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Experimental procedures
Sixteen right-handed female advanced tennis players who were members of
the Lafayette College tennis team (mean ± standard deviation: age, 20
± 1.4 years; weight, 54.0 ± 5.7 kg; height, 1.61 ± 0.08 m) served as subjects.
The relative skill level of the players was subjectively designated by
their coach via an integer-based numerical ranking scheme. The players
provided additional data regarding playing experience (11.2 ± 4.0 years)
and amount of formal coaching/instruction (7.8 ± 2.4 years).
Informed consent for the following procedure was obtained from all subjects.
Each subject had reflective markers placed upon her body and the racket
as described below. All subjects used the same midsize medium string racket
for consistency of racket inertia properties and mechanical response (Bahamonde
and Knudson, 2003).
After practicing for several minutes to acclimate to the markers, racket,
and testing environment, the subjects were asked to execute a series of
normal mid-level flat forehand shots using a closed stance. The closed
stance is defined by Bahamonde and Knudson, 2003
as "the body turned sideways to the net (hip perpendicular to the
baseline) and, as the ball approached, the player takes a step forward
toward the ball rotating the hips and trunk." All trials were performed
indoors with a ball machine projecting the ball at a waist-high level
(adjusted for each subject) at approximately 15 m/sec. Six trials were
recorded for each subject. The trial with the maximum ball velocity was
selected (Knudson and Bahamonde, 1999).
The subjects were not instructed that their knee positions and movements
were being investigated.
After these trials, each subject was instructed to repeat the closed stance
forehand swing while increasing by approximately 33% the pre-bending and
range-of-motion of the knees. In addition, each subject was instructed
to repeat the closed stance forehand swing while decreasing by approximately
33% the pre-bending and range-of-motion of the knees. Several practice
trials were run in an effort to have the subject become comfortable with
the increased/decreased movement trials. Once a relative level of comfort
was obtained, the subject swings were again recorded and selected in the
same manner as described above.
Extra trials were run with the most skilled player to investigate the
consistency of knee positions and range-of-motion for a given ball height
(20 trials), and to determine the effects of ball height on knee positions
and range-of-motion (10 trials at mid-thigh level and 10 trials at mid-torso
level). These trials, while outside the scope of the wider study, were
intended to provide context for the results obtained from the subject
group, and to suggest possible areas of further study.
An eight camera Motion Analysis Corporation system was used to track passive-reflective
markers that were placed upon the player and the racket. The system utilized
Eagle digital cameras (1280 x 1024 resolution) and operated at 200 frames
per second. There were 23 markers (13 and 19 mm in diameter) placed on
the player, and three on the racket. On the player the markers were located
at the wrists, forearms, elbows, shoulders, cervical and lumbar vertebra,
head, hips, knees, mid lower leg, ankles, and feet. All markers were located
relative to bony landmarks for consistency, and securely attached with
two- sided tape (skin) or Velcro (clothing). Markers were attached directly
to the skin wherever possible. Subjects wore snug-fitting clothing (tank-top
and bicycle-style shorts), a baseball hat (head marker), and shoes of
their choice. Marker/joint offsets were measured, and virtual joint- center
markers were located from these data using features provided by the data
collection software. Reflective tape was attached to the tennis ball to
determine the precise time of impact.
The three-dimensional marker paths were recorded at 200Hz then smoothed
with a Butterworth Filter Algorithm (Motion Analysis, 2004)
then processed to yield global body 1-2-3 angular motions of each body
segment and the racket. The global angular motions were transformed into
local relative joint position angles by comparing the orientations of
adjacent body segments using processes described in Craig, 1986.
These relative joint angles were used to kinematically drive the joints
of the computer model. Figure 1 shows the camera locations, the working
volume, global origin, and a stick figure representation of a subject
during forehand swing.
Computer
model
A full-body model of a human coupled to a parametric model of a tennis
racket was developed to determine the kinematic and kinetic quantities
necessary for this study (see Figure 2). The computer model was built, analyzed,
and post- processed with the aid of the commercial software packages ADAMS
(Mechanical Dynamics, Inc.) and LifeMod humanoid pre-processor (Biomechanics
Research Group, Inc.). ADAMS is a multi-body dynamic analysis program
where models are built from rigid segments connected with flexible elements
and/or a variety of joints. Forces and motions can be superimposed on
the model. ADAMS derives the differential equations of motion for the
model employing methods of Lagrangian dynamics. The equations of motion
are solved using one of several backward differentiation formula (BDF)
integrators. The results are output and the model is simulated using the
ADAMS postprocessor. This modeling approach has been used to analyze the
tennis swing and racket behavior (Nesbit et al., 2006),
as well as other sports motions and equipment behavior (Nesbit, 2007).
The player was modeled as a variable full-body, multi-link, three-dimensional
humanoid mechanism made up of seventeen rigid segments interconnected
with joints. The model was configured with the following fifteen body
segments; head, neck, thorax, lumbar, pelvic, upper arm (2), forearm (2),
thigh (2), lower leg (2), hand (2), and foot (2). All segments were defined
by their adjacent joints with exceptions of the neck (C1-C8), thorax (T1-T12),
and lumbar (L1-L5 and S1-S5) which were defined by the associated vertebrae.
The segment size, mass and inertia properties were determined from gender,
age, and overall body height and weight using the GeBod data base accessible
through the ADAMS software. The model consisted of the following sixteen
joints; ankles (2), knees (2), hips (2), lumbar, thoracic, neck, shoulders
(2), elbows (2), and wrists (2). All joints were spherical yielding a
maximum of three relative angular degrees-of-freedom with the exceptions
of the knees and elbows which were modeled as single degree-of-freedom
revolute joints. The motions superimposed upon the joints were specified
in terms of Bryant angles (see below) and their time dependent derivatives.
The body segment reference coordinate systems, established when the model
is posed in the standard anatomical position, places the Z-axis pointing
downward with the exception of the feet which point forward parallel to
the long axis of the foot segment. The X-axis points outward from the
body, and the Y-axis completes a right-handed coordinate system. Joint
motions, forces, and torques are of the distal body segment coordinate
system relative to the proximal body segment coordinate system. The angular
quantities are specified according to the relative body (Euler angle)
1-2-3 Bryant angle convention where alpha motion (α) is about the
X-axis, beta motion (β) is about the Y'-axis, and gamma motion (γ)
is about the Z''-axis (Kane et al., 1983).
The racket was modeled as a rigid structure with representative mass and
inertia properties (see Figure 3)
using the methods described in Nesbit (2006).
The mass (0.324 kg), mass center location (314.6 mm from end of handle),
and three principal inertia values (IGX = 14,613 kg-mm-s-2;
IGY = 13,394 kg-mm-s-2; IGZ = 1007.3
kg-mm-s-2) were determined using an inertia pendulum (Brody,
1985).
The connection between the racket and the hand was modeled as perfectly
rigid with no damping. This rigid body approach to the modeling of the
human/racket connection was similar to the methods of Bahamonde and Knudson,
2003
and Elliot et al. (2003)
in studying swing mechanics.
A ground surface model was added to support the humanoid model using methods
described in Nesbit et al., 1994.
A standard linear spring-damper system was used to represent the contact
between the feet and the ground, and frictional forces provided traction.
The initial contact parameters were obtained from Scott et al., 1993
and were adjusted at solution time to prevent over-stiffening the model.
The humanoid model was balanced by kinematically driving the angular DOF's
of the lower torso segment (hips) relative to the global coordinate system.
To avoid over- constraining the model, the linear DOF's were set free.
The ground reaction forces determined by this modeling approach yield
reasonable results compared to force plate data when used to study golf
swing mechanics (Nesbit, 2007).
For this study, the mean peak total vertical ground reaction forces as
determined by the model were 127 ± 3% of the subject's body weight which
agrees well with the force plate data of Iino and Kojima, 2001
and Van Gheluwe and Hebbelinck, 1986
who each report total ground reaction forces for one representative subject.
Force
plate data were not obtained for this study since is was not possible
to consistently predict the subjects' foot placements for the forehand
shot. Other studies of the closed forehand did use force plates (Iino
and Kojima, 2001;
Van Gheluwe and Hebbelinck, 1986),
however the subjects in these studies were instructed not to move their
feet while swinging the racket. Either forcing the subjects to keep both
feet in a stationary position, or requiring them to step in a predefined
manner in order to ensure consistent contact with force plates was thought
to be detrimental to the goals of the study. Allowing the subjects to
freely move their feet without being conscious of their placement was
believed to result in more representative knee movements. However the
consequence of determining joint moments via inverse dynamics without
force plate data are possible large errors in the kinetic results predicted
by the model. Thus the reader must consider the kinetic results predicted
by the model within this possibility.
Solution,
output, and verification of model
The humanoid and racket components of the model are rigid and kinematically
driven yielding simultaneous linear equations. However the ground-surface
model introduced non-linearities and time-dependent dynamic responses
into the system. Thus, the entirety of the model represents a forward
dynamics problem requiring numerical integration to solve. The resulting
dynamic equations of motion were solved using a Wielenga Stiff Integrator
(Mechanical Dynamics Inc.). Solution of the model yielded the kinematic
and kinetic quantities of the body joints, the macro body mass center
(CG) trajectories, racket kinematics, racket/hand interaction forces and
torques, and ground reaction forces. The work of the body joints were
determined from the joint kinematic and kinetic data using methods described
in Nesbit and Serrano, 2005
which are summarized in Appendix.
General verification of this modeling approach and model output was done
with force plate data, static anatomical posturing, and simple harmonic
joint motions (Nesbit et al., 1994
and Nesbit, 2007).
Where available, data from this study are compared to previously published
data to support the modeling approach (see above for ground reaction forces).
However the amount of kinematic and kinetic data reported in the literature
for the lower extremities for a closed-stance forehand swing are limited
and is provided mainly by Iino and Kojima, 2001
and Van Gheluwe and Hebbelinck, 1986.
Modeling
sensitivity analysis
A sensitivity analysis was performed to determine the effects of small
changes/errors to modeling parameters on the kinematic and kinetic results
predicted by the model. The number of parameters involved in this model
is considerable. Each body segment has associated length, mass, mass center
(CG) location, and inertial properties. The racket model adds its own
mass, CG location, and inertial properties to the overall model. The body
segment modeling parameters of length, mass, CG location, and inertial
properties were determined from population parameters (gender, age, height,
and weight), thus represent average values. As such the segment modeling
parameters
may be slightly different from the actual subject values. A sensitivity
analysis was performed using variations of ± 30mm on segment length and
mass center location, and ± 10% on inertial properties as suggested by
the literature references of Reinbolt et al., 2007.
These variations were applied to the left (front) lower leg segment, and
the effects upon the kinematic and kinetic quantities of the adjacent
ankle and knee joints were determined. The joint kinematic quantities
were not affected by changes in the inertial properties or location of
the mass center. These results were expected for kinematically driven
joints. The effects of small changes in segment mass, CG location, and
inertial properties when done individually had relatively linear effects
on the adjacent joint toques. The change in joint torque in every case
was either near or below the percentage change to the mass, CG location,
or inertia value. Changes in link lengths had the largest overall effect
on the joint kinematic and kinetic quantities. The joint angles for the
adjacent joints were affected to a small degree. This effect was magnified
slightly for the joints velocities and accelerations. Joint torque values
changed by as much as the square of the change in segment length. It appears
that the model is kinematically robust for small changes in all segment
modeling parameters. However, small changes in segment lengths had moderate
effects upon joint torque values. Thus the joint kinetic quantities predicted
by the model should be viewed within this context.
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