Vol. 91, Issue 3, 1307-1317, September 2001
Muscle activity in the leg is tuned in response to ground
reaction forces
James M.
Wakeling,
Vinzenz
Von Tscharner,
Benno M.
Nigg, and
Pro
Stergiou
Human Performance Laboratory, Faculty of Kinesiology, University of
Calgary, Calgary, Alberta, Canada T2N 1N4
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ABSTRACT |
During walking and running, the human body reacts to its
external environment. One such response is to the impact forces that occur at heel strike. This study tested previous speculation that the
levels of muscle activity in the lower extremities are adjusted in
response to the loading rate of the impact forces. A pendulum apparatus
was used to deliver repetitive impacts to the heels of 20 subjects.
Impact forces were of similar magnitude to those experienced during
running, but the loading rate was varied by 13% using different
materials in the subjects' shoes. Myoelectric patterns were measured
in the tibialis anterior, medial gastrocnemius, vastus medialis, and
biceps femoris muscles. Wavelet analysis was used to resolve intensity
of the myoelectric patterns into time and frequency space. Substantial
and significant differences in the myoelectric activity occurred
between the impact conditions for the 50 ms before and the 50 ms after
impact, reaching 3 ms in timing, 16% in wavelet number, and 154% in
the intensity of the muscle activity.
electromyogram; wavelet; impact force; time-frequency analysis
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INTRODUCTION |
DURING WALKING AND
RUNNING, the human body reacts to input from its external
environment. One such input is the ground reaction force, which occurs
during the ground contact phase of each stride. One possible reaction
is the modification of the muscle activity patterns in response to that
force. The lower extremity muscles are used for many different tasks
during each stride, including the control of skeletal position, joint
stiffness, vibrations of the soft tissue packages, stability during
ground contact, and propulsion for the movement task. It has been
speculated that there is a requirement for the muscles to control and,
thus, minimize soft tissue vibrations during locomotion
(25) and, thus, that there will be a change in muscle
activity patterns in response to different vibration loadings on the
lower extremity.
Impact forces in heel-toe walking and running are forces resulting from
the collision of the heel with the ground, reaching their maximum (the
impact peak) earlier than 50 ms after first contact (25).
The rate at which the impact peak is reached is termed the loading rate
and is a correlate of the major frequency of the impact peak. Impact
forces have frequency contents in the range 10-20 Hz and should be
expected to produce vibrations of the soft tissues of the body.
However, observations suggest that impact-related vibrations are
minimal in the muscular soft tissues of the lower extremities during
walking and running. One possible mechanism to reduce soft tissue
vibrations is to ensure that the resonant frequencies of the soft
tissues are distinct from the frequency of the impact force and that
vibrations within the soft tissues are strongly damped. The frequency
and damping coefficients of vibrations in the soft tissues of the lower
extremities are increased by increases in muscle force production and
muscle shortening velocity (34). The natural vibration
frequencies of the triceps surae, quadriceps, and tibialis anterior
tissues are in the range 10-50 Hz, depending on the levels of
muscle activity (34). Therefore, it should be expected
that if the frequency of the impact force is close to the natural
frequency of the soft tissues, then additional muscle activity will be
used to minimize possible vibrations. Muscle activity has been shown to
respond to frequencies of applied continuous vibrations in the medial
head of the triceps brachii in the arm during isometric tests
(7). However, changes in the myoelectric patterns of the
lower extremities that may occur in response to the different impact
conditions experienced during walking and running have not yet been demonstrated.
To test whether the body reacts to the loading rate of the impact force
by modifying muscle activity, an experiment must be performed that can
alter the loading rates of the impact force. The magnitude of the
impact force typically increases with the running velocity (8,
12, 26); however, the magnitude of the impact force remains
relatively insensitive to changes in cushioning from the shoe or
ground. However, the shoe cushioning does change the loading
rate (a correlate of frequency) of the impact force (8, 16, 17,
19, 26). Thus, changing the material properties of the shoe
provides a tool by which the loading rate of the impact force can be
experimentally manipulated.
To isolate the changes in muscle activity due to the ground impact from
the muscle activity that is required for joint movement, an
experimental procedure must be used that can minimize the myoelectric activity required for joint movement. One such approach is to use a
human pendulum apparatus (17, 18). Use of this apparatus involves the subject lying supine on a bed with the leg supported by a
harness. The pendulum motion causes the heel to impact with a wall, and
thus impacts are generated without requiring the joints to move. The
cyclical swinging of the subject on the pendulum results in a series of
impact forces that mimic those found during running.
In this study, we use such a pendulum technique to investigate the
response of muscle activity in the leg to different impact forces. To
vary the loading rate of the impact forces experienced by each subject,
the viscoelastic properties of their shoe midsoles were experimentally
manipulated. This study tested the hypothesis that muscle activity
patterns in the lower extremities change in response to the different
impact loading rates that occur at ground impact.
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METHODS |
Approach to the problem.
A pendulum apparatus was used to apply repeatable impacts to the heel.
Subjects lay supine on a bed, which was cyclically swung so that the
right heel impacted a wall-mounted force plate. The amplitude of each
swing was set so that the impact forces were similar to those
experienced during walking and running. A harness supported the leg so
that muscle activity was not required for joint movement of the lower
extremity. The frequency component of the impact force was modified by
changing the midsole properties of the subjects' shoes, with a soft
shoe giving a lower-frequency component than a hard shoe. Muscle
activity patterns in the lower extremities were measured by
electromyography (EMG) from the tibialis anterior, medial
gastrocnemius, vastus medialis, and biceps femoris muscles. The
myoelectric signals were resolved into components in intensity, time,
and frequency to determine which features of the signals changed
between the shoe interventions. The data were used to test the
hypothesis that the muscle activity required to stiffen the leg for
impact differs between the two impact conditions produced by the shoes.
Subjects.
Twenty men (76.4 ± 1.7 kg body mass, 33.3 ± 2.6 yr old) who
regularly ran or exercised participated in the study. They gave their
informed consent in accordance with the University of Calgary Medical
Bioethics policy on research using human subjects.
Pendulum apparatus.
A human pendulum apparatus was constructed (Fig.
1), adapted from that described by
Lafortune and Lake (18). The pendulum was made from a
canvas bed strapped around a polyvinyl chloride frame, and it was able
to swing freely via polyvinyl chloride rods attached to the corners.
The subject lay supine on the bed, with leg suspension straps around
the ankle and knee maintaining the right knee at a flexion angle of
20°. In this manner, the lower leg was held parallel to the ground,
and the hip flexion angle was 20°.
Subjects tested two shoe conditions. The upper, outsole, and insole
materials were identical between the shoes, but the midsole material
varied between a medium elastic (Shore C = 55) and a soft viscous
heel (Shore C = 26). The elastic shoe had a hardness similar to
that of most commercially available running shoes; the viscous shoe was
much softer and was used to reduce the loading rate of the impact
force. Subjects had size 9-10 feet, and the appropriate shoe size
was provided. The shoes had similar mass: 289.1 g for the elastic shoe
(size 9) and 287.5 g for the viscous shoe (size 9). The order in which
the shoes were tested was varied randomly between subjects. Subjects
did not perform exercise immediately before testing. Each subject
performed only one test session for both conditions.
The pendulum was pulled back to a reference stop and allowed to swing
so that the subject impacted the wall with his right heel. The impacts
were cyclically repeated with one impact per second. The reference
stops were adjusted for each subject so that the velocity at impact was
kept constant at 1.0 m/s (to the nearest 0.1 m/s as measured using 2 photocells). This impact velocity was used, because it provided an
impact force with shape and magnitude similar to those of impact forces
during overground running (17). The subjects were required
to dorsiflex the foot and resist the motion of the pendulum at impact.
A videocamera confirmed that impacts occurred only with the heel and
that the foot did not "slap" onto the wall. In order for the
subjects to adjust to each new shoe, 25-40 initial impacts were
administered before data collection. Impact forces were recorded for a
set of 30 impacts for each shoe condition using a vertically mounted
force plate (Kistler Instrumente, Winterthur, Switzerland), which was
sampled at 1,200 Hz.
Myoelectric signals were recorded from the tibialis anterior, medial
gastrocnemius, vastus medialis, and biceps femoris muscles. Bipolar
surface electrodes (Ag-AgCl) were placed on the muscle bellies after
removal of the hair and cleaning with isopropyl wipes. Each electrode
was 10 mm diameter, and the intraelectrode distance was 30 mm. A ground
electrode was placed on the medial malleolus of the ankle joint. The
EMGs were preamplified at the source and then recorded using a
Biovision (Wehrheim, Germany) system at 1,200 Hz.
Wavelet analysis of EMG.
Wavelet analysis (33) was used to resolve simultaneously
the intensities of the EMG signals in time and frequency space. The
analysis yields an intensity that closely approximates the power of the
EMG signal. The procedure to obtain the intensity from the measured EMG
signal consisted of the following three steps: 1) compute
the wavelet-transformed EMG signal using a filter bank of wavelets that
include intensity and damping factors, 2) compute the
intensity of the wavelet-transformed signal by adding its square and
the square of its time derivative divided by the center frequency, and
3) apply a Gauss filter to the wavelet-transformed signal.
In step 1, a filter bank of eight wavelets was used. Each
wavelet was well defined in time and frequency space, and its integral over time was 0. The set of wavelets was nearly biorthogonal and was
produced by nonlinear scaling. The scaling was adjusted to obtain a
physiologically acceptable time resolution at all frequencies considering the uncertainty principle. Center frequency, bandwidth, and
the time resolution are indicated in Table
1.
The convolutions between each wavelet and the EMG are called the
wavelet transform. The process is best visualized in frequency space,
where the convolution consists of multiplication of the Fourier-transformed signal by the Fourier-transformed wavelet. Such a
multiplication is equivalent to a filtering procedure. The filtered
data (convoluted signal) were submitted to an inverse Fourier transform
to obtain the wavelet transform of the signal in the time domain.
The wavelets, and thus the convoluted signal, were multiplied by a
frequency-dependent intensity factor to take care of the fact that the
amplitude of the EMG signal, as seen at one frequency point in the
Fourier-transformed EMG signal, contributed to the wavelet-transformed
signal of three adjacent wavelets. Splitting the amplitude into three
components and forming the sum of the squares does not yield the
amplitude squared, because the cross terms are missing. These missing
cross terms were included in the analysis in the following manner: the
original wavelets, and thus the convoluted signal, were divided by the
square root of the sum of the squared wavelet intensities at each
frequency in Fourier space.
In step 2, the intensity of the EMG signal was computed. The
intensity of the EMG signal was defined in time space from the wavelet-transformed signal by adding its square and the square of its
time derivative divided by the center frequency. If this operation is
applied to a pure sine wave, then it yields the amplitude squared at
any time. In the present case, the computed intensity represents an
approximation to the amplitude squared because of errors caused by the
finite sampling frequency. The approximation was further improved by
using a damping factor to compensate for these errors. The damping
factors were optimized by matching the intensity obtained by the
wavelet analysis of a pure sine wave with its amplitude squared. The
match between the intensities and the amplitude squared was >95% for
all frequencies in the range 10-250 Hz.
Step 3 consists of smoothing the intensity using a Gauss
filter. This step is required, because the approximation made by the
calculation of the intensities would otherwise contain some oscillations that are a result of interference. The width of the Gauss
filter was kept shorter than the time resolution of the wavelet (width
is 
of that of the time resolution). Thus the time resolution
was not significantly enlarged.
Definitions.
A wavelet domain was defined as the time series of intensity resolved
for one wavelet only. The frequency bands for each wavelet domain are
shown in Table 1. Intensities at frequencies <10 Hz typically contain
movement artifacts, and so data from wavelet domain 0 were
ignored. An intensity ij,k was calculated for
each sample point j and wavelet domain k. Time
tj is the time of sample j, and
wavelet wk is the wavelet number for wavelet
domain k. The global intensity (Ij)
gives the power in the myoelectric signal at each sampling point and
was defined as the sum of the intensities over wavelet domains
1-7 for each tj
The mean global intensity (Mi) over a
50-ms time window is equivalent to twice the square of the
root-mean-square (RMS) value for that window and was given by
Mean time (Mt) for the time-intensity
space is the time taken for the mean myoelectric work to be done within
the signal and gives an index of when the myoelectric activity was predominant within each 50-ms window. Mt was
calculated as
In a similar manner, the total intensity
(I'k) for a 50-ms time window
for wavelet domain k is
The mean wavelet value (Mw) for the
wavelet-intensity space is a correlate of the mean frequency of the
myoelectric signal for a 50 ms time window and is given by
Time 0 was defined as the time of impact, which was
the moment of heel strike. The subscripts "pre" and "imp" to
the variables Mt, Mw, and
Mi denote data from the preactivation (
50 
tj < 0 ms) and postimpact (0 tj < 50 ms) windows, respectively. For each
shoe condition and for each subject, the means of
Mi, Mt, and
Mw for the 30 impacts are 
i,
t, and w,
respectively. The intensity values for i
were normalized so that the maximum of
i,imp for the elastic shoe
condition had a value of 1.
Statistics.
The significance of the muscle responses was tested with analyses of
variance (ANOVAs). Separate balanced-design ANOVAs were used for each
muscle and each time window (Minitab). The dependent variables used for
these tests were Mt, Mw, and
Mi. Subject number, shoe condition, and a
"subject × shoe condition" interaction term were used as
factors in the ANOVA. Each ANOVA contained data from both shoe
conditions, all 20 subjects, and all 30 trials.
Differences in i,
t, and w were
calculated between the elastic and viscous shoe conditions for each subject. The ranges of these differences are represented by their RMS value.
Muscle fatigue during the experiment can result in a reduction in the
frequency content of the EMG (5). If the frequency content
of the EMG was systematically changed with the fatigue state, then the
order in which the interventions were presented would be related to
w. The effect of muscle fatigue and the order in
which the interventions were presented were tested in the following
way. The differences in w,pre and
w,imp between the shoe conditions were scored as +1
or 1 if the elastic condition was higher or lower than the viscous
condition, respectively. These scored values were then ranked by
whether the subject was presented with the elastic or the viscous shoe
condition first. If fatigue had a significant effect on the results,
then it would be expected that the subjects who tested the elastic shoe
condition first would have a score of 1, and if they tested the viscous shoe first, then the score would be 1. The distribution of these ordered scores was tested with a nonparametric runs test (Minitab) to
determine whether there was a significant effect of muscle fatigue on
the myoelectric response.
Values are means ± SE. The range of differences in values was
indicated by their RMS value. Significance was tested at a confidence level of 
= 0.05. Power analyses (29) showed that
in all cases the power of the ANOVA tests was >99%.
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RESULTS |
Typical force and EMG for the vastus medialis muscle that resulted
from one heel strike are shown in Fig. 2,
A and B. The square of the EMG and the global EMG
intensity are shown in Fig. 2C. The intensity became visible
75 ms before impact. In the 50-ms preactivation phase, the EMG
intensity was contained almost exclusively in the summed intensity from
wavelet domains 3 and 4 (Fig. 2E). During the 50-ms postimpact period, additional activity could be
observed in wavelet domains 1 and 2 (Fig.
2D).
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Fig. 2.
"Ground" reaction force (A), electromyogram (EMG),
and intensity traces for a single impact. B: EMG data
recorded from the vastus medialis muscle. Time 0, moment of
heel strike. C: rectified, squared signal; thick line,
global EMG intensity summed from wavelet domains 1-7.
EMG intensity was further subdivided into low-frequency (11-54
Hz), medium-frequency (46-116 Hz), and high-frequency
(104-252 Hz) bands generated from the sum of wavelet domains
1 and 2 (D), domains 3 and
4 (E), and domains 5-7
(F), respectively.
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Individual EMG responses.
The EMG intensities for the vastus medialis muscle for one subject are
shown in Fig. 3. In wavelet domains
1-4 the EMG intensity had increased beyond the background
levels by 50 ms before heel strike. In this example, the intensity was
generally the same between the two shoe conditions for the
preactivation and postimpact periods for all wavelets. The main
exception was at wavelet 2 for the 50-ms preactivation
period. Here the total intensity was 243% greater for the viscous than
for the elastic shoe condition, a difference that was significant.
There were 160 different combinations of 20 subjects, 4 muscles, and 2 shoe conditions. The ratio of the SE to the mean intensity was
calculated for the maximum intensity in the postimpact period for each
of these combinations. These ratios had a range of 19%, as shown by
their RMS value.
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Fig. 3.
EMG intensities in wavelet domains 1-7 for the
vastus medialis muscle for 1 subject. Differences in EMG intensity can
be localized to a specific time (preimpact) and frequency band (wavelet
domain 2). Time 0, moment of heel strike.
Bandwidths for each wavelet domain are given in Table 1. Thick dashed
line, mean response of 30 impacts in the elastic shoe; thick solid
line, mean response of 30 impacts in the viscous shoe; thin lines, ±1
SE at each time.
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EMG intensities were greater than background levels in the 50-ms
preactivation period for the majority of subject-muscle-shoe combinations (Fig. 4). For the tibialis
anterior muscle, such preactivation occurred for every subject. For the
medial gastrocnemius, vastus medialis, and biceps femoris muscles, this
preactivation was seen in 75% of the cases. The examples in Figs. 3
and 4 illustrate the differences in preactivation intensity that
occurred for all four muscles tested. A detailed description of
i,pre and
i,imp is shown on a
subject-by-subject basis for both shoe conditions in Fig.
5. These data showed differences in
intensity between the shoe conditions. The ratios of the mean preactivation intensities to the postimpact intensities across all
subjects were 96, 48, 44, and 73% for the tibialis anterior, medial
gastrocnemius, vastus medialis, and biceps femoris muscles, respectively. There was a decrease from w,pre to
w,imp, as shown for the biceps femoris muscle in
Fig. 6. This decrease was calculated for
139 of the 160 subject-muscle-shoe combinations tested, and
2 analysis showed this decrease to be significantly
different from random.
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Fig. 4.
EMG intensities showed changes in the preactivation period for
tibialis anterior (A), gastrocnemius medialis
(B), and biceps femoris (C) muscles for 3 subjects. See Fig. 3 legend for explanation of lines. Wavelet
domain 2 (bandwidth 25-54 Hz) is shown for A
and B, and wavelet domain 3 (bandwidth 46-82
Hz) is shown for C.
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Fig. 5.
EMG intensities for subjects 1-20 for tibialis
anterior (A), medial gastrocnemius (B), vastus
medialis (C), and rectus femoris (D) muscles. For
each subject, 4 columns are shown for each muscle in the following
order (from left to right): mean global intensity
before impact and after impact
(i,pre and
i,imp) in the elastic
condition and i,pre and
i,imp in the viscous
condition. Intensities were normalized to the maximum
i,imp in the elastic
condition for each subject.
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Fig. 6.
Mean wavelet number (w) for the
preactivation (w,pre) and postimpact
(w,imp) periods for the biceps femoris muscle. Data
points are means ± SE for each subject. There was a significant
positive regression between w,pre and
w,imp. In all cases, w,pre was
greater than w,imp.
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In some subject-muscle combinations, the timing of the EMG intensity
peaks was shifted between the shoe conditions (Fig.
7). In the examples shown, the viscous
shoe condition compared with the elastic condition had a 58% peak
intensity, which occurred 28 ms later for the medial gastrocnemius
muscle (Fig. 7A), a 124% peak intensity, which occurred 12 ms earlier for the vastus medialis muscle (Fig. 7B), and a
229% initial peak intensity with minimal time shift for the biceps
femoris muscle (Fig. 7C). In other subject-muscle combinations, the EMG intensity differed between shoe conditions, although the underlying timing remained the same. In Fig.
8, the two events occurred at 40 and 70 ms after impact. The intensity of the myoelectric signal for the
viscous shoe condition for the first event was 135% of that for the
elastic shoe but declined to 59% for the second event.
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Fig. 7.
Changes in EMG timing and intensity for wavelet
domain 2 (bandwidth 25-54 Hz) in 1 subject for the
medial gastrocnemius (A), vastus medialis (B),
and biceps femoris (C) muscles. See Fig. 3 legend for
explanation of lines.
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Fig. 8.
Changes in EMG intensity for wavelet domain 3 (bandwidth 46-82 Hz) in 1 subject for the biceps femoris muscle
between shoe conditions. See Fig. 3 legend for explanation of lines.
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Range of individual EMG responses.
Mean values of t,
i, and w differed
between the elastic and viscous shoe conditions for the preactivation and postimpact periods (Fig. 9). The
direction of the response varied for all measures of EMG activity
examined, with some subjects showing greater values for the elastic
shoe condition and others showing greater values for the viscous shoe
condition. The smallest RMS range for the differences in response
occurred in the preactivation phase for the tibialis anterior muscle,
with RMS values of 5% of w,pre for the shift in
wavelet number, 26% of i,pre for the shift in intensity, and 1.4 ms for
t,pre (Fig. 9). The largest
RMS ranges for the difference in response occurred in the postimpact
phase for the medial gastrocnemius muscle, with RMS values of 16% of
w,imp for the shift in wavelet number and 154% of
i,imp for the shift in
intensity. The largest RMS range for a change in timing between the
responses was 3.0 ms for
t,imp for the biceps femoris
muscle (Fig. 9).
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Fig. 9.
Difference in the mean intensity
(i), wavelet number
(w), and time (t) for
each subject between viscous and elastic shoe conditions.
 Show a consistency of response for the tibialis
anterior muscle in the preactivation phase;  show a
larger range of responses in the biceps femoris muscle during the
postimpact phase.
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Significance of EMG responses.
The significance of the differences between Mi,
Mw, and Mt between subjects, shoes,
and subject × shoe condition was analyzed using ANOVA,
and the results are shown in Table 2. Significant differences occurred between the subjects for
Mt, Mw, and
Mi of the EMG response, and these differences were ubiquitous for all four muscles tested. The shoe condition produced significant differences in the EMG response in half of the
tests (Table 2), and these differences occurred during the preactivation and postimpact phases of the EMG signal for
Mt, Mw, and
Mi. The magnitude of the changes in
response is shown in Table 3. Significant
effects of the subject × shoe condition interaction term were
observed for Mw for all 8 tests, and significant
differences were observed for 12 of the 16 tests of this interaction
for Mt and Mi of the EMG response. The results from this interaction term show significant differences in the way in which the subjects reacted to the two shoe
conditions for Mt, Mw, and
Mi.
Runs tests were performed for preactivation and postimpact phases for
all four muscles, and in all cases there was no significant effect of
the order in which the shoe conditions were presented to the subjects
on the change in their EMG frequency response.
Ground reaction forces.
There were significant differences in the maximum impact force
(Fmax), the time to reach this maximum (
t),
and the mean rate of force increase (Fmax/t)
to this maximum between the two shoe conditions (ANOVA for 1,200 subject-muscle-shoe trial combinations). Changing from the elastic to
the viscous shoe condition resulted in a 4.1% decrease in
Fmax, a 10% increase in t, and a 12.6% decrease in Fmax/t (Table
4).
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DISCUSSION |
An earlier experiment using a pendulum device delivered impacts to
a subject with a fully extended leg, and the impact forces were shown
to have magnitudes and loading rates that matched those reported for
running (18). In this study, the knee was held at a
flexion angle of 20° to more closely resemble the leg posture at heel
strike during walking and running. A 20° knee flexion results in a
reduction in the magnitude of the impact force. The magnitude and
loading rate of the impact force have been measured for running speeds
between 3 and 5 m/s (23), and extrapolation of these data
shows that the impact forces experienced during this present pendulum
study (Table 4) had magnitudes corresponding to a 2.5 m/s run and
loading rates corresponding to a 1.1 m/s walk. This experiment
therefore demonstrated that muscle activity patterns were different
when the loading rate of the impact force was varied and when those
impact forces were within the physiological range experienced during
walking and running. To isolate the muscle activity patterns that
occurred in response to the impact force from those required for a
complete walking or running motion, the experiments were conducted on a
pendulum with the lower extremity positions constrained. The supine
position of the subjects could affect perceptual and proprioceptive
processes that occur during impact, and this is the trade-off that must
be made to obtain the pendulum-generated impact results. The impact
forces with the elastic shoe had substantially higher loading rates
than those with the viscous shoe. The different impact forces
experienced with the two shoe conditions resulted in clear differences
in the intensity patterns resolved by this functional EMG analysis.
When a muscle is activated, it takes time before the muscle force is
fully developed. The time taken to reach maximum force depends on
factors such as the muscle fiber type, the activation level, and the
contraction dynamics but, for isometric twitches in mammalian muscle,
is in the range 23-73 ms (3, 4, 10). The intensity of
the myoelectric signal indicates the activation level within the
muscle. The force achieved by the muscle depends on the activation
level, the number and fiber type of the muscle fibers activated, the
contraction dynamics, and the history of previous contractions
(31). Fast- and slow-twitch muscle fibers have different
intrinsic contraction properties (1), and it is typically
thought that slow-twitch fibers are recruited for low-intensity
activities, with a greater proportion of fast-twitch fibers being
recruited as increasing force is required (6, 13). The
frequency components of the myoelectric signal can indicate the muscle
fiber type recruitment for any given activity, and a coefficient of
frequency is given by Mw in this study. The frequency
increases with the conduction velocity of the muscle fiber action
potentials (20), and action potentials travel faster along
larger-diameter cells (14). Fast-twitch fibers have
diameters 1.2 times those of slow-twitch fibers in the vastus lateralis (9), suggesting that they should have higher-frequency
components. In addition, fast-twitch muscle fibers can have faster (1.5 times) conduction velocities than slow-twitch fibers, even when they have similar diameters (27). Muscle fiber recruitment
strategies can thus be determined from the EMG frequency spectra, and
this has previously been demonstrated for graded muscle contractions in
the cat gastrocnemius (30). The timing, activation level, and motor unit recruitment patterns leave characteristic features within the myoelectric signal. The ability to resolve time, intensity, and frequency simultaneously gives insight into how the muscles are
used to accomplish a given task. Differences in the timing of
activation can be seen in Figs. 4, 7, and 8; differences in the
intensity of activation can be seen in Figs. 4, 5, 7, and 8;
differences in the frequency of the myoelectric signal being confined
to distinct wavelet domains can be seen in Figs. 2 and 3.
In this study, the myoelectric signals were resolved by wavelet
analysis into their intensities in time and frequency space. The
intensity represents the power within the EMG for any given time and
frequency band. EMG power spectra, which are traditionally used to
assess EMG frequency content, are calculated from the square of the
Fourier-transformed EMG signal (21) and are thus also a
measure of the power within the signal. Mw used in this study is comparable to the mean power frequency used to measure EMG
contractions (32). By contrast, RMS analysis computes the amplitude, and not a power, of the signal as a function of time. The
square of the RMS value is comparable to half the intensity from this
wavelet analysis.
The wavelet analysis of EMG signals resulted in a variety of intensity
peaks and leads to the concept of events in muscle activation. Events
can be resolved over a set of wavelets and thus have their own wavelet
spectrum. Events, which are separated by more than the time resolution
of the wavelet, represent distinct events within the muscle activation.
The example in Fig. 2, D and E, shows a muscle
activation event that was localized to wavelet domains 3 and
4 and to the preactivation period. Differences in the EMG
signal were thus characterized by the specific frequency band and the
specific time at which they occurred. Differences that occurred in the
time and the frequency content of the myoelectric signals were
statistically significant (Table 2), thus justifying the use of the
wavelet techniques to decompose the EMG into its components in time and
frequency space. Such separation of the myoelectric signal into time
and frequency components would not be possible using traditional
analysis techniques such as RMS or Fourier transforms. Time-frequency
decomposition is possible using windowed Fourier techniques; however,
this approach is not without its difficulties (15). The
time resolutions for each wavelet were chosen so that they covered
physiologically relevant durations on the order of 20-40 ms. The
higher-frequency wavelets progressively resolved shorter times (Table
1); however, this resulted in broader frequency bands for these
wavelets because of the uncertainty principle governing signal
processing (15).
Differences in the intensity patterns that resulted from the different
loading rates were limited to a narrow range of wavelet domains (Figs.
2 and 3). The differences thus occurred at specific frequency bands. A
net decrease in the frequency content of the EMG may occur because of
recruitment of a greater proportion of slow- than fast-twitch fibers.
However, a decrease in the frequency content may also be caused by the
decrease in action potential conduction velocity that occurs during
fatigue because of a lowering of the interstitial pH (2)
and is probably due to an accumulation of metabolic by-products such as
lactate within the muscle (22). Thus muscle fatigue may
also appear as a shift in the EMG frequency spectrum (11).
However, we have shown that fatigue was not a significant factor in
these results. Therefore, it can be speculated that the change in
myoelectric frequency patterns that occurs with different loading rates
of the impact force is due to different patterns of muscle fiber type recruitment.
The preactivation period occurred before heel strike, and muscle
activity within this period was the result of feedforward control
mechanisms. Thus the different muscle preactivation states that
occurred between the impact conditions (Table 2) must have been the
consequence of the different muscle tuning requirements that were
anticipated for each heel strike. The postimpact period began at heel
strike and would initially be the result of feedforward mechanisms.
However, toward the end of the 50-ms postimpact period, there may
additionally have been some stretch reflex responses. Short-latency
reflex responses have been shown to occur 41 ms after stretch in the
triceps surae muscles (24) and may occur with even shorter
latencies as low as 34 and 39 ms in the biceps femoris and rectus
femoris, respectively (28). The majority of the postimpact
period occurred before the muscle activity could have been influenced
by stretch reflexes. Indeed, differences in postimpact muscle activity
before the influence of stretch reflexes can be clearly seen in Figs.
2-4. Toward the end of the postimpact period, however, differences
in short-latency monosynaptic reflexes may have additionally
contributed to the different muscle activity patterns.
The intensity patterns were complex in time and wavelet space. The
results could be more easily submitted to statistical analysis by
reducing the intensity patterns to coefficients describing the mean
time, wavelet number, and intensity for the 50-ms time windows before
and after impact. Such mean coefficients have smaller magnitudes than
the peak values in the intensity patterns. Nonetheless, the RMS values
for the differences of the mean responses for all the subjects show
that the response of the body to the different shoe conditions can be
of substantial magnitude. A range of responses occurred across the
individuals tested, with some subjects showing an increase in
t, w, or
i between the impact conditions, whereas
other subjects showed decreases in these parameters (Figs. 5 and 9).
Furthermore, testing between elastic and viscous midsole conditions
resulted in large differences in t,
w, or i for some
subjects, whereas little change was observed in others. The way in
which the muscle activation patterns respond to different loading rates
of the impact force is thus subject specific. Patterns of
i can be seen in Fig. 5; for instance,
subjects 3 and 20 showed a negligible intensity
during the preactivation period for the medial gastrocnemius, vastus
medialis, and rectus femoris muscles, while subjects 1, 8, 9, 13, 16, 18, and 19 showed some periods of preactivation
that had substantially greater intensity than the following postimpact
period. It would seem as if the individual responses to these impacts
can be grouped into distinct strategies, although the functional
significance of the strategies is not yet understood.
Some locomotor studies are challenged by the fact that muscles are used
to move the limbs (to change the joint angles) and, additionally, to
provide the joint stiffness required for the locomotor task. In such
cases, it can be difficult to segregate these two tasks from the EMG
signals. In this study, the leg motion was minimized, with the lower
leg being supported in a harness. By eliminating the muscle action
required to move the legs, the EMG activity recorded was mainly that
necessary for minimizing soft tissue vibrations and for generating the
joint stiffness required to withstand the "ground" impact.
Significantly different myoelectric patterns were seen in response to
the different shoe midsole conditions and, thus, the loading rate of
the impact force. These changes were present in the timing, intensity,
and frequency content of the EMG and occurred in the period 50 ms
before impact and the period 50 ms after heel strike. If "tuning"
is considered to be the alteration of the mechanical properties of the
leg due to changes in muscle activity, irrespective of any motion that occurs in the joints, then the results from this study have shown that
the leg is tuned differently in response to the changes in the loading
rate of the impact force.
| |
ACKNOWLEDGEMENTS |
We thank Darren Stefanyshyn for help during the study.
| |
FOOTNOTES |
Financial support was provided by Adidas International, the Alberta
Heritage Foundation for Medical Research, and the Swiss National Foundation.
Address for reprint requests and other correspondence: J. M. Wakeling, Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada T2N 1N4 (E-mail:
wakeling{at}kin.ucalgary.ca).
The costs of publication of this
article were defrayed in part by the
payment of page charges. The article
must therefore be hereby marked
"advertisement"
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 31 December 2000; accepted in final form 10 May 2001.
| |
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