Human Physiology Section of the Scientific Institute Santa Lucia and the University of Rome "Tor Vergata", 00179 Rome, Italy
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Grasso, R.,
M. Zago, and
F. Lacquaniti.
Interactions Between Posture and Locomotion: Motor Patterns in
Humans Walking With Bent Posture Versus Erect Posture.
J. Neurophysiol. 83: 288-300, 2000.
Human erect
locomotion is unique among living primates. Evolution selected specific
biomechanical features that make human locomotion mechanically
efficient. These features are matched by the motor patterns generated
in the CNS. What happens when humans walk with bent postures? Are
normal motor patterns of erect locomotion maintained or completely
reorganized? Five healthy volunteers walked straight and forward at
different speeds in three different postures (regular, knee-flexed, and
knee- and trunk-flexed) while their motion, ground reaction forces, and electromyographic (EMG) activity were recorded. The three postures imply large differences in the position of the center of body mass
relative to the body segments. The elevation angles of the trunk,
pelvis, and lower limb segments relative to the vertical in the
sagittal plane, the ground reaction forces and the rectified EMGs were
analyzed over the gait cycle. The waveforms of the elevation angles
along the gait cycle remained essentially unchanged irrespective of the
adopted postures. The first two harmonics of these kinematic waveforms
explain >95% of their variance. The phase shift but not the amplitude
ratio between the first harmonic of the elevation angle waveforms of
adjacent pairs was affected systematically by changes in posture.
Thigh, shank, and foot angles covaried close to a plane in all
conditions, but the plane orientation was systematically different in
bent versus erect locomotion. This was explained by the changes in the
temporal coupling among the three segments. For walking speeds >1 m
s1, the plane orientation of bent locomotion indicates a
much lower mechanical efficiency relative to erect locomotion. Ground
reaction forces differed prominently in bent versus erect posture
displaying characteristics intermediate between those typical of
walking and those of running. Mean EMG activity was greater in bent
postures for all recorded muscles independent of the functional role.
The waveforms of the muscle activities and muscle synergies also were affected by the adopted posture. We conclude that maintaining bent
postures does not interfere either with the generation of segmental
kinematic waveforms or with the planar constraint of intersegmental
covariation. These characteristics are maintained at the expense of
adjustments in kinetic parameters, muscle synergies and the temporal
coupling among the oscillating body segments. We argue that an
integrated control of gait and posture is made possible because these
two motor functions share some common principles of spatial organization.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The issue of the relationship between posture and
locomotion is of great theoretical and experimental relevance (see
Burleigh et al. 1994; Lacquaniti et al.
1997
; Massion 1992
; Mori 1987
; Winter 1991
; Zernicke and Smith 1996
).
Neurophysiological studies indicate that the control of posture and
locomotion are interdependent at many different levels of the CNS, from
the motor cortex to the basal ganglia, the brain stem, and the spinal
cord. Thus basal ganglia are connected reciprocally with
ponto-mesecephalic structures (the peduncolopontine nucleus)
belonging to the mesencephalic locomotor region (an area known to
modulate spinal locomotion oscillators) (Shik and Orlovsky
1976
), on the one side, and with the axial motor cortex, on the
other side. These structures participate in the preparation and
initiation of locomotion by providing the appropriate spatial
frameworks necessary to incorporate postural adjustments in the
locomotion process (Garcia-Rill 1986
; Grasso et
al. 1999
). Also when specific areas in the hypothalamus or the
brain stem are stimulated, changes in posture are triggered just before
step initiation (Mori 1987
; Mori et al.
1978
, 1983
, 1989
). Interactions between pathways controlling
posture and gait exist even at the level of spinal premotor
interneurons (Jankowska and Edgley 1993
). In sum, it has
been suggested that the "activation of setting mechanisms in the
level of postural muscle tone and that of the spinal stepping generator
are not separate phenomena" (Mori 1987
).
If the interrelationship between posture and locomotion is well
recognized, the implications vis-à-vis the issue of the motor pattern generation are not well understood (see, however,
Zernicke and Smith 1996). In particular, it is not clear
whether or not the setting of different task-dependent postural
configurations of the body should affect the waveforms of the basic
motor patterns that are rhythmically output during walking. This
question hinges on the more general problem of the nature of the
control waveforms output by central locomotor networks (see
Lacquaniti et al. 1999
; Zernicke and Smith
1996
).
It is generally thought that the multisegment motion of mammals
locomotion is controlled by a network of coupled oscillators (central
pattern generators or CPGs) (see Grillner 1981;
Pearson 1993
; Rossignol 1996
). Although
it often is assumed that CPGs control patterns of muscle activity, an
equally plausible hypothesis holds that they control patterns of limb
segment motion instead (Bianchi et al. 1998b
;
Borghese et al. 1996
; Grasso et al. 1998
; Shen and Poppele 1995
). This hypothesis is based on the
following observations (for a review, see Lacquaniti et al.
1999
). In walking the waveforms of limb segment angular motion
are much simpler and more consistent than the corresponding waveforms
of muscle activity, both in man (Borghese et al. 1996
;
Grasso et al. 1998
) and cat (Shen and Poppele
1995
). Moreover the temporal changes of the elevation angles of
the lower limb segments do not evolve independently of each other, but
they are tightly coupled together (Bianchi et al. 1998b
;
Borghese et al. 1996
). When the elevation angles are
plotted one versus the others, they describe a regular loop constrained
close to a plane, common to both the stance and swing phase. The
specific orientation of the plane of angular covariation reflects the
phase relationships between the elevation angles of the segments and
therefore the timing of the intersegmental coordination. The phase
delay shifts systematically with increasing speed both in man
(Bianchi et al. 1998b
) and cat (Shen and Poppele 1995
). Because in man this phase-shift is correlated with the net mechanical power output over a gait cycle (Bianchi et al. 1998b
), we hypothesized that the control of kinematic phase can be used by the nervous system for limiting the overall energy expenditure with increasing speed (Bianchi et al.
1998a
). Finally, we observed that a reversal of the direction
of walking from forward to backward involves the same waveforms
(time-reversed) of the elevation angles as in forward gait, with a
simple reversal of the delay in the phase coupling between limb
segments, at the expense of a reorganization of the patterns of muscle
activity (Grasso et al. 1998
).
How does a change in walking posture affect these locomotor patterns?
Although we normally walk with an erect posture, we can as easily walk
stooped (as it happens in a low tunnel). Here we compared normal erect
walking (regular, RE) with two different styles of bent walking, namely
knee-flexed walking (KF) and knee-flexed plus trunk-flexed walking
(KT). RE walking is a unique feature of human locomotion (nonhuman
primates normally walk with a bent posture). Its evolutionary history
indicates highly specific adaptations of the skeletal and muscular
apparatus (Crompton et al. 1998; Spoor et al.
1994
). Also, erect posture is mechanically efficient in humans
because the center of body mass vaults over the supporting limb like an
inverted pendulum, thereby limiting energy expenditure by means of an
exchange of the forward kinetic energy with the gravitational potential
energy (Cavagna et al. 1977
). Maintaining KF and KT
posture while walking may interfere with the pendulum mechanism.
McMahon et al. (1987)
showed that in subjects running knee-flexed, reaction moments acting on the knee are increased and the
effective vertical spring stiffness of the legs is decreased relative
to normal running. We introduced condition KT in addition to KF to
assess the specific role of trunk orientation in the generation of
locomotor patterns. In fact it has been proposed that the trunk may act
as a reference in the control of posture and movement (Darling
and Miller 1995
; Massion et al. 1997
;
Mouchnino et al. 1993
). This role then could be
disrupted when the trunk is flexed.
In looking for the effects of changes of walking posture on the
locomotor patterns, we keep in mind that there exist inevitable mechanical consequences of a bent posture. In particular, KF and KT
walking must involve an offset in the mean value of several kinematic
and kinetic variables as compared with RE walking. For instance,
because the legs are flexed, the mean elevation angles of the limb
segments in the former tasks will be generally different from those
measured in the latter task. Also the mean level of muscle activity
will be increased because of the reduced mechanical advantage of bent
limbs (Biewener 1990). With regard to the issue of
locomotor pattern generation, however, the key point concerns the
waveform of the kinetic and kinematic signals rather than their mean
value. According to our previous hypothesis, locomotion is controlled
in a kinematic space (see Lacquaniti et al. 1999
). If
so, one would expect that the changes in posture should leave the
kinematic waveforms essentially unchanged. The interrelationship between posture and locomotion should manifest itself instead on the
phase delay between the motion of different limb segments; that is, the
control parameter that is set centrally according to the specific task
demands. (As we noted in the preceding text, the phase delay varies as
a function of changes in walking speed or reversal of walking
direction.) An additional implication of our hypothesis is that the
conservation of kinematic templates across changes in body posture can
occur only at the expense of a reorganization of muscle patterns and synergies.
![]() |
METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
General procedures have been previously described
(Bianchi et al. 1998b; Borghese et al.
1996
). Kinematic data were obtained by means of the ELITE
system (Ferrigno et al. 1990
). Four 100-Hz TV cameras
were spaced on the recording side of the walkway to enhance spatial
accuracy. After three-dimensional (3D) calibration, the SD spatial
accuracy of the system was better than 1.5 mm (Bianchi et al.
1998b
). The position of selected points on the side of the
dominant lower limb was recorded by attaching the infrared reflective
markers to the skin overlying the following landmarks: gleno-humeral
joint (GH), anterior superior iliac spine (ASIS), posterior superior
iliac spine (PSIS), greater trochanter (GT), lateral femur epicondyle
(LE), lateral malleolus (LM), and fifth metatarso-phalangeal joint
(VM). VM marker was placed on the shoe after verifying the
correspondence on the bare foot. ASIS and PSIS coordinates were
averaged to obtain ilium (IL) position. The body was modeled as an
interconnected chain of rigid segments: GH-IL for the trunk, IL-GT for
the pelvis, GT-LE for the thigh, LE-LM for the shank, and LM-VM for the
foot. The limb axis was defined as GT-LM.
Ground reaction forces were recorded at 500 Hz by means of a multicomponent force platform (0.6 × 0.4 m, Kistler 9281B), placed approximately at the center of the walkway. EMG activity was recorded by means of surface electrodes from the gluteus maximus (GM), biceps femoris (long head) (BF), rectus femoris (RF), vastus lateralis (VL), lateral gastrocnemius (GCL), and tibialis anterior (TA). EMG signals were preamplified (100 ×) at the recording site, digitized, and transmitted to the remote amplifier via 15-m optic fibers. These signals were band-pass filtered (10-Hz high-pass and 200-Hz low-pass, 4-pole Bessel filters), and sampled at 500 Hz. Sampling of kinematic, force, and EMG data were synchronized.
Protocol
Experiments were approved by the Ethics Committee of Santa Lucia
Institute and conformed with the Declaration of Helsinki on the use of
human subjects in research. Five healthy volunteers (2 females, 3 males, 21-36 yr age range) participated after giving informed consent.
Before the recording session, the dominant lower limb of each subject
was determined according to standard criteria (Vanden-Abeele
1980). All subjects proved to be right-dominant. Before the
experiment, we asked our subjects to adopt static KF and KT postures on
the force platform and computed the instantaneous position of the
center of pressure of the net reaction forces during 20-s records
(which were repeated 2 times). Fifteen seconds of steady state data
were averaged to obtain the mean anteroposterior displacement of the
center of pressure relative to RE condition, which was 0.38 ± 0.48 and 0.43 ± 0.66 cm (mean ± SD) forward for KF
and KT, respectively. This small displacement is not statistically significant.
Subjects then were instructed to walk (with their shoes on) with the arms folded on the chest, at a freely chosen, roughly constant speed within the ELITE sampling volume. They were encouraged to vary the speed across trials. Because no additional constraints were used, the speed range overlapped but did not coincide exactly in different experiments. To avoid modifications of the natural step length, subjects were asked not to pay attention whether or not they stepped onto the force platform. Walks were performed in one of the three different postures (RE, KF, and KT), randomly alternated from trial to trial.
Data analysis
Three-dimensional kinematic data were filtered with an optimal
low-pass FIR filter with automatic bandwidth selection (D'Amico and Ferrigno 1990). The angle of elevation of the
ith segment in the sagittal plane with respect to the
vertical was computed as ai =
arctan[(xd
xp)/(yp
yd)], subscripts p and d
denoting proximal and distal endpoints of the segment, respectively,
and x and y the horizontal and vertical
coordinates in the sagittal plane, respectively. Elevation angles are
positive in the forward direction relative to the vertical (i.e., when
the distal marker falls anterior to the proximal). Gait cycle
(T) was defined as the time interval between two successive
maxima in the time series of the limb axis elevation, step length
(S) as the linear translation of GT marker during
T, and average speed as V = S/T. Stance phase was defined as the interval
during which the vertical reaction force exceeded 7% of body weight.
The maximum elevation of the limb axis slightly precedes the heel
touch-down (Borghese et al. 1996
). Different trials from
each subject were ensemble-averaged after time-interpolation of the
kinematic data over T to fit a normalized 1,000-points time base.
Postural geometry was measured by taking the mean, maximum and minimum
value of the elevation angle of each segment over T. The x,
y coordinates of the location of the center of body mass (CM) are
computed as the mean of x, y coordinates of the respective
centers of mass of seven body segments: the H.A.T. plus the thigh,
shank and foot of the right and left limbs. H.A.T. is comprised of
head, folded arms, trunk, and pelvis and is assumed to be one rigid
link (corresponding to GH-GT segment) (see Winter 1991
).
The center of mass of each of these segments was derived from
anthropometric measurements taken on the subject and the geometric
models based on the gamma-scanner method (Zatsiorsky et al.
1990
). Motion of the side of the body contralateral to the
recording one was estimated by time-shifting the recorded data by T/2,
under the assumption of symmetrical gait (Bianchi et al.
1998b
).
EMGs were rectified numerically and low-pass filtered (in both time
directions to avoid tail and phase distortions) by means of a
Butterworth filter, with cutoff at 25 Hz. Cross-correlation functions
(CCF) between pairs of muscles were computed as previously described
(Grasso et al. 1998).
Intersegmental coordination among the lower limb segments (thigh, shank, and foot) was evaluated starting from elevation angle waveforms both in the time domain (1) and in position space (2).
1) Because of the periodic structure of the elevation angle
waveforms over the gait cycle (Bianchi et al. 1998b;
Borghese et al. 1996
), we decomposed such waveforms into
their Fourier series components. The series was truncated at the 10th
harmonic which had a frequency corresponding to the highest low-pass
cutoff frequency in the data series.
The amplitude transfer ratio
(Gpd) and phase shift
(pd) between the corresponding Fourier
harmonics from the elevation angles of adjacent limb segments
p and d were computed, respectively, as
Ak(d)/Ak(p)
and as
k(d)
k(p) where
Ak and
k are the
amplitude and phase respectively of the kth order Fourier
series component.
2) The intersegmental coordination was evaluated in position
space as previously described (Borghese et al. 1996).
Briefly, the changes of the elevation angles of the thigh, shank, and
foot covary linearly throughout the gait cycle. The thigh-shank-foot 3D
loops describe paths that can be fitted by a plane that is computed by
means of orthogonal regression (Bianchi et al. 1998b
; Borghese et al. 1996
). In each trial, we computed the
covariance matrix R of the ensemble of time-varying
elevation angles over the gait cycle, after subtraction of their
respective mean value. Eigenvalues
and eigenvectors u
are computed by factoring the covariance matrix R from the
set of original signals by using a singular value decomposition
algorithm such that
![]() |
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
General gait parameters
Walking flexed was performed easily in both KF and KT conditions.
The subjects had no difficulty in maintaining that posture for the few
seconds required to walk the 10-m path. We analyzed a total number of
463 gait cycles at speeds within the range 0.2 2.0 m
s
1. The range of speeds 0.5
1.5 m
s
1 was represented evenly across subjects and conditions.
The well-known monotonic increase of step length with walking speed was
not significantly affected by postural geometry. The slopes of the
intraindividual regression lines were 0.48 ±0.11 m per 1 m
s1 speed increment, 0.44 ± 0.33, and
0.33 ± 0.38 for RE, KF, and KT, respectively (the intercepts were
0.75 ±0.09 m, 0.84 ± 0.41, 0.83 ± 0.37). However the
scatter was larger for KF and KT than for RE (correlation coefficients
were 0.97 ± 0.02, 0.85 ± 0.10, and 0.69 ± 0.38, respectively). Repeated-measures ANOVA across the three conditions
yielded nonsignificant outcomes for the comparisons between regression
parameters from the three conditions. On average, the duration of the
stance phase was ~60% of the gait cycle in all conditions (61.9 ± 3.9%, 62.1 ± 2.7%, and 62.8 ± 2.6% for RE, KF, and
KT, respectively) and decreased with increasing speed (on average by
6.5% of gait cycle duration per 1 m s
1).
The latter relationship was less consistent in KF and KT than in RE.
Geometric postural features
Figure 1 (top) shows
stick-diagram series from one subject walking at approximately the same
speed (1 m s
1) with the three different
postures. As implied by the task, postural geometry differs drastically
across the three conditions. In KF and KT, the lower limbs are flexed
and the trunk is tilted, more so in KT. The vertical oscillations of
the trunk and pelvis segments are less pronounced in KF and KT than in
RE. The position of the CM is displaced forward and downward in KF and
more so in KT so that it tends to lie outside of the body. (CM in the
1st stick diagram from each of the 3 series is displayed as a
in
Fig. 1). Note that the hip is raised above the supporting foot in
midstance like an inverted pendulum in RE but that it remains level or
is even lowered in midstance in KF, a situation closer to that
occurring in running than in normal walking. On the other hand, the
trajectory described by the foot tip is similar across the three
postures. Figure 1, bottom, shows the instantaneous location
of the CM as a function of time from the gait cycles displayed in the
top. The prominent oscillation typical of regular gait is
dampened drastically in KF, indicating that the impact shock with the
ground is attenuated by the lower limb joints. The decrease in the
oscillation of the CM in KF relative to RE was more evident at higher
speeds.
|
Table 1 reports the mean value (over the
gait cycle, from all trials of all subjects) of the elevation angles of
each limb segment in the sagittal plane, and the mean value of the
length of the limb axis. The mean orientation of the body segments
changed according to the instructions. Thus in bent postures, the trunk was flexed anterior, the thigh was elevated in the forward direction, and the shank in the backward direction in comparison with erect posture. The changes in these parameters were not independent of each
other. The mean elevation of the trunk and thigh changed in a
correlated manner across all subjects and postures, as did thigh and
shank (r = 0.73 and 0.77, respectively). As expected, the mean limb length was significantly shorter in KF and KT than in RE
(F1,460=194.3, P < 0.001, with no difference between KF and KT, Scheffé's post hoc
test).
|
Kinematic waveforms
Figure 2 shows the average waveforms
(±SD, data from all trials and subjects) of the elevation angles in
the three conditions. These waveforms are plotted versus normalized
time, expressed as a percentage of gait cycle duration. The elevation
angle of the trunk and pelvis changed little during the gait cycle. All other elevation angles displayed the typical biphasic shape, which has
been described thoroughly in previous articles (Bianchi et al.
1998b, Borghese et al. 1996
, Grasso et
al. 1998
). Briefly, the lower limb segments rotate backward
during stance and forward during swing.
|
The new finding is that the segmental kinematic waveforms have a very similar shape in the three conditions (RE, KF, and KT) even though they may differ in offset or amplitude. The SD bands are rather narrow but more so in the RE condition. Table 2 shows the correlation coefficients (mean ± SD, data from all subjects) computed between the individual average waveforms of each experimental condition. The correlation was always very high, confirming that the shape of the elevation angle waveforms was maintained across the three walking conditions in all subjects.
|
Figure 3 shows the range (±SD) of the values of the elevation angles during one gait cycle for the different tasks. For all limb segments, the overall extent of the angular excursion changed only slightly.
|
Intersegmental coordinationtime domain
The first two harmonics of the Fourier series expansion account together for >95% of the experimental variance of each kinematic waveform (Table 3), and the shape of the reconstructed waveforms faithfully reproduces the main features of the original ones. The first harmonic alone explains most (>70%) of the original waveform variance.
|
Figure 4 shows the average time-normalized waveforms of thigh, shank, and foot elevation angles from one subject. To superimpose all traces on the same scale, the mean value was subtracted from each one. The time changes of thigh elevation lead those of shank elevation (the time delay between the 2 minima is indicated by the shaded areas in the figure). However, the time lead is consistently shorter in KF and KT than in RE. In contrast, the amplitude of segmental excursions does not vary substantially across the three conditions.
|
The amplitude transfer ratio (G) and the phase shift ()
between pairs of adjacent lower limb segments (thigh-shank and
shank-foot) was quantified by means of harmonic analysis (see
METHODS) and is shown in Fig.
5 for the first harmonic (mean ± SD, from all subjects). One-way ANOVA showed that the changes of the
two gains across conditions were marginally significant (0.01 < P < 0.05 and P = 0.05 for
Gts and
Gsf, respectively). The difference
between RE and KT was significant, whereas KF could not be
distinguished from either RE or KT (Scheffé's post hoc test).
The relative increase of Gsf means
that the amplitude of foot movements relative to that of the shank
tended to increase in bent postures relative to the erect posture. The
opposite tendency is showed by Gts.
|
For the phase shifts ts and
sf the changes from RE to KF and KT were
prominent and highly significant (P < 0.0001). In RE, the first harmonic of the thigh elevation waveform led consistently that of shank elevation (by 53.3 ± 5.1°, corresponding to
14.8 ± 1.4% of T) and the shank led slightly the foot
(by 15.2 ± 10.8°, 3.1 ± 1.2% of T). In KF the
phase shift between the thigh and shank dropped (to 15.2 ± 10.8°, 4.2 ± 3.0% of T) and the shift between shank
and foot increased approximately twofold (27.6 ± 6°, 7.7 ± 1.7% of T).
Ratios and phase shifts for the second harmonic displayed little changes across the conditions for the shank-foot pair, whereas for the thigh-shank pair only KF differed from either RE or KT (which were similar to each other). Higher harmonics contributed little to the original waveform shape, and gain and phases between adjacent segments were related inconsistently to the walking condition.
Planar covariation of limb elevation angles
We have found previously that in normal erect posture the
temporal changes of the elevation angles of the limb segments covary along a plane common to both the stance and the swing phase
(Bianchi et al. 1998b, Borghese et al.
1996
, Grasso et al. 1998
). We now report that a
planar law holds also for walking with a bent posture. Figure
6 shows the gait loops from the subject
displayed in the stick diagram of Fig. 1. Each loop is obtained by
plotting the elevation angle of the thigh, shank, and foot one versus
the others (3D position space). Paths progress in time in the
counter-clockwise direction, heel strike and toe off phases
corresponding to the top and bottom of the loop, respectively. In all
conditions, the gait loop lies close to one plane. The grids correspond
to the (least-squares) best-fitting planes and to their intersection with the cubic wire frame of the angular coordinates. The proportion of
variance explained by the three eigenvalues
1,
2, and
3, from all
trials is shown in Table 4. In all
conditions, the planar regression (the first 2 eigenvalues together)
accounts for >98% of the data variance. However, the loop tends to be
contracted along its long axis in the two flexed postures. This is
reflected by a significantly lower percent variance explained by
2 in KF and KT with respect to RE (Table 4,
F2,460=88.4, P < 0.001).
|
|
The orientation of the plane of angular covariation is not the
same in the three conditions, but it shows a rotation about the long
axis of the loop in KF and KT relative to RE. The direction cosine of
the plane normal with the thigh axis
(u3t) is the parameter most sensitive
to this type of rotation (see Bianchi et al. 1998b). Figure 7 shows the value of
u3t for the overall data set. This parameter displayed a systematic increment for the two flexed postures
with respect to the regular one (0.22 ± 0.07 for RE, 0.61 ± 0.18 for KF and 0.67 ± 0.15 for KT,
F2,447 = 509.1, P <0.001). The difference between KF and KT was small but significant
(Scheffé's post hoc test, P < 0.01).
|
Patterns of contact forces with the ground
Figure 8 shows the
time-normalized vertical (top) and longitudinal
(bottom, anterior force is positive) ground reaction forces for the speed range 0.5-1.5 m s1 from 11 trials of the same subject as in Fig. 1. In RE the time course of
vertical forces exhibits the classical pattern, with two main peaks
(one in early stance and the second one in late stance) separated by a
trough in midstance due to partial unloading from the contralateral
limb (see Borghese et al. 1996
; Winter 1991
). The first peak and the trough become more pronounced
with increasing speed. By contrast, in KF and KT the profile of
vertical forces tends to be monophasic, with peaks and trough much less pronounced than in RE. Moreover this pattern does not vary with increasing speed as much as in RE (the slopes and r
coefficients of the regression line relating the walking speed to the
peak force are 109 ± 25 N m
1 s,
r = 0.81 for RE, 40 ± 10 N
m
1 s, r = 0.77 for KF, and
41 ± 25 N m
1 s, r = 0.53 for KT). The amplitude of the plateau in bent postures is lower than
the two peaks displayed in RE especially at higher speeds.
|
As for the longitudinal reaction force, in RE it exhibits an ordered
amplitude increase with speed both in the deceleration (after heel
touch-down) and the acceleration (before toe take-off) phase
(r = 0.94 for the peak force vs. speed linear
regression). In KF and KT, this force has a lower peak amplitude than
in RE and the profiles are much more variable across trials
(r = 0.46 and 0.41 for KF and KT, respectively). Also
the stance phase duration displays a less consistent relation with
speed as compared with RE. The linear regression between stance percent
duration and speed yielded the following parameters: slope = 6.9% per 1 m s
1,
7.2 and
8.2 for
RE, KF, and KT; intercept = 65.6, 68.7, and 68.5%, for RE, KF,
and KT; correlation coefficients = 0.91, 0.83, and 0.61 for RE,
KF, and KT.
EMG activity
Figure 9, left, shows the
EMG ensemble average of 10 gait cycles from one subject walking at
1 m s1 in each of the three conditions. In
all examined muscles, the mean activity tended to be greater in KF and
KT than in RE. GM mean integrated EMG over the gait cycle increased 117 and 230% from RE to KF and KT, respectively, RF increased 271 and
174%, VL 418 and 189%, BF 152 and 460%, TA 72 and 35%, and GCL 278 and 145%. Not only the mean amplitude but also the pattern of activity differed across conditions. RF displayed prominent activity in the
early swing phase of KT condition compared with RE. BF displayed a
prominent activity during the stance phase of KF and even more so in
KT, whereas in RE this muscle is typically active during the swing
phase mainly. Task-dependent changes in the activity profile also were
observed in the other tested muscles. As a consequence, the squared
correlation coefficients between postural conditions tend to be lower
than those computed for the corresponding changes in the elevation
angles (right). This was true for all subjects.
|
Not only the patterns of activation of individual muscles but also the time sequence of activation of different muscles, or muscle synergies, varied substantially as a function of body posture. To quantify this kind of synergies, we computed the CCF between pairs of EMG ensemble averages. The CCFs for the three tasks are superimposed in Fig. 10. The patterns of muscle synergies differ across conditions as indicated by the different shape of the CCF. For instance, in RE and KF the peak in the pair RF-VL occurs at ~0% of the gait cycle, indicating agonistic activity. By contrast, in KT the peak occurs at ~40% lead, indicating quasi-antagonistic activity in these two muscles. Most other pairings of muscles exhibit significant time shifts in either the maximum or the minimum of the CCF in KF and KT conditions relative to the RE condition. This is shown by the scatter plot of these values in Fig. 11. Several data points (13/30 and 18/30 for the maximum and minimum, respectively) fall outside the ±10% cycle duration band (shaded area).
|
|
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Kinematic waveforms
The purpose of the present study was to verify how walking with a bent posture affects the generation of gait motor patterns. In bent postures (KF and KT), the lower limbs and the trunk are flexed as compared with the erect posture (RE). In spite of the drastic changes in postural geometry, the oscillations of each limb segment relative to the vertical are very similar across different postures.
The adaptation of the intersegmental coordination to the different
postures is achieved by changing the phase delays between the motion of
different limb segments. Thus the phase lead of thigh oscillations
relative to shank oscillations is systematically shorter with bent
postures than with an erect posture, whereas the phase lead of the
shank relative to the foot oscillations is longer in the former than in
the latter. As a result, there is an approximately constant phase
décalage (of ~20° corresponding to 5% of the gait cycle)
between adjacent limb segments in the proximal-to-distal direction in
bent walking. The behavior of a flexed lower limb then can be equated
to that of a rolling wheel with the pivot corresponding to the knee joint.
Somewhat similar results have been reported previously in the
comparison of level walking versus crouched walking in cats (Trank et al. 1996). (The latter is an attitude typical
of stalking behavior in this species.) The profiles of hindlimb
kinematics were rather similar between the two forms of locomotion
either for the hip, knee, ankle, or the metatarso-phalangeal joint
angles (see Fig. 3 of Trank et al. 1996
) in spite of
prominent changes in their mean values and ranges of excursion. The
timing of motion reversals (from flexion to extension) differed at some joints.
Therefore postural adaptation acts on the same variable, intersegmental
kinematic phase, which has been shown previously to be centrally
modified as a function of changes in walking speed (Bianchi et
al. 1998b) or walking direction (Grasso et al.
1998
). Intersegmental phase plays a role of global control
variable similar to that previously advocated for the network of
coupled oscillators involved in the generation of locomotion (see
Grillner 1981
; Pearson 1993
;
Rossignol 1996
). According to the kinematic view we have exposed in the introduction, each unit oscillator would directly control a limb segment, alternately generating forward and backward oscillations of the segment. Intersegmental coordination would be
achieved by coupling unit oscillators with a variable phase. Variable
coupling could result, for instance, by changing the synaptic strength
(or polarity) of the relative spinal connections. Supraspinal centers
may drive or modulate functional sets of coordinating spinal
interneurons to generate different walking modes. We shall take up the
issue of the mechanisms of postural modulation of the coordinating
network in a subsequent section.
Planar covariation of elevation angles
The changes in the elevation angles of the thigh, shank, and foot
covary along a plane common to both the stance and the swing phase.
More than 98% of the data variance is explained by the planar
regression in all postural conditions. However, the orientation of the
plane differs across conditions: the plane is rotated about the long
axis of the loop with bent postures as compared with the erect posture.
The direction cosine of the plane normal with the thigh axis
(u3t) is the parameter most sensitive
to this type of rotation (see Bianchi et al. 1998b). We
have shown previously that changes of
u3t reflect changes in the phase
coupling between the limb segment elevation angles (Bianchi et
al. 1998b
). Therefore the demonstration of changes of
u3t in KF and KT relative to RE is
consistent with the observation that the adaptation of locomotion to
these postural changes affects the intersegmental phase coupling.
We have shown previously that the parameter
u3t generally correlates with the
mechanical energy expenditure during erect walking: the greater is the
value of u3t in any given trial, the
greater is the corresponding net mechanical power output over a gait
cycle (Bianchi et al. 1998a). Here we found that the
values of u3t for KF and KT are
systematically greater than the corresponding values for RE. This
result is consistent with the observation that the level of muscle
activity (and therefore of overall energy expenditure) is
systematically higher with bent posture than with erect posture (see
next section).
Adaptation of limb kinetics to bent postures
The patterns of ground reaction forces and muscle activity change
systematically as a function of walking posture. The time profile of
the vertical reaction forces is multiphasic in erect posture but
monophasic and of lower amplitude in bent postures (Li et al.
1996). In the latter case, in fact, the impact shock with the
ground is attenuated, especially by the knee joint (McMahon et
al. 1987
). The monophasic profile is reminiscent of that
measured during erect running. However, in bent walking the percent
duration of stance is much longer than in running, so that
McMahon et al. (1987)
pointed out that knee-flexed
locomotion is somewhat intermediate between walking and running. The
peak amplitude of the longitudinal forces is greater in erect than in
bent walking. In general, reaction forces are more variable in bent postures.
The level of muscle activity is systematically higher with bent posture
than with erect posture. When the limb joints are flexed and displaced
away from the main axis of the limb, the mechanical advantage of the
muscles to support body weight during stance and to flex the limb
during swing is reduced in comparison with that of the erect posture,
and more muscle activity is needed to generate the appropriate joint
torques. An increment of muscle activity also is required because the
recovery of mechanical energy by means of the pendulum mechanism is
less effective, and more muscular work needs to be done to move the
center of body mass and to swing the limbs. In erect walking, in fact,
the center of body mass oscillates above the supporting limb like an
inverted pendulum, thereby limiting energy expenditure by means of an
exchange of the forward kinetic energy with the gravitational potential energy (Cavagna et al. 1977). By contrast, in bent
postures the center of body mass tends to be displaced downward and
forward, and its oscillations are reduced because the legs cannot fully extend (see Fig. 1). Thus the exchange of kinetic and potential energy
is more limited. Indeed, human knee- and hip-flexed walking has been
found to produce in-phase fluctuations in potential and kinetic
energies (Li et al. 1996
) rather than the out-of-phase fluctuations typical of erect locomotion.
Not only the amplitude but also the pattern of activation of individual muscles, as well as the time sequence of muscle synergies, vary substantially as a function of body posture. This was shown by considering the cross-correlation functions of pairs of EMG ensemble averages. In particular we observed considerable shifts in either the maximum or the minimum of several cross-correlation functions, indicating the transition from an agonistic activity in a given postural condition to a quasi-antagonistic activity in the same pair of muscles in a different postural condition (see Figs. 10 and 11).
Trank et al. (1996) also found that muscle patterns for
cats walking knee-flexed display some changes with respect to normal walking. The burst duration for three primary knee, ankle, and digit
flexor muscles are longer and EMG amplitude has often a higher
amplitude according to the increased range and duration of flexion
during the swing phase. Furthermore two muscles that show mainly
swing-related activity in normal walking (digit extensors) have
distinct stance-related bursts in crouched walking, similar to what we
found for BF. Similar changes in the functional role of specific
muscles with respect to normal walking also have been described for
down-slope (Smith et al. 1998
) and up-slope walking (Carlson-Kuhta et al. 1998
). The extensive investigation
of different forms of locomotion in cats (normal forward, backward,
crouched, up slope, and down slope) (reviewed in Zernicke and
Smith 1996
) has made clear that the coupling between muscle
groups is an important functional variable and may be modified
centrally depending on the behavioral context. Even robust
stance-sustaining synergies, which rely on the excitatory coupling
between hip and knee extensor units, may be switched off in particular
cases (i.e., in down-slope walking). To explain these transitions to
different synergies without dropping the schema of an array of
joint-related CPG units (Grillner 1981
), Zernicke
and Smith (1996
; see also Prochazka 1996
)
proposed that the rule for setting interunit connections may be issued
by supraspinal control following conditional statements related to the
behavioral goal.
Our view is that, in response to the kinematic reference signals, the
appropriate muscle synergies are determined in a flexible manner to fit
the current mechanical constraints. Indeed, the relationship between
muscle activity and whole body mechanics seems too variable and complex
to allow a direct control of the latter without an intermediate
kinematic representation. Neurophysiological data indicate that
intermediate kinematic representations may be found in both premotor
(Bizzi et al. 1991) and proprioceptive sensory areas of
the spinal cord (Bosco et al. 1996
) as well as in
somatosensory and motor cortices (Helms Tillery et al.
1996
; Lacquaniti et al. 1995
; Scott and
Kalaska 1997
).
Integrated control of gait and posture
Postural adaptation conserves the general kinematic waveforms with
an appropriately tuned intersegmental phase and allocates specific
patterns of muscle activity as a function of the required kinematic
coordination. Integrated control of gait and posture is made possible
because these two motor functions share some common organizational
principles (Lacquaniti et al. 1997; Massion 1992
). First, the frame of reference for the kinematic
coordination for both postural responses and locomotion seems to be
anchored to the vertical. Second, a control of the position of the
center of body mass for static or dynamic equilibrium is involved in both gait (Cavagna et al. 1977
) and posture
(Massion 1992
). Also the planar law of intersegmental
kinematic coordination applies to both tasks. The planar law involved
in postural responses has been reported in previous work
(Lacquaniti and Maioli 1994a
,b
). The changes in the
geometric configuration of the forelimbs and hindlimbs in cats pitched
by variable tilts of the support platform lie close to one plane. This
planar covariation is not affected by adding loads that shift the
animal's center of mass. Intersegmental coordination of lower limbs
and trunk also has been described for human postural responses evoked
by perturbations of the support platform (Nashner and McCollum
1985
) or by axial bending movements (Massion
1992
). It is not surprising to find that similar laws of
intersegmental coordination apply to the control of posture and
locomotion. Locomotion must assure a forward progression compatible with dynamic equilibrium, adapting to potentially destabilizing factors
(e.g., changes in body posture or load, uneven terrain, obstacles,
etc.) in an anticipatory fashion by means of coordinated synergies of
upper limbs, trunk, and lower limbs (Dietz et al. 1987
;
Hirschfeld and Forssberg 1991
).
The concept of an integrated control of posture and locomotion also
stems from neurophysiological data. The stimulation of specific areas
in the brain stem and hypothalamus in freely moving cats causes the
animal to adopt different locomotor and postural styles (Mori et
al. 1989). Flexed locomotion can be evoked in cats by
stimulating the lateral hypothalamus (Mori et al. 1989
). Also selective lesions in Deiters' nucleus result in a significant decrease in extensor muscle activity during locomotion (Orlovsky 1972
). Repetitive stimulation of medial longitudinal fasciculus neurons may disrupt fictive locomotor rhythms (Floeter et al. 1993
; Gossard et al. 1996
). The link between
posture and gait control is also mediated through afferent feedback.
Thus Shik and Orlovsky (1976)
proposed that
vestibulospinal and reticulospinal pathways carrying information from
both the sensory feedback and from the state of the spinal circuitry
may operate during locomotion to "sculpt" the output patterns
generated in the segmental CPGs (see also Armstrong
1988
; Hasan and Stuart 1988
).
A specific role in setting the spatial framework for the control of the
postural geometry of the trunk and the coordination of lower limb
segments is provided by the basal ganglia (Garcia-Rill 1986). A recent study addressed this issue in patients with
Parkinson's disease (Grasso et al. 1999
). Patients
could be switched ON by means of either a
D1-D2 receptor agonistic
drug (apomorphine) or by globus pallidum internum (GPi) electrical
stimulation. It was found that the inclination of the trunk with
respect to the vertical, the waveforms of the elevation angles, and the
planar law of angular covariation change all in parallel in the
transition from the OFF to the ON condition.
![]() |
ACKNOWLEDGMENTS |
---|
We thank D. Prissinotti for skillful technical help with the experiments.
The financial support of Telethon-Italy (Grant 1159) is gratefully acknowledged.
![]() |
FOOTNOTES |
---|
Address for reprint requests: F. Lacquaniti, IRCCS Santa Lucia, via Ardeatina 306, 00179 Rome, Italy.
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 6 May 1999; accepted in final form 14 September 1999.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|