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First published online January 30, 2009
Journal of Experimental Biology 212, 523-534 (2009)
Published by The Company of Biologists 2009
doi: 10.1242/jeb.024927
Control and function of arm swing in human walking and running
1 Department of Anthropology, Washington University, 119 McMillan Hall, Saint
Louis, MO 63130, USA
2 Department of Anthropology, University of Arizona, 1009 E. South Campus Drive,
PO Box 210030, Tucson, AZ 85721, USA
3 Department of Anthropology, Harvard University, 11 Divinity Avenue, Cambridge,
MA 02138, USA
* Author for correspondence (e-mail: hpontzer{at}artsci.wustl.edu)
Accepted 19 November 2008
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Summary |
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 TOP Summary INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Key words: arm swing, walking, running, passive dynamics, tuned mass dampers
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INTRODUCTION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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;
Fernandez Ballesteros et al.,
1965; Jackson et al.,
1978; Hinrichs,
1987; Ohsato,
1993; Webb et al.,
1994; Gutnik et al.,
2005), the extent to which the shoulder muscles actively drive the
arms, and the effect of arm swing on stability and economy during walking and
running are poorly understood. In this paper, we examined the control of arm
swing during walking and running, and investigated the effect of restricting
arm swing on stability and metabolic cost.
In a seminal study examining the movements of the torso and arms during
walking, Elftman suggested that the arms did not move as simple pendulums, but
instead were driven by muscle activation in the shoulder
(Elftman, 1939). Fernandez
Ballesteros and colleagues expanded upon this work, using indwelling
electrodes to measure muscle activity in the anterior, intermediate and
posterior deltoid during walking, and confirmed that arm movement was
accompanied by activity of the deltoid muscle, particularly during retraction
(Fernandez Ballesteros et al.,
1965). Retraction of the shoulder was associated with firing of
the posterior deltoid and, to a lesser extent, protraction of the shoulder was
associated with anterior deltoid activity
(Fernandez Ballesteros et al.,
1965). Further, Fernandez Ballesteros and colleagues showed that
the shoulder muscles fire even when the arm is restrained during walking
(Fernandez Ballesteros et al.,
1965), suggesting that the neural control of arm swing may be
controlled by a locomotor pattern generator, and is perhaps an evolutionary
hold-over from a quadrupedal past, a view supported by other workers (e.g.
Gray, 1944;
Jackson et al., 1978).
Functionally, arm swing is often considered to be a mechanism for
counteracting free vertical moments (i.e. torque about the body's vertical
axis) imparted by the swinging legs. Elftman first proposed this mechanism for
walking, showing that the angular acceleration of the arms was equal to that
of the torso but in the opposing direction
(Elftman, 1939). Hinrichs
provided similar evidence for running, showing that the horizontal angular
momentum of the upper and lower body were of equal magnitude and in opposing
directions, resulting in a net angular momentum near zero for the entire body
(Hinrichs, 1987;
Hinrichs, 1990). More
recently, Herr and Popovic (Herr and
Popovic, 2008) showed that net angular momentum in all axes is
kept remarkably close to zero during walking, and provided further evidence
that arm moments serve to cancel lower limb moments about the body's vertical
axis [figure 5C in Herr and
Popovic (Herr and Popovic,
2008)]. These results are consistent with those of Li and
colleagues, which showed that the free vertical moments produced by the stance
limb during walking are higher when the arms are restrained from swinging
(Li et al., 2001). Presumably,
these greater vertical moments result from the absence of counteracting arm
swing. It has also been suggested that restricting arm swing affects the
metabolic cost of locomotion. Anderson and Pandy
(Anderson and Pandy, 2001), in
comparing their forward dynamics simulation of human walking with experimental
data from human subjects, suggested that the high cost of walking observed in
their simulation resulted from the lack of arm swing in their model.
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), these studies suggest that arm swing is largely driven by
muscle activity in the shoulder, and serves an important role in maintaining
stability during walking and especially during running. However, an
alternative hypothesis is that arm swing is largely a passive response to the
forces exerted on the torso by the swinging of the legs. According to this
model, horizontal torques imparted on the pelvis by the swinging legs are
transferred up the spinal column to the shoulder girdle, and then to the arms.
Tonic or stabilizing muscle activity in the trunk and shoulder, along with
ligamentous and other connective tissues, cause these elements to act as
elastic elements or springs, an idea proposed by Fernandez Ballesteros and
colleagues (Fernandez Ballesteros et al.,
1965); the forces exerted by these anatomical `springs' will
increase in proportion to their angular displacement, or torsion. The
direction of the torque transmitted through the trunk will alternate
(clockwise/anti-clockwise) with each step as the legs swing in turn. The
inertia of the torso and arms will tend to resist these forces, causing a time
lag between movement in the pelvis and movement in the shoulder girdle. As a
result, the shoulder girdle and arms will have the same oscillating frequency
as the legs and pelvis, but will rotate out of phase with the legs.
Viewing arm swing as a passive, emergent property of human walking and
running fits well with recent work demonstrating the self-stabilizing,
`passive-dynamic' nature of lower limb movement during walking
(Collins et al., 2005). In
fact, even simple physical models can develop human-like arm swing in response
to leg swing (see supplementary material Movie 1). A passive model for arm
swing would also have the advantage of being self-tuned, with greater leg
accelerations leading to greater arm accelerations. Importantly, the effect of
arm swing predicted by a passive model is similar to that suggested by active
models, with the arms acting as mass dampers (see below), and angular
acceleration of the upper body canceling horizontal angular accelerations by
the swinging legs and maintaining whole-body net angular acceleration near
zero. Lieberman and colleagues (Lieberman
et al., 2007; Lieberman et
al., 2008) have recently suggested that the arms act as mass
dampers to minimize head pitch in the sagittal plane.
Here, we examined the control and function of arm swing in human walking and running. First, we tested the hypothesis that the arms act as mass dampers that decrease the amplitude of upper body rotation about the vertical axis. We then investigated the control of arm swing, testing predictions of the passive arm swing hypothesis against those of an active arm swing hypothesis, in which arm swing is driven by the shoulder muscles. We measured muscle activity, kinematics and oxygen consumption during walking and running in a sample of humans. The moment of inertia of the arms was decreased by asking subjects to run with arms folded across their chest, or increased by adding weights at the elbow. We expected arm swing in humans to behave as a mass-damped system, with changes in the moment of inertia of the arms leading to predictable changes in upper body rotation. Further, we predicted that the arms would act as passive mass dampers, with the energy for arm swing ultimately derived from movement of the lower body, and the trunk and shoulders acting as damped spring elements. Finally, to examine the effect of normal arm swing in maintaining stability, we examined the effect of restraining the arms on locomotor kinematics, footfall variability and the energetic cost of walking and running.
Modeling arms as mass dampers
In mechanical systems exposed to vibration or other external forces,
several approaches can be used to decrease the amplitude of displacement of
the principle mass (see Soong and Dargush,
1997). Systems for decreasing the amplitude of movement are
generally termed energy dissipation systems, or dampers, and can be classed as
passive or active. Passive dampers are those which impart no energy into the
system, instead using the energy of the system to decrease movement of the
principle mass (Symans and Constantinou,
1999). For example, frictional dampers convert energy in the
system to heat, reducing energy and movement in the principle mass (see
supplementary material Movie 1) (Soong and
Dargush, 1997). Tuned mass dampers
(Soong and Dargush, 1997)
decrease movement of the principle mass by attaching an auxiliary mass using a
damped spring (Fig. 1A). The
effectiveness of passive tuned mass dampers is a complex function of the
stiffness and damping constants of the damped spring by which they are
attached but, generally, effectiveness is increased (i.e. movement of the
principle mass is minimized) when the auxiliary mass is increased, and when
the natural frequency of the auxiliary mass is below that of the principle
mass (Soong and Dargush,
1997).
|
). Notably, both active and passive mass dampers can be
effective over a range of oscillation frequencies
(Soong and Dargush, 1997;
Symans and Constantinou,
1999).
In the body, the torso is the principle mass whose angular displacement
must be controlled. The hypothesis that the arms act as mass dampers for the
torso thus leads to three predictions. First, since the effectiveness of mass
dampers generally increases with their mass
(Soong and Dargush, 1997),
decreasing the moment of inertia of the arms (the auxiliary mass) about the
vertical (z) axis (Fig.
2) as in the no arms condition is expected to result in greater
rotation of the torso (the principle mass). Conversely, increasing the moment
of inertia of the arms in the arm weights condition is expected to decrease
torso rotation. A second, related prediction is that these changes in torso
rotation should result in similar changes in head yaw, since the head is
modeled as a mass attached to the torso via a damped spring
(Fig. 1). Third, changes in the
moment of inertia of the upper body (i.e. in the no arms or arm weights
conditions) are predicted to have measurable effects on the phase differences
in the movement of the pelvis and shoulder girdle. Increasing the moment of
inertia of the arms, and therefore the upper body moment of inertia, is
expected to lengthen the lag between pelvic and shoulder rotation, while
decreasing the upper body moment of inertia is expected to shorten the lag
between pelvic and shoulder movement.
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; Symans and
Constantinou, 1999), in which all energy in the system derives
from the swinging legs, and the spinal column and shoulders act as damped
springs (Fig. 1B). This
hypothesis leads to the following predictions. First, angular acceleration of
the upper torso is predicted to increase with angular displacement between the
pelvis and shoulder girdle (Fig.
1B). That is, as torsion of the spinal column increases, the force
exerted by this spring-like element will increase, resulting in greater
acceleration of the shoulder girdle. Second, the angular acceleration of the
arm is predicted to increase with angular displacement at the shoulder (i.e.
the angle of the upper arm segment relative to vertical), just as the force
generated by a spring increases with its displacement
(Fig. 1B). In this way,
rotation of the shoulders in the transverse plane is expected to result in arm
swing in the sagittal plane. For example, as the shoulder girdle rotates and
the right shoulder translates anteriorly, the arm will tend to remain in place
following Newton's first law. Thus, the right arm will appear to swing
posteriorly relative to the right shoulder, until the angular displacement of
the shoulder is sufficient to swing the arm forward (protraction). As the
right arm swings forward, the right shoulder will begin to translate
posteriorly as the torso rotates with the next step, resulting again in
angular displacement of the shoulder and acceleration of the arm. The shoulder
musculature is expected to act as a spring-like element, translating angular
displacement into torque. Thus a third prediction of the passive model is that
the anterior and posterior portions of the deltoid will fire together, acting
to stabilize the shoulder.
Active arm swing model
The active arm swing hypothesis proposes that arm swing is an active mass
damping mechanism in which the arms (an auxiliary mass) are driven by the
shoulder muscles acting as controllers in order to decrease the amplitude of
torso rotation (Fig. 1B). Since
the arm and torso are attached at the shoulder, anterior acceleration of the
arm in the sagittal plane will lead to posterior acceleration of the shoulder
and torso following Newton's third law: protraction of the right arm will tend
to accelerate the right shoulder posteriorly, while retraction of the left arm
will force the left shoulder anteriorly, thereby translating sagittal plane
accelerations of the arms into transverse plane angular accelerations of the
shoulders and torso. Thus the primary prediction of the active arm swing
hypothesis is that increased anterior angular acceleration of the arm in the
sagittal plane will result in increased posterior angular acceleration of the
shoulder girdle in the transverse plane. Second, anterior and posterior
deltoid fibers are expected to fire alternately, acting as agonists driving
angular acceleration of the arm at the shoulder. Third, angular accelerations
of the pelvis and shoulder girdle are predicted to be similar in magnitude but
opposite in direction, as the upper body is driven to counteract vertical free
moments produced by the swinging legs.
Stability and cost
To examine the function of arm swing in maintaining stability, we tested
the effect of removing arm swing (no arms condition) on footfall variability
and metabolic cost. If arm swing is critical for maintaining stability, then
removing arm swing as in the no arms condition is expected create stability
problems during walking and especially running, resulting in greater
variability in footfall placement (Fig.
1B). Similarly, while the relationship between muscular work and
metabolic cost is complex (Cavagna and
Kaneko, 1977; Willems et al.,
1995), if the muscular work is done to compensate for decreased
stability in the no arms condition, the metabolic cost of locomotion in the no
arms condition is expected to be greater relative to control walking and
running (see Anderson and Pandy,
2001). In contrast, if the upper body acts as a passive
mass-damped system, then stability and cost should remain unchanged in the no
arms condition, with the energy imparted by the swinging legs dissipated
through greater excursion of the pelvis, torso and head.
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MATERIALS AND METHODS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Kinematics and muscle activity
Small (1 cm diameter) spherical reflective markers were adhered to the body
using double-sided tape, and the position of these markers was tracked using
an infrared camera system (Vicon®; Centennial, CO, USA) recording at 200
frames s–1. Markers were placed on the following landmarks
and locations: forehead (two markers), right and left acromia, right elbow,
right wrist, right and left anterior superior iliac spines, right greater
trochanter, right knee, right ankle (lateral malleolus), right and left heels,
and right and left first toe (Fig.
1). All markers were adhered directly to the skin, except those
for the toe and heel, which were adhered to the subjects' shoes.
Anterior and posterior deltoid activity was recorded using self-adhering
surface electrodes (Ambu® Blue Sensor, Glen Burnie, MD, USA) and an
electromyography (EMG) system (RunTech® Myopac Jr, Mission Viejo, CA,
USA). Subjects wore a light (320 g) amplifier unit that transmitted
conditioned EMG signals along a fiber optic cable to a receiver. Analog
signals were then passed through the Vicon MX Control A/D board and recorded
at 4000 Hz in Vicon Nexus software, simultaneously with the kinematic data.
Electrode placement was determined by palpation and confirmed by having the
subject flex anterior and posterior portions of the deltoid individually
against resistance while the EMG signal was observed. Although other muscles
may also serve as shoulder flexors and extensors (e.g. triceps, biceps,
latissimus dorsi), we focused on the deltoids here, since they have been shown
to be important in this role during walking
(Fernandez Ballesteros et al.,
1965). Additionally, other shoulder flexors serve multiple roles,
such as elbow flexion and extension or arm rotation, making their action
difficult to characterize.
After being fitted with the EMG sensors and reflective markers, subjects performed an arm pump trial, in which they stood in place and swung their arms back and forth as during normal running. Next, after warming up on the treadmill (Sole Fitness F85, Jonesboro, AR, USA), subjects performed a series of walking and running treadmill trials for a range of speeds and experimental conditions. In the control condition, subjects walked normally at three speeds (1.0, 1.5 and 2.0 m s–1) and ran normally at three speeds (2.0, 2.5 and 3.0 m s–1). In the arm weight condition, these walking and running speeds were repeated, while the subject wore a 1.2 kg `ankle-weight' style weight on each arm, just proximal to the elbow. Finally, in the no arms condition, walking and running speeds were repeated again, with the subject instructed to keep their arms folded tightly across their chest. Note that the moment of inertia of the arms and upper body is increased in the arm weights condition, and decreased in the no arms condition, but the magnitude of change is likely to be different between conditions and among subjects.
Data analysis
Mean contact time (i.e. step duration), stride period and stride frequency
were determined from five strides for each kinematic trial. Contact time was
measured as the time between heel strike (the first kinematic frame showing
heel–ground contact) and toe-off (the last kinematic frame showing
foot–ground contact). Stride period was measured as the time between two
consecutive right heel strikes.
Marker position data were filtered in Matlab® (MathWorks Inc., Natick,
MA, USA) using a fourth-order, zero-lag Butterworth filter with a low-pass
cut-off set at 10 Hz. Filtered data were then used to calculate angle, angular
velocity (deg.s–1) and angular acceleration (deg.
s–2) for different body segments. Angular displacements for
the head, shoulder girdle and pelvis were calculated in the transverse plane
using the two forehead markers, right and left acromia markers, and right and
left anterior superior iliac spine markers, respectively
(Fig. 1). For the right arm,
the locations of the acromium, elbow and wrist markers were used to determine
the location of the whole arm center of mass relative to the shoulder marker
following Winter (Winter,
2005). This point mass was then used to determine the angular
displacement of the arm relative to the shoulder joint in the sagittal
plane.
EMG signals were band-pass filtered in Matlab® using a fourth-order,
zero-lag Butterworth filter with cut-offs at 60 and 300 Hz. Filtered signals
were then processed using Thexton's randomization method
(Thexton, 1996). The signal
was recitified and binned following Winter
(Winter, 2005) using a 0.01 s
reset integral. Thextonization requires a threshold, set at 1% of the maximum
integrated signal. The number of times the signal rose above this threshold
(`runs') was determined for each 8 s trial. The threshold was then raised by
0.5% of the maximum integrated signal and the number of runs was found. This
process was repeated, each time raising the threshold by 0.5% of the maximum
integrated signal, until the threshold was equal to the maximum magnitude. The
signal was then randomized, and the threshold method was repeated on the
randomized signal. The number of runs in the randomized signal was subtracted
from the number of runs in the original signal, and the maximum difference was
set as the threshold for the lowest muscle activity. All values below this
threshold (e.g. values lower than random muscle activity) were removed from
the original signal.
Metabolic cost of locomotion
After the kinematic trials described above, a subset (N=6, four
male, two female, 70.2±15.9 kg) of subjects performed a set of
metabolic trials in order to determine the effect of arm restraint on
locomotor cost. For these trials, oxygen consumption was measured using the
`open-flow' method described previously
(Fedak et al., 1981;
Pontzer, 2007). Subjects wore
a light mask through which air was pulled at 250 l min–1.
This air was sub-sampled continuously, scrubbed of water vapor and carbon
dioxide, and analyzed for oxygen concentration using a paramagnetic analyzer
(Sable Systems®, Las Vegas, NV, USA). Oxygen concentration was monitored
in near-real time and recorded at 30 Hz in Vicon Nexus software. Oxygen
concentration was then used to calculate the rate of oxygen consumption (ml
O2 s–1) following Fedak et al.
(Fedak et al., 1981); the
system was calibrated daily and checked for leaks using a known flow rate of
pure nitrogen.
The resting rate of oxygen consumption was first measured with the subjects standing on the treadmill. Next, the subjects performed two 1.5 m s–1 walking trials, and two 3.0 m s–1 running trials. In one walking trial and one running trial the subjects walked or ran normally, as in the control condition; in the other walking and running trial, they walked or ran with arms folded tightly across their chest as in the no arms condition. The order of conditions was varied, so that half of the subjects performed the control trials first, and half performed the no arms condition first. Each metabolic trial lasted at least 6 min, and mean oxygen concentration from the final 2 min of each trial was used to calculate the rate of oxygen consumption. Only trials in which oxygen consumption visibly reached a plateau (less than 10% change over the final 2 min) were used for analysis. For each subject, the resting rate of oxygen consumption was subtracted from the rate of consumption while walking or running in order to calculate a net cost of locomotion. This net cost was then divided by body mass and then by speed to give the mass-specific cost of transport (ml O2 kg–1 m–1) for each speed in each condition.
Hypothesis testing
Filtered kinematic and thextonized EMG data were used to examine predicted
relationships. Segment velocities and accelerations were calculated using the
finite differences method described in Winter
(Winter, 2005). Predictions
were considered to be supported if the correlation between two kinematic
variables (e.g. shoulder displacement and arm acceleration) had a Pearson's
R greater than 0.5 or less than –0.5, and in the predicted
direction, following Cohen's index for a `large' effect size
(Cohen, 1992). This effect size
(R=±0.5) recognizes the complexity of the multi-segment,
multi-muscle system being analyzed, and anticipates variability within the
system and between subjects. It should be noted that the conventional
criterion for statistical significance, a P-value of <0.05 or
<0.01, is inappropriate in determining the biological or biomechanical
significance of these segment correlations due to the large number of data
points generated by high-speed kinematic data. With a capture rate of 200
frames s–1, three strides generate approximately 600 data
points, because each frame produces a position, velocity and acceleration
estimate for a given segment. With a sample of 600 points, even small
correlations of R=±0.1 become significant at
P<0.01; however, such small correlations indicate that only 1% of
the variance in the dependent variable is explained by the independent
variable. Therefore the criterion for a `large' effect size
(R=±0.5) (Cohen,
1992), while admittedly arbitrary, is preferable to a calculated
P-value for these correlations.
To determine the effect of the no arms condition on locomotor cost, we compared the net mass-specific cost of locomotion in the control and no arms condition during walking and running for each subject using Student's one-tailed, paired t-test. Similarly, we used Student's one-tailed, paired t-test to compare mean contact times, stride frequencies, head yaw amplitude and footfall variability for each subject walking at 1.5 m s–1 and running at 3.0 m s–1 in each condition. Note that using a one-tailed test was deemed appropriate here, since the direction of the predicted difference is known a priori. We discuss the effect of using a one-tailed test below.
Lag time between shoulder and pelvis rotation and footfall variability were also compared between conditions using Student's paired t-tests. As pelvis and shoulder rotation occur with similar frequency but with different times of peak amplitude, we calculated the phase difference between pelvis and shoulder movement in order to determine the effect of increasing or decreasing the moment of inertia of the upper body. The phase difference between peak pelvis rotation (tpelvis) and peak shoulder rotation (tshoulder) was calculated as phase difference=360 deg.x(|tpelvis–tshoulder|/stride period). The closest shoulder and pelvis peaks were compared, so that the maximum phase difference was 180 deg. To test for differences in footfall variability, the medio-lateral position of the heel at heel strike was recorded for eight consecutive steps at each speed (Fig. 1). The medio-lateral distance between successive steps, hereafter termed step width, was measured, and the coefficient of variation (a size-corrected measure of variance) was determined for each subject at each speed. Coefficients of variation (c.v.) were then compared using Student's paired t-test.
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RESULTS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Lag time between pelvis and shoulder rotation increased with greater moment of inertia of the upper body as predicted (Fig. 3C), with the greatest phase differences between pelvis and shoulder rotation during arm weights trials for both walking (157.6±22.1 deg.) and running (74.1±35.8 deg.). Phase differences in control trials were slightly lower (walking 149.2±41.5 deg., running 93.9±60.9 deg.) but these differences were not significant (walking P=0.10, running P=0.46). Phase differences were smallest in the no arms trials (walking 93.9±60.9 deg., running 33.0±23.3 deg.), significantly smaller than control trials for both gaits (walking P=0.01, running P<0.01), and significantly smaller than arm weights trials during running (running P<0.01, walking comparison approach significance at P=0.055). For each condition, phase differences were significantly greater during walking (P<0.01, walking versus running trials, all comparisons).
Passive arm swing predictions were strongly supported by kinematic results. During walking and running, angular acceleration of the shoulders in the transverse plane was consistently, positively correlated with torsion of the spinal column, measured as the difference in angle between the shoulder and pelvis in the transverse plane (mean Pearson's R=0.59; Table 1; Fig. 4). Similarly, for both walking and running trials, angular acceleration of the arm in the sagittal plane was strongly correlated with angular displacement of the shoulder, with greater retraction associated with greater anterior acceleration (mean Pearson's R=0.59; Table 1; Fig. 4). These results are consistent with the passive arm swing prediction that the spinal column and shoulder effectively act as springs, with greater displacement leading to greater acceleration.
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Active arm swing predictions were generally not supported by kinematic analyses. Angular accelerations of the pelvis and shoulder were not correlated (mean Pearson's R=0.00; Table 1; Fig. 4). Further, while arm acceleration was weakly correlated with the angular acceleration of the shoulder (mean Pearson's R=0.27; Table 1; Fig. 4), the positive direction of correlation was opposite to that of the active arm swing hypothesis, which predicts that anterior acceleration of the arm will lead to posterior acceleration of the ipsilateral shoulder.
Comparing walking and running (Table 1), it is evident that Pearson's correlations between shoulder acceleration and both arm acceleration and spinal torsion are greater during running. The significance of this change and the underlying mechanism are unclear. In both cases, the greater ground forces encountered during running may lead to greater stabilizing muscle activity, and therefore a stronger linkage (i.e. a stiffer `spring') between the pelvis, shoulder and arm. Stiffer `springs' may also be necessitated by the greater stride frequencies used in running, since stiffer springs would increase the natural frequencies for the body segments involved. For example, a stiffer `spring' in the shoulder will increase the natural frequency of the swinging arm. Finally, the greater angular excursions seen in running (Fig. 3A,B) may lead to a stronger correlation of movement between segments.
Muscle activity
Patterns of muscle firing were generally consistent with predictions of the
passive arm swing hypothesis, although some alternating activity in the
anterior and posterior deltoid was observed. When compared with the clear
alternating pattern of anterior and posterior deltoid activity seen in the arm
pump trials (Fig. 5A), firing
of these muscles during both walking and running was largely simultaneous.
This suggests that the deltoid is acting to stabilize the shoulder as
predicted by the passive arm swing hypothesis, rather than to drive it
anteriorly or posteriorly as predicted by the active arm swing hypothesis.
However, some alternating activity was observed, particularly in walking
trials (Fig. 5B), indicating
that the deltoid does drive arm swing at least occasionally for some
individuals. During running, firing of the anterior and posterior portions of
the deltoid was almost exclusively co-contraction
(Fig. 5C).
Overlaying the angular velocity and acceleration of the shoulder in the sagittal plane on EMG activity (Fig. 6), it appears that many, perhaps most, of the deltoid contractions are eccentric, with the anterior deltoid firing while the arm moves posteriorly, and the posterior deltoid firing while the arm moves anteriorly. These eccentric contractions are consistent with the view of the shoulders as spring-like linkages. Further, while contraction of the anterior or posterior deltoid is typically associated with predictable accelerations at the shoulder, there are also periods in which arm acceleration and deltoid activity are in opposition, with anterior acceleration of the arm associated with posterior deltoid activity (Fig. 6A), even when the lag time between activation and force production are considered. Similarly, periods of arm acceleration are also seen when the deltoid muscles are quiet (Fig. 6B). These patterns suggest that forces, in addition to those from the deltoid, are acting on the arm. These results are consistent with the mass damping hypothesis, in which forces acting on the arms are primarily derived from the legs via the trunk.
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Footfall variation and metabolic cost
During walking at 1.5 m s–1, variation in step width
during no arms trials (mean c.v. 0.053±0.026) was greater than for
control trials (0.044±0.021) although this difference was only
marginally significant (P=0.039). There was no difference between
control and arm weights (0.056±0.013) conditions, or between no arms
and arm weights conditions (P>0.10 both comparisons;
Fig. 7A). During running at 3.0
m s–1, there were no differences between no arms
(0.059±0.020), control (0.053±0.018) and arm weights trials
(0.048±0.017; Fig.
7A).
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Restricting arm swing in the no arms condition had no effect on the mass-specific energetic cost of transport (ml O2 kg–1 m–1). Locomotor costs during no arms trials (walking 0.13±0.03 ml O2 kg–1 m–1, running 0.21±0.04 ml O2 kg–1 m–1) and control trials (walking 0.12±0.02 ml O2 kg–1 m–1, running 0.21±0.04 ml O2 kg–1 m–1) were similar (walking P=0.10, running P=0.14; Fig. 7B).
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DISCUSSION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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).
While this relationship was not observed for walking
(Fig. 1A), this does not mean
that a mass-damper view of the arms should be rejected; the effectiveness of a
mass damper and the effect of changing its inertial properties depend upon the
magnitude and frequency of the external forces acting on the system
(Soong and Dargush, 1997). The
magnitude and frequency of forces from the lower body may simply be too low
during walking to elicit a significant change in torso movement with the
manipulations of arm inertial properties used here. The magnitude of head yaw
was less than that of the shoulders, but changes in head yaw across
experimental conditions generally followed the pattern of shoulder movement,
supporting the view of the head as a mass attached via a damped
spring. Finally, phase lag between the lower body and upper body decreased
when the moment of inertia of the arms was decreased during both walking and
running (Fig. 1C), as predicted
for a mass-damped system. The view of the arms as mass dampers is consistent
with previous work (e.g. Hinrichs,
1987; Hinrichs,
1990; Li et al.,
2001; Herr and Popovic,
2008) indicating that angular acceleration in the upper and lower
body tend to cancel, resulting in near-zero net moments about the vertical
axis. However, while the results of this study fit predictions of a
mass-damper model, the tests here are certainly not exhaustive, and future
work might test other predictions of a mass-damper hypothesis in order to
determine whether this model alone is sufficient for explaining upper body
movement, particularly during walking.
Passive versus active arm swing
The passive arm swing hypothesis proposes that upper body movement is
driven by movement in the legs and pelvis, with force transferred to the
shoulders and arms via spring-like elements (ligaments and muscles)
in the spine and shoulder. This differs from an active arm swing hypothesis,
which proposes that upper body movement is driven primarily by swinging the
arms using the shoulder muscles. As predicted by the passive arm swing
hypothesis, angular acceleration of the shoulders was correlated with
increased trunk torsion, and arm acceleration was strongly correlated with
angular displacement of the shoulder (Fig.
4). In contrast, angular acceleration of the shoulders and pelvis
were not inversely correlated, nor was shoulder acceleration inversely
correlated with arm acceleration, as predicted by the active arm swing
hypothesis (Fig. 4). EMG
recordings of the anterior and posterior deltoid suggest that, while these
muscles may play a limited role in driving arm swing, they act primarily to
stabilize the shoulder through co-contraction or eccentric contractions (Figs
5 and
6). Taken together, the
kinematic and EMG results support the passive arm swing hypothesis.
Additional support for the passive arm swing model comes from the metabolic comparisons of control and no arms conditions. As noted above, upper body movement during running increases in the no arms condition by approximately 50% compared with control trials (Fig. 1A). If upper body movement is actively driven by trunk and arm musculature as in the active arm swing model, the larger displacements of the torso should require a corresponding increase in oxygen consumption. Instead, energy use is similar to that in the control condition, indicating that greater movement of the torso in the no arms trials results from the decreased inertia of the upper body, not an increase in muscle activity.
Further tests of the passive mass damping model
While our results support the hypothesis that the upper body behaves as a
passive system, limitations in our methods must be considered. Perhaps most
critically, our analysis of muscle activity is limited to surface EMG of the
deltoids, and further data are needed to determine whether muscles and other
connective tissues in the back and shoulder performed mechanical work or acted
as springs. Our analyses suggest these linkages behave like springs, but the
possibility that muscles are performing work while mimicking purely elastic
behavior cannot be ruled out using our methods; such `pseudo-elastic' muscle
activity has been suggested before for the leg muscles during terrestrial
locomotion (Ruina et al.,
2005). Even if the muscular linkages involved do act as springs,
without performing positive mechanical work, it is important to note that such
isometric or eccentric muscle contraction incurs a metabolic cost. Thus, arm
swing may be `passive' in the mechanical sense, with energy for movement being
derived ultimately from leg swing, and yet be `active' in the metabolic sense,
requiring metabolic energy for muscle activation.
It is also important to note that mass-damped systems can respond in
complex ways to changes in the oscillation frequency, spring and damping
constants, and relative masses of the segments
(Soong and Dargush, 1997). Our
simple five-segment model essentially treats these variables as constant
across conditions, but this assumption is difficult to test and not addressed
here. More sophisticated models, in concert with more in-depth measurements of
muscle activity, may provide a more comprehensive test of the mass damping
model for upper body mechanics. Specifically, expanding current forward
dynamics models of human walking (e.g.
Anderson and Pandy, 2001) to
include full musculoskeletal treatment of the trunk and arms will provide a
means of examining the interaction between upper and lower body movement.
Both the active arm swing and passive arm swing hypotheses predict that net moments about the body's vertical axis will be kept near zero for steady-state walking and running, and thus net-moment analyses are not able to distinguish between these two mechanisms. Our passive arm swing hypothesis differs primarily in that the power for arm swing is ultimately derived from the swing legs. As such, future work might examine non-steady-state locomotion in which lower limb energy changes, such as with the increase in stride frequency associated with increased walking speed. Active arm swing models would predict these changes to be immediately matched by corresponding changes in upper body movement, whereas a passive model would predict a measurable lag time of at least one step (i.e. one oscillation of the pelvis in the transverse plane) for the increased energy in the legs to be transferred to the upper body.
By highlighting the importance of spring-like mechanisms in the trunk and
shoulder, our work builds upon that of Fernandez Ballesteros and colleagues
(Fernandez Ballesteros et al.,
1965), which suggested that elastic mechanisms in the shoulder are
critical to normal arm swing. This view of arm swing as an emergent property
of human walking also fits well with recent passive-dynamic models of lower
limb mechanics for human walking (Collins
et al., 2005). As with passive-dynamic lower limb movement,
passive spring-driven arm swing mechanics proposed here are inherently
self-tuning without requiring extensive feed-forward neurological control.
Passive-dynamic walkers which include upper body segments connected to the
lower body through elastic elements would provide a further test of the
passive arm swing hypothesis, and perhaps refine current models for upper body
movement in humans.
The role of arm swing in walking and running
With the exception of a small, mechanically negligible decrease in stride
frequency during no arms running and a small but statistically significant
increase in footfall variability during no arms walking, restricting arm swing
or adding weights to the arms had no effect on the lower limb kinematics or
footfall variability measured here, nor did restricting arm swing affect
walking or running cost (Fig.
7B). These results provide further support for the idea that upper
body movement is inherently self-tuned, producing stable walking and running
even when upper body inertial properties are modified. However, as a
consequence of this self-tuning, upper body kinematics were significantly
affected by restricting arm swing, with shoulder rotation and head yaw
increasing substantially in no arms running trials
(Fig. 3A,B). These results, as
well as the relative isolation of the head from the larger rotations
experienced by the shoulders, support Bramble and Lieberman's
(Bramble and Lieberman, 2004)
hypothesis that the derived configuration of the human upper body in which
humans have low, wide shoulders that are mostly decoupled from the head are
exaptive for walking, and are especially important for limiting head yaw and
improving visual stability during running.
The importance of normal arm swing in reducing head yaw in humans raises
the question of how cursorially adapted birds dampen upper body oscillations,
and how bipedal dinosaurs met this mechanical challenge. While researchers
have examined head stabilization in the sagittal plane in birds (e.g.
Katzir et al., 2001;
Troje and Frost, 2000;
Necker, 2007), stability in
the transverse plane warrants investigation. Three potential mechanisms are
immediately apparent. First, the horizontally oriented trunks of these bipeds
will serve to increase the moment of inertia about the vertical axis and
decrease angular excursions. Second, the long, relatively thin neck of some
avian cursors (e.g. ostriches) might act as a filter for oscillations of the
torso, limiting transverse head movements. Third, the long, relatively massive
tails of dinosaurs might provide adequate mass damping of the torso. Indeed,
passive mass damping might be a widespread phenomenon in terrestrial animals.
For example, in kangaroos, movement of the tail in the sagittal plane acts to
dampen pitching of the trunk during hopping
(Alexander and Vernon, 1975);
the long tendons in the kangaroo tail suggest an elastic linkage between the
trunk and tail, as would be expected for a passively damped system.
The anatomical model used here greatly simplifies upper body anatomy,
reducing the multi-segment, multi-muscle, upper body to a five-segment system
with simple damped spring linkages. Still, the evidence for a passive mass
damping model as a predictor of the relative movements of the pelvis,
shoulders and arms suggests that the passive arm swing hypothesis tested here
may provide valuable insight into the mechanics and control of upper body
movement during human walking and running. Future work might integrate a more
sophisticated, multi-segment anatomical model (e.g.
Herr and Popovic, 2008) with a
focus on the mechanisms driving upper body movement. The implication that
upper body movement is a self-tuned, self-stabilizing phenomenon may inform
future analyses of human gait, and may be useful in biomimetic and prosthetic
engineering.
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
|---|
Alexander, R. M. and Vernon, A. (1975). The mechanics of hopping by kangaroos (Macropodidae). J. Zool. Lond. 177,265 -303.
Anderson, F. C. and Pandy, M. G. (2001). Dynamic optimization of human walking. J. Biomech. Eng. 123,381 -390.[CrossRef][Medline]
Bramble, D. M. and Lieberman, D. E. (2004). Endurance running and the evolution of Homo. Nature 424,345 -352.
Cavagna, G. A. and Kaneko, M. (1977).
Mechanical work and efficiency in level walking and running. J.
Physiol. 268,467
-481.
Cohen, J. (1992). A power primer. Psych. Bull. 112,155 -159.[CrossRef][Medline]
Collins, S., Ruina, R., Tedrake, R. and Wisse, M.
(2005). Efficient bipedal robots based on passive-dynamic
walkers. Science 307,1082
-1085.
Elftman, H. (1939). The function of the arms in walking. Hum. Biol. 11,529 -535.
Fedak, M. A., Rome, L. and Seeherman, H. J.
(1981). One-step N2-dilution technique for calibrating
open-circuit VO measuring systems. J. Appl.
Physiol. 51,772
-776.
Fernandez Ballesteros, M. L., Buchtal, F. and Rosenfalck, R. (1965). The pattern of muscular activity during the arm swing of natural walking. Acta Physiol. Scand. 63,296 -310.[Medline]
Gray, J. (1944). Studies in the mechanics of
the tetrapod skeleton. J. Exp. Biol.
20, 88-116.
Gutnik, B., Mackie, H., Hudson, G. and Standen, C. (2005). How close to a pendulum is human upper limb movement during walking? Homo 56,35 -49.[Medline]
Herr, H. and Popovic, M. (2008). Angular
momentum in human walking. J. Exp. Biol.
211,467
-481.
Hinrichs, R. (1987). Upper extremity function in running. II. Angular momentum considerations. Int. J. Sport Biomech. 3,242 -263.
Hinrichs, R. N. (1990). Upper extremity function in distance running. In Biomechanics of Distance Running (ed. P. R. Cavanagh), pp.107 -133. Champaign, IL: Human Kinetics Books.
Jackson, K. M., Joseph, J. and Wyard, S. J. (1978). A mathematical model of arm swing during human locomotion. J. Biomech. 11,277 -289.[CrossRef][Medline]
Katzir, G., Schechtman, E., Carmi, N. and Weihs, D. (2001). Head stabilization in herons. J. Comp. Physiol. A 187,423 -432.[CrossRef][Medline]
Li, Y., Wang, W., Crompton, R. H. and Gunther, M. M. (2001). Free vertical moments and transverse forces in human walking and their role in relation to arm-swing. J. Exp. Biol. 204,47 -58.[Abstract]
Lieberman, D. E., Bramble, D. M. and Raichlen, D. A. (2007). Integration of the head and forelimb in bipedal hominids. J. Morphol. 268,1099 .
Lieberman, D. E., Bramble, D. M., Raichlen, D. A. and Whitcome, K. W. (2008). Functional, developmental and moprhological integration: the case of the head and forelimb in bipedal hominins. Am. J. Phys. Anthropol. Suppl. 46,140 -141.
Necker, R. (2007). Head-bobbing of walking birds. J. Comp. Physiol. A 193,1177 -1183.[CrossRef][Medline]
Ohsato, Y. (1993). Relationships between trunk rotation and arm swing in human walking. Nippon Seikeigeka Gakkai Zasshi 67,440 -448.[Medline]
Pontzer, H. (2007). Predicting the cost of
locomotion in terrestrial animals: a test of the LiMb model in humans and
quadrupeds. J. Exp. Biol.
210,484
-494.
Ruina, A., Bertram, J. E. A. and Srinivasan, M. (2005). A collisional model of the energetic cost of support work qualitatively explains leg sequencing in walking and galloping, pseudo-behavior in running and the walk-to-run transition. J. Theor. Biol. 237,170 -192.[CrossRef][Medline]
Soong, T. T. and Dargush, G. F. (1997). Passive Energy Dissipation Systems in Structural Engineering. New York: Wiley.
Symans, M. D. and Constantinou, M. C. (1999). Semi-active control systems for seismic protection of structures: a state-of-the-art review. Eng. Struct. 21,469 -487.[CrossRef]
Thexton, A. J. (1996). A randomisation method for discriminating between signal and noise recordings of rhythmic electromyographic activity. J. Neurosci. Methods 66, 93-98.[CrossRef][Medline]
Troje, N. F. and Frost, B. J. (2000). Head-bobbing in pigeons: how stable is the hold phase? J. Exp. Biol. 203,935 -940.[Abstract]
Webb, D., Tuttle, R. H. and Baksh, M. (1994). Pendular activity of human upper limbs during slow and normal walking. Am. J. Phys. Anthropol. 93,477 -489.[CrossRef][Medline]
Willems, P. A., Cavagna, G. A. and Heglund, N. C. (1995). External, internal and total work in human locomotion. J. Exp. Biol. 198,379 -393.[Medline]
Winter, D. A. (2005). Biomechanics and Motor Control of Human Movement. 3rd edn. Wiley: New York.
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) was defined as negative when the arm was retracted
(as shown), positive when protracted. Step width was measured as the
difference in x-position of successive heel strikes.
Significant difference compared with
arm weights trials (P<0.05). 
Significant
difference compared with both arm weights and control trials
(P<0.05).
