimmer is gliding
underwater. Zaidi et al. (2008)
also found an important contribution of friction drag to the total drag
when the swimmer is passively gliding underwater. These authors found
that friction drag represented about 20% of the total drag. In this
sense, issues such as sports equipments, shaving and the decrease of
immersed body surface should be considered with detail, since this drag
component seems to influence performance especially during the
underwater gliding after starts and turns. In addition, form and wave
drag represent the major part of total hydrodynamic drag, thus swimmers
must emphasize the most hydrodynamic postures during swimming (Toussaint, 2006; Marinho et al., 2009). Although wave drag represents a huge part of total drag during swimming (Kjendlie & Stallman, 2008); when gliding underwater there is a tremendous reduction of this drag component. For instance, Lyttle et al. (1999)
concluded that there is no significant wave drag when a typical adult
swimmer is at least 0.6 m under the water’s surface. Moreover, Vennell et al. (2006)
found that a swimmer to avoid wave effects must be deeper than 1.8 and
2.8 chest depths below the surface for velocities of 0.9 m s-1 and 2.0 m s-1, respectively.
4. Competitive swimming strokes neuromuscular response
Since the early sixties some research was done regarding the swimming neuromuscular activity (Ikai et al., 1964). However, for a long time such research was merely qualitative, with a reduce focus quantifying this phenomena. For instance, Ikai et al (1964)
qualitatively showed that the bicep braquialis, the triceps braquialis,
the deltoid and grand dorsal were highly activate during the strokes.
On the other hand, for a quantitative perspective, the authors verified
that the elbow extensors presented a higher activation than the elbow
flexors at Front Crawl, Breaststroke and Butterfly stroke. Indeed this
electromyographic (EMG) assessment from Ikai et al. (1964) was thereafter the basis for the swimming stroke descriptions popularized in some swimming textbooks including the ones from Counsilman (1968) or Catteau and Garrof (1968). In the late sixties a research trend more focus in quantifying the EMG signal was started by Lewillie (1967; 1973) and followed by Clarys (1983; 1988).
Comparing to kinematics and kinetics researches, neuromuscular
assessments are the less used approach for competitive swimming.
4.1. Qualitative assessment
Qualitative
EMG relies on judgment of wave form patterns from neuromuscular
activity in graphical demonstration. Based on the visual interpretation
of the gross EMG signal it is possible to describe the neuromuscular
activation according to the temporal domain. In most circumstances, the
bio-signal amplitude and the duration are used as variables for a
temporal interpretation. The amplitude is roughly proportionally to the
force exerted by the underlying muscle. This relationship can be easily
appreciated by viewing the EMG signal in real-time while the intensity
of the muscular contraction increases. However, the EMG signal is not an
estimation of the muscle force produced. On the other hand, analyzing
the duration of muscular activation it is possible to observe whether a
muscle is active or inactive. Moreover, it is possible to establish
timing patterns for dynamic movements and the co-activation of several
opossite muscle groups.
For swimming
researchers the main focus relies in understanding the dynamics of
neuromuscular activity between strokes during the limbs and trunk
actions.
Lewillie (1973)
conducted a case study in the four strokes at three conditions (slow,
normal, fast). The highest neuromuscular activation was observed for the
Butterfly stroke at fast condition. Increasing intensity imposed an
increase in the anterior rectum and triceps surae activation for all
strokes. Nuber et al. (1986)
observed high activation of the supraspinatus, infraspinatus, middle
deltoid, and serratus anterior during the recovery phases of the Front
Crawl, Breaststroke and Butterfly. On the other hand, the latissimus
dorsi and pectoralis major were predominately pull-through phase muscles
(Nuber et al., 1986). Latter, similar activation during Front Crawl was reported by Pink et al. (1991)
for the pectoralis major and latissimus dorsi to propel the body and
for the infraspinatus to externally rotate the arm at middle of the
arm’s recovery. Authors also observed high activation for the three
heads of the deltoid and the supraspinatus during the arm’s entry and
exit.
A study in breaststrokes demonstrated
consistently activation for the serratus anterior and teres minor
muscles throughout the stroke cycle (Ruwe et al., 1994). Barthels and Adrian (1971)
found a great activity for the rectus abdominus and for the spine
erector, suggesting that the trunk movement in Butterfly stroke is
associated to the lower limbs action. Concerning the upper lims
propulsion, Pink et al (1993) reported that the serratus anterior and
the subscapularis maintained a high level of activation, being highly
susceptible to fatigue and vulnerable to injury.
4.2. Quantitative assessment
The
quantitative EMG analyzes the subtle changes on wave form patterns that
normally are missed or not appreciated by qualitative EMG. This
approach combines graphical interpretation with numerical processing
data to describe the neuromuscular activation. The amplitude and
duration analysis are improved using several data analysis procedures.
On a regular basis, researchers use some quantified variables, including
the root mean square (RMS) and threshold models for that purpose in the
time domain. The RMS is considered to be the most meaningful technique,
since it gives a measure of the power of the signal. Threshold
intervals are also helpful because they more clearly demarcate the
beginning and end of each muscle contraction. Both techniques require
the use of automated algorithms that extract and analyze motor unit
action potentials. The algorithms can simultaneously identify several
different motor units’ wave forms from the EMG signal to facilitate the
acquisition of more data in less time (Stálber et al., 1996).
One
other approach used in the quantitative EMG assessment is the spectral
analysis. This approach allows to change the signal from temporal domain
to frequency domain. Essentially it gives an evaluation of what
contribution each frequency has to the original sign. To evaluate the
different frequencies contents of maximal voluntary contraction the
usual procedure is to use the Fourier transformation. However, new
spectral indices (e.g. FInsmk) have been proposed and considered to be
valid, reliable and more sensitive than those traditionally used for
competitive swimming Dimitrov, 2006; Figueiredo et al., 2010).
Generally,
the mean and median frequencies of the EMG signal decrease with time
during a task that induces fatigue. The pratical aplication for spectral
analysis in swimming is to study muscle fatigue and its relationship to
limb’s kinematics. Monteil et al. (1996)
analyzing the fatigue at the beginning and at the end of a 400m Front
Crawl bout in a flume found a data decrease during the insweep phase
followed by an increase during the outsweep. Authors indicated a shift
of the force production from the insweep to the outsweep and a decrease
of hand velocity during the insweep phase. A similar phenomenon was
observed by Aujouannet et al. (2006).
EMG spectral parameters of the biceps brachii and triceps brachii
demonstrated a shift toward lower frequency before and after a maximal
4*50m swimming test (Aujouannet et al., 2006).
In a fatigue state, the spatial hand path remained unchanged, with a
greater duration of the catch, the insweep and the outsweep phases (Aujouannet et al., 2006).
A 4*100 Front Crawl test until exhaustion demonstrated larger muscular
recruitments obtained during the insweep phase and the antagonist
activities increases (Rouard et al., 1997). Caty et al. (2007) found an important stabilization of the wrist and high antagonist flexor and extensor carpi activity during the insweep phase (Caty et al., 2007). On the other hand, in outsweep phase, less stabilization and lower antagonist activities were noted (Caty et al., 2007). Fatigue analysis showed an increase in latissimos dorsi and triceps braquialis during 100m all out Front Crawl (Stirn et al., 2010).
When increasing distance to 200m, the inability to maintain swimming
velocity in the last laps was coincident with the increase of the
fatigue indices for the flexor carpi radialis, biceps brachii, triceps
brachii, pectoralis major, upper trapezius, rectus femoris and biceps
femoris (Figueiredo et al., 2010).
5. Competitive swimming strokes biomechanics and performance
The
main focus of swimming researchers is to enhance performance. From a
historic perspective, a large part of the research dedicated to
competitive swimming aims to identify variables that determine the
performance. This can be considered as an exploratory research trend.
Very recently, confirmatory data analysis became another topic of
interest. In such research designs, researchers try to understand the
relationships between the variables identified in previous researches
and model the links among them and performance (Barbosa et al., 2010b).
5.1. Exploratory research
With
exploratory research the aim is to identify from several biomechanical
variables those that are associated or related to the swimming
performance. This kind of research has been developed based on (Barbosa et al., 2010b): (i) comparing cohort groups; (ii) applying exploratory regression models and; (iii) implementing neural network procedures.
The
comparison of cohort groups is done comparing mean values or analyzing
the variation of some selected biomechanical variables between different
competitive level sub-sample groups. For instance, compare expert
versus non-expert swimmers, national level versus international/elite
level swimmers or, world championships and Olympic Games finalists
versus non-finalists. It is obvious that better competitive level is
related to a higher swim velocity. On the other hand, higher swim
velocity, from better swimmers, is achieved by an increasing stroke
length than remain swimmers (Craig et al., 1985; Vilas-Boas, 1996; Leblanc et al., 2007; Seifert et al., 2007). Higher level swimmers also present a higher efficiency, which is expressed by a higher stroke index (Sánchez et al., 2002;
Jesus et al., 2011). During high-standard competitions, world-ranked
swimmers already maintain a high stroke length. Therefore their
biomechanical strategy to increase the swim velocity is to increase as
well the stroke rate (Jesus et al., 2011). At least one study attempted
to compare the stroke cycle kinematics between World championships
medalists versus remaining finalists. There were no significant
differences in the stroke kinematics between medallists and
non-medallists. As both cohort groups have a very small gap performance,
differences between them might be explained by other variables (Jesus
et al., 2011). There are also some limb’s kinematics differences
according to competitive level. The elite swimmers posses a great
strength and power to accelerate through the water. They present a
limb’s kinematics making them able to apply it effectively. Plus, the
same limb’s kinematics also aims to maintain a better body streamlining
position to reduce drag force (Cappaert et al., 1996). For instance, comparing elite versus non-elite swimmers, participating in world championships and Olympic Games (Cappaert et al., 1996;):
(i) the trunk angle is lower and there is a higher elbow extension
during the finish phase of the pulling pattern for elite than for
non-elite swimmers swimming Butterfly stroke; (ii) there is a higher
body roll and a higher emphasis in the kicking for elite backstrokes
than non-elite ones; (iii) in Breaststroke, timing between arm’s and
leg’s actions is a key factor as non-elite swimmers sometimes achieve a
null body velocity within a stroke cycle; (iv) a higher elbow position
is required to achieve higher propulsion and a higher body roll in Front
Crawl, as done by elite swimmers in comparison to non-elite. Few
studies suggest that better competitive level swimmers also present a
lower intra-cyclic variation of the body’s swimming velocity (Manley and
Atha, 1996; Takagi et al., 2004). This seems consistent in Breaststroke but less obvious in remaining swim strokes and should be clear out in near future.
Another
possibility is to develop statistical models to identify the best
biomechanical predictors of swimming performance. Stroke length was
related to swimming economy (Costill et al., 1985)
and this one to swimming performance. One attempted was made to
determine the stroke cycle variables that are related to Olympic
swimmers performance. However, stroke rate, stroke length and stroke
index did not correlated significantly with the performance (Arellano et
al., 2001). As reported in the previous paragraph, the arguably best
swimmers in the world make it difficult to see trends in these variables
on the basis of stroke variations. Some papers report the prediction of
children swimming performance. The stroke index for the boys (Saavedra et al., 2003; 2010; Vitor & Bohem, 2010) and the mean velocity of a 50-m maximal bout for girls were included in the final models (Saavedra et al., 2003).
In both genders, from 9 to 22 years-old, for the 50-m freestyle event,
increases in the swim velocity happen due to increases in the stroke
length and stroke index (Morales et al., 2010).
Neural network is a somewhat recent approach to solve complex problems to model a given phenomena (Fig. 6).
Few attempts were made to apply this data analysis procedure to model
swimming performance (Pffier & Hohmann, in press). Modeling the
400-m freestyle performance in young male swimmers, based on several
variables including kinetic and kinematical ones, the estimation error
was 7±7.8% and for the 200-m medley performance 1.7±13.3% (Silva et al., 2007).
Same trend was reported in another couple of papers that included Front
crawl and Backstroke techniques, gliding in supine and back positions
to predict the 50-m Backstroke (Lobenius, 2003) and the stroke rate, swim velocity to predict the 50-m freestyle event (Hohman & Seidel, 2010).
5.2. Confirmatory research
This
procedure consists of a mathematical approach for testing and
estimating causal relationships using a combination of statistical data
and qualitative causal assumptions previously defined by the researcher
to be (or not to be) confirmed. This approach rather than to identify
variables, suggests the kind of interplay existing among them (Barbosa et al., 2010d).
Hence, structural equating modeling allows analyzing the hypothetical
relationships between several biomechanical variables with swim
performance and the model’s good-of-fit. Indeed this approach is often
used on other scientific domains although it is not so popular in the
sport’s performance, including competitive swimming. To the best of our
knowledge this procedure only was applied for young swimmers.
One
paper reported the development of a path-flow analysis model for young
male swimmers’ performance based on biomechanical and energetics
variables (Fig. 7).
The model included variables such as the stroke length, stroke rate,
stroke index, and swim velocity. The confirmatory model explained 79% of
the 200-m freestyle performance and being suitable of the theory
presented (Barbosa et al., 2010d).
One other study developed a structural equation modeling for active
drag force based on anthropometric, hydrodynamic (i.e., frontal surface
area, drag coefficient) and biomechanical variables (i.e., stroke
length, stroke rate and swim velocity) in young boys (Barbosa et al., 2010e).
The confirmatory model explained 95% of the active drag after the
elimination of the frontal surface area. Main limitation of the model is
related to the frontal surface area estimation equation that does not
fit in the model. The confirmatory model included all selected
anthropometrical variables, prone gliding test, stroke length, stroke
frequency and velocity. Final model excluded the vertical buoyancy test.
The confirmatory path-flow model good-of-fit was considered as being
very close to the cut-off value, but even so not suitable of the theory.
Vertical buoyancy and prone gliding tests are easy and cheap procedures
to assess swimmer’s kinetics. However, both procedures are not the best
techniques to assess the swimmer’s hydrostatic and hydrodynamic
profile, respectively. Hohmann and Seidel (2010)
predicted 41% of girl’s 50-m freestyle performance based on
psychological, technique (i.e., stroke rate, swim velocity, limb’s
coordination), physical conditioning and anthropometrical variables.
6. Conclusion
There
are several biomechanical variables determining the competitive
swimmer’s performance. For instance, some of those are kinematics
variables (e.g., stroke length, stroke frequency, speed fluctuation,
limbs’ kinematics), kinetics variables (e.g., propulsive drag, lift
force, drag force) and neuromuscular variables.
Attempts
are being made nowadays to understand the links between all these
variables and how it is possible to enhance performance manipulating it.
Some models about these relationships are already at the disposal of
practitioners. Moreover, a great effort is done by researchers and
coaches to assess, to compare and to manipulate these variables from
times to times to define goals, establish milestones in the
periodization program or even predict the swimmers performance.
La informacion que nos compartes es muy extensa gracias de antemano por la ayuda.
ReplyDeleteAlan Eduardo Almendarez Loperena