Biomechanics and Medicine in Swimming XI

(including crawl stroke) have also been carried out (Nakashima 2007b).
The details are described in the references respectively. Some of the analysis
data and animation movies are open to the public at the SWUM website
(http://www.swum.org/).
The first extension made on SWUM was for the optimizing calculation.
In the optimizing calculation, a single simulation of the time
integration is repeated changing the design variables until a given objective
function is maximized. For the design variables, all the input data,
such as the body geometry of the swimmer and joint motion, can be
employed. With respect to the objective function, the user can describe
it freely using the output data, such as maximizing the swimming speed
and maximizing the propulsive efficiency. As the optimizing algorithm,
the Downhill Simplex method was employed. This extension has been
implemented in Swumsuit version 2.0.0.
The second extension was the musculoskeletal simulation. In the
musculoskeletal simulation, the human body is modeled as a series of
body segments accompanying muscles modeled as wires. For the musculoskeletal
calculation of the present study, commercial software, Any-
Body Modeling System (AnyBody) (AnyBody Technology, Denmark)
was employed. The whole body musculoskeletal model with 458 muscles
shown in Fig.1 was employed for the present study. With respect to the
data connection between SWUM and AnyBody, the joint motion (relative
body motion) is given as input data and the absolute movement of
the whole body is calculated as output data in SWUM. These motion
data are input into AnyBody. The fluid forces acting on swimmer are
also input into AnyBody as external forces distributed on the swimmer’s
body. This extension has been implemented in Swumsuit 3.0.0.
Figure 1. Whole body musculoskeletal model with 458 muscles employed
for the musculoskeletal simulation.
The third extension was “multi agent/object simulation.” “Multi agents”
means multiple swimmers and “multi objects” means implements for
swimming such as fins, a starting block, the pool wall, and so on. Before
this extension, SWUM had been capable of analyzing one swimmer
only by one simulation program. In the multi agent/object simulation,
multiple simulation programs for multiple agents/objects run simultaneously
to analyze. In addition to this, mechanical interaction among
the agents/objects can be described freely by the user. For example, if the
user would like to attach a fin to the swimmer, it can be simulated by
locating a sufficiently strong “virtual” spring between the swimmer and
fin. This extension has been implemented in Swumsuit 4.0.0.
With respect to the accuracy of simulation, it is difficult to estimate
the general value for all cases. The validation study for each case will be
necessary for the future task.
results And dIscussIon
The optimizing calculation for the trunk motion of the underwater dolphin
kick has already been carried out by the authors (Nakashima, 2009).
From this optimization, it was found that the obtained optimal motion
which maximizes the propulsive efficiency is considerably similar to that
of an elite athlete swimmer, in which the amplitude of the upper limbs’
displacement is small, the trunk moves as a ‘pitching seesaw’ with a node,
and the lower limbs form a traveling wave. As the next application of
the optimizing calculation, the optimization of arm stroke in the freestyle
swimming has recently been tackled by the authors (Nakashima et
al., 2009). In this optimization, the maximum joint torque characteristics
were imposed, and the design variables were joint angles during the
underwater stroke. As the next step, the optimization for more detailed
maximum joint torque characteristics, which depend on joint angles and
angular velocities, is now in progress. An example of its results is shown in
chaPter2.Biomechanics
Figure 2. In this optimization, the swimming speed was maximized under
the constraints of the joint torque characteristics and the range of motion
of the joints. Note that the time t was nondimensionalized by the stroke
cycle. In order to reduce the calculation time, PSO (Particle Swarm Optimization)
was employed as the optimizing method instead of the Downhill
Simplex method. The dark lines emitting from the swimmer’s body
represent the point of application, direction, and magnitude of the fluid
force acting on the swimmer. It was found that the thrust by the hand had
two clear peaks when pulling (t = 0.29) and pushing (t = 0.54) the water.
Figure 2. Simulation results of optimizing calculation (maximizing
swimming speed under constraints with respect to joint torque characteristics).
Top: side view, bottom: bottom view.
Next, the musculoskeletal simulation of the crawl stroke was carried out
by Nakashima et al. (2007a). The simulation results of muscle activities
were compared to those of an experiment in a previous study. It was
found that most of the timings of the muscle activations in the simulation
agreed with those in the experiment. The analyses of the other
three strokes have also been conducted (Nakashima et al., 2008). Many
reasonable tendencies were obtained in the simulation results. As a next
step, the examination of quantitative accuracy of the simulation is now
in progress. For this examination, the simulation results based on motion
analysis data were compared with experimental EMG data, which
were measured simultaneously with the swimming motion. An example
of the simulated and experimental results is shown in Fig.3. It was found
that the swimming motion obtained in the experiment was represented
well by the simulation. Note that the time t is nondimensional. It was
also found that the upper limb muscles were activated during the hand
stroke (Fig.3 (d)), and that the lower limb muscles were activated during
the kick (Fig.3 (f )). If the accuracy of such musculoskeletal simulation
becomes satisfactory, it will be greatly useful for the training and coaching
of swimmers.
Figure 3. Results of musculoskeletal simulation and images in the experiment
for the breaststroke.
Figure 4. Simulation results of synchronized swimming by three swimmers.
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