J Neurophysiol 112: 374 –383, 2014.
First published April 30, 2014; doi:10.1152/jn.00138.2014.
A neuromechanical strategy for mediolateral foot placement in
walking humans
Bradford L. Rankin,1 Stephanie K. Buffo,1 and Jesse C. Dean1,2
1
Division of Physical Therapy, College of Health Professions, Medical University of South Carolina, Charleston, South
Carolina; and 2Ralph H. Johnson Department of Veterans Affairs Medical Center, Charleston, South Carolina
Submitted 18 February 2014; accepted in final form 27 April 2014
biomechanics; locomotion; muscle activity; stability
MAINTAINING STABILITY is traditionally considered a basic requirement for functional human gait (Woollacott and Tang
1997). Among many clinical populations, mobility is limited
by the increased fall risk and fear of falling that accompany
decreased stability (Jorgenson et al. 2002; Schmid and Rittman
2009). To address this issue, several metrics have been proposed to quantify gait stability (e.g., stride period variability,
Lyapunov exponents, Floquet multipliers), each of which have
been associated with fall risk (Granata and Lockhart 2008;
Hausdorff et al. 2001; Lockhart and Liu 2008). However,
interpretation of these metrics is difficult (Bruijn et al. 2013;
Monsch et al. 2012; van Schooten et al. 2011), as causal
relationships with stability have not been established. For
example, increases in kinematic variability have been interpreted as a sign of both increased (Bauby and Kuo 2000) and
Address for reprint requests and other correspondence: J. C. Dean, 77
President St. MSC700, Charleston, SC 29425 (e-mail: [email protected]).
374
decreased (Sawers and Hahn 2012) active control of stability.
Rather than focusing on potential indicators of fall risk, the
present study investigates the neuromechanical strategy that
healthy, uninjured humans use to maintain mediolateral gait
stability and prevent falls. A clearer understanding of this
typical stabilization strategy may lead to methods better able to
directly identify the cause of increased fall risk among clinical
populations.
Gait stability requires appropriate mechanical interactions
with the environment. While walking, humans continuously
experience mechanical perturbations due to motor control errors or environmental factors. Model simulations have demonstrated that small anteroposterior perturbations can be stabilized by the body’s passive dynamics, while mediolateral
perturbations require active stabilization (Kuo 1999). This may
explain why frontal-plane motor control appears to play a
dominant role in gait stability (Allet et al. 2012). Both models
and experiments have indicated that gait can be efficiently
stabilized through appropriate mediolateral foot placement
(Bauby and Kuo 2000; Kuo 1999; MacKinnon and Winter
1993), as controlled through frontal-plane positioning of the
leg during the swing phase (Hurt et al. 2010; Krishnan et al.
2013). However, simply being able to accurately control swing
leg position may not be sufficient for mediolateral stability, as
humans must also identify a mechanically appropriate target
location for swing leg placement. For example, during a typical
right step, placing the swing foot to the left of both the stance
foot and the center of mass (CoM) would likely lead to a loss
of balance, no matter how accurately the foot was guided
toward this position. The appropriate foot placement target
may be derived from the mechanical state of the stance leg or
trunk (Hurt et al. 2010; Kuo 1999). Supporting this proposal,
the body’s extrapolated center of mass (a metric combining
CoM position and velocity) generally predicts mediolateral
foot placement for a variety of walking conditions (Hof et al.
2007; Rosenblatt et al. 2012). However, the underlying neural
mechanism through which stance leg mechanics contributes to
the control of swing leg placement is unclear (Hof et al. 2010).
Active control of lateral stability implies a neural strategy to
meet the mechanical requirements of walking. While it can be
difficult to differentiate between gait characteristics caused by
passive mechanics and active control, muscle activation patterns can provide insight into the underlying control strategies
(Chvatal and Ting 2012). To convincingly demonstrate the
existence of an active control strategy for mediolateral foot
placement, we must first show that muscle activity influences
foot placement location. Subsequently, we must determine
which factors humans use to choose the level of this muscle
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Rankin BL, Buffo SK, Dean JC. A neuromechanical strategy
for mediolateral foot placement in walking humans. J Neurophysiol 112: 374 –383, 2014. First published April 30, 2014;
doi:10.1152/jn.00138.2014.—Stability is an important concern during human walking and can limit mobility in clinical populations.
Mediolateral stability can be efficiently controlled through appropriate
foot placement, although the underlying neuromechanical strategy is
unclear. We hypothesized that humans control mediolateral foot
placement through swing leg muscle activity, basing this control on
the mechanical state of the contralateral stance leg. Participants
walked under Unperturbed and Perturbed conditions, in which foot
placement was intermittently perturbed by moving the right leg
medially or laterally during the swing phase (by ⬃50 –100 mm). We
quantified mediolateral foot placement, electromyographic activity of
frontal-plane hip muscles, and stance leg mechanical state. During
Unperturbed walking, greater swing-phase gluteus medius (GM) activity was associated with more lateral foot placement. Increases in
GM activity were most strongly predicted by increased mediolateral
displacement between the center of mass (CoM) and the contralateral
stance foot. The Perturbed walking results indicated a causal relationship between stance leg mechanics and swing-phase GM activity.
Perturbations that reduced the mediolateral CoM displacement from
the stance foot caused reductions in swing-phase GM activity and
more medial foot placement. Conversely, increases in mediolateral
CoM displacement caused increased swing-phase GM activity and
more lateral foot placement. Under both Unperturbed and Perturbed
conditions, humans controlled their mediolateral foot placement by
modulating swing-phase muscle activity in response to the mechanical
state of the contralateral leg. This strategy may be disrupted in clinical
populations with a reduced ability to modulate muscle activity or
sense their body’s mechanical state.
NEUROMECHANICAL FOOT PLACEMENT STRATEGY
MATERIALS AND METHODS
Participants
Ten healthy, right-leg-dominant adults [8 women, 2 men; age ⫽ 23 ⫾
1 yr, height ⫽ 1.70 ⫾ 0.09 m, mass ⫽ 63.8 ⫾ 12.1 kg (mean ⫾ SD)]
participated in this study. Participants provided informed consent, and
the protocol was approved by the Medical University of South
Carolina Institutional Review Board.
Experimental Procedure
For all trials, participants walked on a treadmill at 1.25 m/s and
were instructed to keep their gaze directed forward. Participants first
performed a 5-min warm-up trial in order to become accustomed to
walking on the treadmill (Zeni and Higginson 2010). Participants then
performed a 5-min Unperturbed walking trial and a 5-min Perturbed
walking trial, in randomized order. The Unperturbed trial was used to
identify associations between metrics of gait mechanics and active
control (in the form of muscle activity) during typical, unperturbed
walking. In contrast, the purpose of the Perturbed trial was to more
directly test causality between these metrics by applying mechanical
perturbations and quantifying the behavioral responses. A rest period
of 5 min was provided between trials. During walking trials, participants wore a harness attached to an overhead rail for safety. This
harness did not provide any body weight support but would have
prevented a fall in case of a loss of balance.
In the Unperturbed walking trial, participants were instructed to
walk normally. In the Perturbed walking trial, the mediolateral placement of the right foot was intermittently perturbed. Unlike perturbations applied by pushing the CoM (Hof et al. 2010; Hof and Duysens
2013) or translating the support surface under the stance foot (Chvatal
and Ting 2012; Oddsson et al. 2004), the present type of perturbation
mimics the mechanical effects of errors in foot placement control.
Foot placement perturbations were applied by an experimenter moving the right leg by hand slightly medially (defined as a medial
perturbation) or laterally (a lateral perturbation) during the swing
phase prior to heel strike. Participants were instructed to ignore the
perturbations as much as possible and not to resist or assist the
experimenter. By applying the perturbations during the swing phase,
relatively small forces were sufficient to influence foot placement
location. A lightweight padded cuff was strapped around the right leg
just above the ankle, so participants were unable to detect pressure
applied by the experimenter’s hand. Pressure-sensitive switches (Motion Lab Systems, Baton Rouge, LA) on the medial and lateral sides
of the cuff were used to detect when a perturbation was applied. The
same experimenter applied the perturbations to all participants after an
extensive training period (8 preliminary data collection sessions) of
practicing delivery of these perturbations. There were no apparent
trends for the number or average size of the delivered perturbations to
change over time from the first to the tenth participant (Spearman
correlations: P ⫽ 0.39 for number of perturbations; P ⫽ 0.16 for
lateral perturbation size; P ⫽ 0.82 for medial perturbation size).
To prevent participants from predicting the application of perturbations, the direction (medial or lateral) and timing (every 5–10
strides) of the perturbations were randomized by the experimenter
applying the perturbations. Across participants, 17 ⫾ 2 (mean ⫾ SD)
medial perturbations and 20 ⫾ 3 lateral perturbations were applied.
Medial perturbations had an amplitude of 53 ⫾ 9 mm (mean ⫾ SD),
while lateral perturbations had an amplitude of 98 ⫾ 14 mm, as
quantified by the change in mediolateral foot placement compared
with steady-state walking. As humans appear to recover from mediolateral perturbations within a single stride (Hof et al. 2010), the
perturbation timing allowed participants to return to steady state
between perturbations.
Data collection and processing. Spatiotemporal gait characteristics
were quantified with active LED markers (PhaseSpace, San Leandro,
CA) sampled at 120 Hz and placed on the sacrum, right heel, and left
heel. The sacrum marker was used to represent the mediolateral
position of the body’s CoM. While this relatively simple method does
not account for frontal-plane motion of the extremities, the timing of
mediolateral motion of the pelvis is very similar to that of the body’s
entire CoM (Whittle 1997). The mediolateral displacement of the
swing foot was measured with respect to the CoM, while the mediolateral displacement of the CoM was measured with respect to the
stance foot (Fig. 1A). Velocities were calculated by low-pass filtering
(20 Hz) and differentiating the displacement data. Accelerations were
calculated by performing the same process with the velocity data. The
anteroposterior velocities of the heel markers were used to identify
heel-strike and toe-off events (Zeni et al. 2008), thus defining the
stance and swing phases of the gait cycle. Strides were defined for
each leg as the period from one heel strike to the next heel strike with
the same leg, while steps were defined as the period from one heel
strike to the next heel strike with the opposite leg.
Muscle activity was quantified with surface electromyography
(EMG) electrodes (Motion Lab Systems) sampled at 1,000 Hz. We
measured bilateral gluteus medius (GM) activity and bilateral adductor magnus (AM) activity with electrode placement based on previously published guidelines (Hermens et al. 1999; Lovell et al. 2012).
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activity, providing insight into how the target foot placement
location is determined. Recent cat experiments have demonstrated that during walking muscle activation patterns and foot
placement are influenced by the mechanical state of the contralateral stance leg (Musienko et al. 2012b). However, it is
presently unclear how humans control swing-phase activity
and foot placement during a given step.
The purpose of this project was to investigate the neuromechanical strategy used by healthy, uninjured humans to control
their mediolateral foot placement. Our primary interest was in
the strategy used during what is often termed “steady-state”
walking, in which no external mechanical perturbations are
applied but humans must continuously respond to the effects of
motor control errors. To quantify this behavior, we correlated
step-to-step metrics of gait kinematics and muscle activity
during unperturbed walking. While this approach can reveal
relationships between various gait metrics, it is unable to
demonstrate a causal relationship between the body’s mechanical state and subsequent muscle activity. Therefore, we applied mediolateral foot placement perturbations during the
swing phase of walking (altering the body’s mechanical state)
and determined whether the subsequent response matched the
correlations present during unperturbed walking. Our use of
perturbations to test specific correlation-based predictions differentiates the present work from the many previous studies
that have focused on the immediate effects of mechanical
perturbations during human walking (e.g., Bachmann et al.
2008; Berger et al. 1984; Chvatal and Ting 2012; Field-Fote
and Dietz 2007; Hof and Duysens 2013; Misiaszek et al. 2000;
Shinya et al. 2009; Stanek et al. 2011; Tang et al. 1998).
We hypothesized that humans actively control mediolateral
foot placement by modulating swing leg muscle activity and
base the target of this control on the mechanical state of the
stance leg and torso. Specifically, we expected that the swingphase activity of hip muscles that primarily act in the frontal
plane would control the mediolateral foot placement. We also
expected that the mechanical state of the stance leg (displacement, velocity, and acceleration of the CoM relative to the
stance foot) would predict the muscle activity in the contralateral swing leg.
375
376
A
NEUROMECHANICAL FOOT PLACEMENT STRATEGY
+
B
Sacrum
xswf marker
xCoM
C
Swing
Stance
Swing
Stance
Swing
heel
marker
Stance
heel
marker
Heel-strike
Heel-strike
Toe-off
D
Heel-strike
Toe-off
Heel-strike
Perturbation
Left Strides:
Right Strides:
Right heel-strike
R -1 step
L -1 stance
R 0 step
Right heel-strike
Left heel-strike
Right heel-strike
Left heel-strike
L -1 step
L 0 step
L -1 swing
L 0 stance
L 0 swing
R 0 stance
R 0 swing
R 1 stance
R 1 step
R 1 swing
Fig. 1. Spatiotemporal gait characteristics and patterns of muscle activity were quantified during Unperturbed and Perturbed walking. A: mediolateral swing foot
displacement (xswf) was measured as the mediolateral distance from the sacrum to the swing heel. Center of mass displacement (xCoM) was measured as the
mediolateral distance from the stance heel to the sacrum. For all steps, the positive direction was defined as the direction toward the swing foot. B: the gluteus
medius (GM) activity pattern during Unperturbed walking is illustrated for a typical participant. The GM was most strongly active during stance but also exhibited
a burst of activity during the first half of swing in some strides (boxed area). Individual strides (n ⫽ 250) are shown as thin lines, and the average stride is shown
as a thick dark line. C: Unperturbed adductor magnus (AM) activity is illustrated for a typical participant. The AM was consistently active in the second half
of swing (boxed area) and typically also exhibited smaller bursts of activity during early and late stance. Again, the thick dark line indicates the average of the
illustrated 250 strides. D: during Perturbed walking, periods of gait were classified on the basis of their timing relative to the perturbation, as illustrated in a simple
schematic. Steps were defined using heel-strike events from both legs (i.e., a right step was defined as the period from left heel strike to the next right heel strike).
Strides were defined using heel-strike events from a single leg (i.e., from left heel strike to the next left heel strike) and were divided into stance and swing phases
depending on whether this leg was on the ground. Perturbations were applied to the right leg during a right step (R0 step), while the left leg was in stance (L0
stance) and the right leg was in swing (R0 swing). Negatively labeled strides (e.g., L⫺1) occur before the perturbation, while positively labeled strides (e.g., R1)
occur after the perturbation.
Prior to data collection, we visually inspected the EMG data to
confirm that GM was most strongly active during isolated hip abduction contractions and AM was most strongly active during isolated hip
adduction contractions. The EMG data from each muscle were processed by high-pass filtering at 20 Hz, rectifying, and smoothing with
a low-pass filter at 50 Hz. EMG data were then divided into strides
based on heel-strike timing. The average EMG trace during a stride
was calculated for the Unperturbed walking trial (Fig. 1, B and C), and
all EMG data were normalized by the peak value of this average trace.
Data Analysis and Statistics: Unperturbed Walking
Data analysis was performed for both the right and left legs of each
participant (n ⫽ 20 legs) for the final 250 strides of the Unperturbed
walking trial.
Contributors to mediolateral foot placement. Our first goal was to
determine whether swing leg muscle activity predictably influenced
mediolateral foot placement. Based on the typical patterns of muscle
activity, average swing-phase GM activity was calculated during the
first half of swing (Fig. 1B) and average swing-phase AM activity was
calculated during the second half of swing (Fig. 1C). To account for
the initial mechanical state of the swing leg, we quantified the
mediolateral displacement, velocity, and acceleration of the swing
foot relative to the CoM at the time of toe-off. It should be noted that
the velocity values were dominated by motion of the CoM, as
mediolateral velocity of the foot is low at this point in the gait cycle.
Mediolateral foot placement was quantified as the mediolateral displacement of the foot relative to the CoM at the time of heel strike.
We performed a multiple linear regression to calculate the step-bystep contribution of each of the independent variables [GM swingphase activity (GMsw), AM swing-phase activity (AMsw), initial
swing foot displacement (xswf), initial swing foot velocity (vswf),
initial swing foot acceleration (aswf)] to the dependent variable (mediolateral foot placement). We included 250 strides per leg for each of
the 20 tested legs, for a total of 5,000 data points in the regression. To
account for differences in gait characteristics across individual legs
(e.g., different average foot placement locations or patterns of muscle
activation), we included the leg number in our regression as a
“dummy” indicator variable. Therefore, the results of the regression
focused on step-to-step differences in the measured variables, not
differences across individuals. To more easily interpret the regression
output, each data distribution was normalized by subtracting its mean
and dividing by its standard deviation (Schielzeth 2010). The standardized regression coefficient thus provides an indication of the
strength of the relationship. The variance inflation factors for this
regression were all ⬍1.5 (mean ⫽ 1.19), substantially smaller than the
value of 10 typically cited as an indicator of problematic multicollinearity (Belsey et al. 1980). P values of ⬍0.05 were interpreted as
significant. It should be noted that this statistical approach produces
identical results as analyzing the data with generalized estimation
equations with an equicorrelated structure.
Predictors of swing-phase muscle activity. Our second goal was to
determine whether swing-phase GM activity was predicted by the
state of the contralateral stance leg and torso. This goal was based on
our initial finding that swing-phase GM activity was the best predictor
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Left heel-strike
Steps:
NEUROMECHANICAL FOOT PLACEMENT STRATEGY
of mediolateral foot placement (see RESULTS for details). In comparison, swing-phase AM activity had only a minor effect on foot
placement. For each stride, we quantified average GM activity during
the first half of swing. During the same time period, we quantified the
average mediolateral displacement, velocity, and acceleration of the
CoM (relative to the contralateral stance foot) as well as GM activity
of the contralateral stance leg.
Following the methods described above, we performed a multiple
linear regression to calculate the contribution of each of the independent variables [CoM displacement (xCoM), CoM velocity (vCoM),
CoM acceleration (aCoM), contralateral stance-phase GM activity
(GMst)] to the dependent variable (ipsilateral swing-phase GM activity). As above, a total of 5,000 data points were included in the
regression, data distributions were normalized, all variance inflation
factors were ⬍1.5 (mean ⫽ 1.21), and P values of ⬍0.05 were
interpreted as significant.
During the Perturbed walking trials, steps and strides were classified on the basis of their timing relative to the perturbations (Fig. 1D).
The perturbations were delivered during a right step (R0 step), while
the right leg was in swing (R0 swing) and the left leg was in stance (L0
stance). Step timing values were used to quantify step period and foot
placement location at the end of each step. Stride timing values
(including the division into stance and swing) were used to define the
gait periods in which we calculated average muscle activity. Steps or
strides just prior to the perturbation (e.g., L⫺1) were considered to
represent steady-state behavior during the Perturbed trial. Subsequent
steps or strides were used to quantify the response to the perturbations
by comparing measurements of step period, mediolateral foot placement, GM activity during the swing phase, GM activity during the
stance phase, and AM activity during the swing phase. Lateral
perturbations and medial perturbations were analyzed separately.
To determine whether participants altered their steady-state gait
pattern in anticipation of the perturbations, we performed separate
ANOVAs with gait condition (Unperturbed walking, steady state
during Perturbed walking) as the independent variable and spatiotemporal gait characteristic (step period, mediolateral right foot placement, or mediolateral left foot placement) as the dependent variable.
The primary purpose of performing the Perturbed walking trials was
to test the causality of associations identified during the Unperturbed
walking trials. Therefore, we did not use similar regression methods to
analyze the Perturbed walking results. Instead, specific effects of the
perturbations were examined with a series of repeated-measures
one-way ANOVAs. To determine whether the perturbations influenced spatiotemporal gait characteristics, we performed separate
ANOVAs with stride classification as the independent variable and
0.2
0.1
*
*
0
-0.1
During both Unperturbed and Perturbed walking, swingphase GM activity was related to the subsequent mediolateral
foot placement. This swing-phase activity was predicted by the
state of the contralateral stance leg and torso, particularly by
the mediolateral position of the CoM relative to the stance foot.
These results provide direct evidence for a neuromechanical
strategy based on the swing-phase adjustment of mediolateral
foot placement used by humans to maintain gait stability.
Foot Placement Control During Unperturbed Walking
Contributors to mediolateral foot placement. Mediolateral
foot placement varied from step to step and was influenced by
swing-phase muscle activity (Fig. 2A). Swing-phase GM activity was the strongest predictor of mediolateral foot placement (P ⬍ 0.0001, standardized regression coefficient ␤ ⫽
0.32), as increased GM activity was associated with more
lateral foot placement. The effect of swing-phase AM activity
was smaller (P ⫽ 0.0006, ␤ ⫽ 0.05), with greater adductor
activity present during steps in which the foot was placed more
laterally.
Mediolateral foot placement was also influenced, albeit
more weakly, by the initial mechanical state of the swing leg
(Fig. 2A). Mediolateral foot placement was significantly associated with initial foot displacement (P ⫽ 0.0096, ␤ ⫽ ⫺0.04),
velocity (P ⬍ 0.0001, ␤ ⫽ ⫺0.08), and acceleration (P ⬍
0.0001, ␤ ⫽ 0.12). Therefore, the foot tended to be placed
more laterally if it began the step relatively close to the CoM,
if the CoM was initially moving relatively slowly toward the
contralateral stance foot, and if the foot was initially accelerating relatively slowly toward the CoM.
*
GMsw AMsw x swf
*
vswf
a swf
C
1.2
Swing
Stance
ML Distance (mm)
0.3
RESULTS
B
*
Norm. GM Activity
Regression Coefficient
A
right mediolateral foot placement, left mediolateral foot placement, or
step period as the dependent variable. To determine whether the
perturbations influenced muscle activity, we performed separate
ANOVAs with perturbation type (medial, lateral, or no perturbation)
as the independent variable and muscle activity (right stance-phase
GM activity, right swing-phase GM activity, left stance-phase GM
activity, left swing-phase GM activity, right swing-phase AM activity,
or left swing-phase AM activity) as the dependent variable. For each
of the ANOVAs described above, post hoc paired t-tests were performed where appropriate, with Bonferroni corrections for multiple
pairwise comparisons applied to the resultant P values. Corrected P
values of ⬍0.05 were interpreted as significant.
Active
Inactive
1
0.8
0.6
0.4
0.2
0
0
20
40
60
80
100
Swing
Stance
80
75
70
65
60
55
0
% Gait Cycle
Active
Inactive
20
40
More
lateral
60
80
100
% Gait Cycle
Fig. 2. Several measures of gait kinematics and muscle activity were associated with the subsequent foot placement. A: mediolateral foot placement was
significantly associated with swing-phase GM activity (GMsw), swing-phase AM activity (AMsw), and the initial mechanical state of the swing leg [xswf, swing
foot velocity (vswf), swing foot acceleration (aswf)]. Error bars represent SE. *Significant effect (P ⬍ 0.05). B: active strides were defined by early swing GM
activity (boxed area) exceeding 2 SDs above the minimal EMG level during a stride. Inactive strides were defined by early swing GM activity remaining within
1 SD of this minimal value. C: Active and Inactive strides were paired on the basis of the initial mechanical state of the swing leg (at ⬃60% gait cycle). The
mediolateral (ML) location of the swing foot is plotted with respect to the final position of the CoM. During Active strides, the swing foot landed more laterally.
For B and C, the lines represent averages and the shaded area represents 2 SDs around the average Inactive stride.
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Data Analysis and Statistics: Perturbed Walking
377
NEUROMECHANICAL FOOT PLACEMENT STRATEGY
Foot Placement Control During Perturbed Walking
Steady-state adaptation to perturbations. Participants
adapted their steady-state gait pattern in anticipation of perturbations. During strides defined as steady state (R⫺1, L⫺1),
perturbations had not been applied for at least four strides,
allowing participants to return to their typical steady-state gait
pattern. Steady-state foot placement with the perturbed (right)
leg was significantly (P ⫽ 0.0006) more lateral during the
Perturbed walking trial (86 ⫾ 23 mm; mean ⫾ SD) than during
1.2
0.3
0.6
0.1
*
0
xCoM
vCoM
*
aCoM
*
0.4
0.2
GMst
0
0
20
40
Active
Inactive
100
Active
Inactive
1
0.8
0.2
C
Swing
Stance
CoM Position (mm)
B
*
Norm. GM Activity
Regression Coefficient
A
the Unperturbed walking trial (72 ⫾ 24 mm). However, steadystate mediolateral foot placement with the unperturbed (left)
leg was not significantly (P ⫽ 0.66) different during the
Perturbed trial compared with the Unperturbed trial. The
steady-state step period during the Perturbed walking trial
(0.525 ⫾ 0.031 s; mean ⫾ SD) was slightly, but significantly
(P ⫽ 0.027), shorter than the step period during the Unperturbed walking trial (0.529 ⫾ 0.030 s).
Effects of mediolateral perturbations on gait mechanics.
Perturbations influenced the spatiotemporal characteristics of
gait, but only for two steps. The effects of these perturbations
on mediolateral motion are illustrated for a single participant in
Fig. 4A. Medial perturbations (Fig. 4A, top) directly caused the
right foot to be placed more medially at the end of the
perturbed step (R0 step), while having relatively small effects
on the position of the CoM. Therefore, the CoM was much
closer to the stance foot at the start of the next left step (L0
step). The left foot deviated from its normal trajectory during
this step, ending the step by being placed more medially. In
subsequent steps, foot placement locations appeared to return
to their typical values. Lateral perturbations (Fig. 4A, bottom)
caused the right foot to be placed more laterally, while again
having relatively minor effects on the CoM (during R0 step).
This had the effect of dramatically increasing the mediolateral
distance from the CoM to the new stance foot at the start of the
next left step (L0 step). Subsequently, the left foot was placed
more laterally relative to the CoM at the end of this step, before
the foot placement values returned to normal. Of note, participants were not strictly required to remain in the same mediolateral location on the treadmill on a step-by-step basis. For
example, after the medial perturbation the illustrated participant ended up walking slightly (⬃1 cm) to the right of their
original position.
The effects of the perturbations illustrated in Fig. 4A were
significant across participants. As expected, mediolateral foot
placement was significantly influenced by both medial (P ⬍
0.0001) and lateral (P ⬍ 0.0001) perturbations. Medial perturbations caused the perturbed right step to have a more medial
foot placement than during steady-state walking. The subsequent left step was also placed more medially than normal (Fig.
4B). Similarly, lateral perturbations caused both the perturbed
right step and the subsequent left step to have a more lateral
foot placement than during steady-state walking (Fig. 4B). Step
period was also significantly influenced by medial perturbations (P ⫽ 0.009) and lateral perturbations (P ⬍ 0.0001). Both
60
% Gait Cycle
80
100
D
Swing
1
0.8
80
0.6
60
40
0
40
Active
Inactive
0.4
Farther from
stance foot
20
Stance
1.2
0.2
60
% Gait Cycle
80
100
00
20
40
60
% Gait Cycle
80
100
Fig. 3. Characteristics of the stance leg and torso were associated with swing-phase GM activity in the contralateral leg. A: swing-phase GM activity was
significantly associated with mediolateral CoM displacement (xCoM), velocity (vCoM), and acceleration (aCoM), as well as by the simultaneous stance-phase GM
activity in the contralateral leg (GMst). Error bars represent SE. *Significant effect (P ⬍ 0.05). B: for a single participant, GM activity is illustrated for Active
and Inactive strides. C: the CoM is farther from the stance foot throughout the Active strides. D: GM activity of the contralateral stance leg is higher during the
Active strides than during the Inactive strides. For B–D, the lines represent averages and the shaded area represents 2 SDs around the average Inactive stride.
J Neurophysiol • doi:10.1152/jn.00138.2014 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 14, 2017
During steps with clear swing-phase GM activity, the leg
moved more laterally during swing and landed with a more
lateral foot placement. For illustrative purposes, the effects of
swing-phase GM activity during strides with similar initial
mechanical states of the swing leg are shown for a single
participant in Fig. 2, B and C. Some strides (termed Active)
exhibited a burst of GM activity during early swing, while in
other strides (termed Inactive) the GM was essentially silent
during this period (Fig. 2B; see figure legend for more detailed
definitions). During Active strides, the swing leg trajectories
diverged early in swing and remained more lateral upon foot
placement (Fig. 2C).
Predictors of gluteus medius activity during swing. Activation of the GM during swing (the strongest predictor of
mediolateral foot placement) was associated with the state of
the contralateral stance leg and torso (Fig. 3A). Swing-phase
GM activity was most strongly associated with the mediolateral CoM displacement relative to the stance foot (P ⬍ 0.0001,
␤ ⫽ 0.34) and more weakly associated with CoM velocity (P ⫽
0.024, ␤ ⫽ 0.03), CoM acceleration (P ⬍ 0.0001, ␤ ⫽ 0.06),
and contralateral stance leg GM activity (P ⬍ 0.0001, ␤ ⫽
0.08). Therefore, swing-phase GM activity tended to be higher
during steps in which the CoM was far from the stance foot and
moving and accelerating toward the stance foot slowly and the
contralateral GM was strongly active.
The swing leg GM was more strongly activated when the
CoM was farther from the stance foot. As illustrated for a
single participant, strides were again identified as either Active
or Inactive, depending on whether substantial GM activity was
present during early swing (Fig. 3B). During the Active strides,
the CoM was displaced farther mediolaterally from the stance
foot (Fig. 3C), indicating a more abducted stance leg. Additionally, during the Active strides the GM of the contralateral
stance leg was more strongly active (Fig. 3D), paralleling the
increased GM activity in the swing leg.
Norm. GM Activity
378
NEUROMECHANICAL FOOT PLACEMENT STRATEGY
R 0 step
L 0 step
R1 step L 1 step
R 2 step
Left foot
Lat.
Pert.
CoM
Right foot
Medial
None
Lateral
Step Period (s)
Left
0
Right
#
-100
-200
0.58
*
0.54
0.5
0.46
*
100
0
-100
-200
*
0.58
0.54
0.5
0.46
*
#
R-1 L -1 R0 L 0 R1 L 1 R2 L 2
R-1 L -1 R0 L 0 R1 L 1 R2 L 2
Time
Step number
Step number
Fig. 4. Perturbations influenced subsequent spatiotemporal gait characteristics. A: for a single participant, the effects of medial (top) and lateral (bottom)
perturbations on the mediolateral locations of the left foot, CoM, and right foot are illustrated over 6 consecutive steps. Each trace illustrates the average response
to the (⬃20) perturbations delivered during a trial. Exclamation marks (!) indicate the approximate time when perturbations were applied. Right steps are shaded,
and the vertical arrows indicate the measured mediolateral foot placements, defined as the mediolateral distance from the CoM to the foot at the time of heel
strike. For simplicity, each step is illustrated as having the same duration. B: mediolateral foot placement was predictably influenced by both medial and lateral
perturbations. The plotted values correspond to the average lengths of the vertical arrows illustrated in A across all participants. For clarity, left foot placement
is plotted as positive while right foot placement is plotted as negative. C: step period was affected by medial and lateral perturbations, as the perturbed step
consistently had a longer step period. For B and C, the vertical dashed line indicates the perturbed right step (R0), in which the foot is moved either medially
or laterally during swing. For each participant, we calculated the average values for these gait spatiotemporal metrics preceding and following the applied
perturbations. Here, the dot locations indicate mean values and the error bars represent SDs of these values across all 10 participants. *Magnitude significantly
greater than steady state, #magnitude significantly less than steady state (P ⬍ 0.05; post hoc t-tests).
Right
GM
R0
stance
R0
swing
R1
stance
R1
swing
R2
stance
R2
swing
Medial
None
Lateral
!
L -1
swing
Left
GM
50%
Norm
EMG
L0
stance
!
L0
swing
L1
stance
L1
swing
L2
stance
B
Norm. GM Activity
A
during the perturbed step, with increased activity in response to
medial perturbations (Fig. 5B, R0 swing). The effects of the
perturbations were even more apparent once the foot was
placed on the ground. The perturbations significantly (P ⬍
0.0001) influenced GM activity during the subsequent stance
phase on the right leg, as medial perturbations caused a
decrease in GM activity and lateral perturbations caused an
C
Norm. GM Activity
perturbation types caused the perturbed right step to have a
longer period, while lateral perturbations also caused the subsequent left step to have a shorter period (Fig. 4C).
Effects of mediolateral perturbations on muscle activity.
Perturbations influenced GM activity in the perturbed leg
(Fig. 5A). For the perturbed (right) leg, perturbations significantly (P ⫽ 0.0006) influenced swing-phase GM activity
Medial
0.6
0.4
#
*
Lateral
*
*
0.2
0
R0 swing
R1 stance
*
0.6
0.4
R1 swing
*
#
0.2
0
Time
None
L 0 stance
L 0 swing
L 1 stance
Fig. 5. Perturbations also influenced bilateral GM activity. A: for a single participant, the muscle activity during 3 consecutive right strides is illustrated for both
legs, corresponding to the same time period illustrated in Fig. 4. Again, each trace illustrates the average response to the applied perturbations. For each leg, stance
phases are indicated by shaded areas. Perturbations (indicated by exclamation marks) were delivered to the right leg when this leg was in swing (R0 swing) and
the left leg was in stance (L0 stance). B: across participants, perturbations influenced right GM activity during both the perturbed stride and the subsequent stride.
Generally, lateral and medial perturbations had opposite effects. C: perturbations also influenced the left GM activity. Most notably, after perturbations of the
right leg the next swing phase with the left leg demonstrated substantial changes in GM activity. B and C: *significant increase in muscle activity relative to strides
without a perturbation, #significant decrease (P ⬍ 0.05; post hoc t-tests).
J Neurophysiol • doi:10.1152/jn.00138.2014 • www.jn.org
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100 mm
!
#
Step Period (s)
L -1 step
Foot Placement (mm)
!
Right foot
100
Left
CoM
C
Right
Med.
Pert.
B
Left foot
Foot Placement (mm)
A
379
380
NEUROMECHANICAL FOOT PLACEMENT STRATEGY
DISCUSSION
The aim of this experiment was to identify a neuromechanical foot placement control strategy that uninjured humans use
to maintain lateral stability while walking. We used a combination of unperturbed and perturbed walking trials to relate
movement kinematics to neural control in the form of muscle
activation. Supporting our hypothesis, we found that swingphase GM activity predictably influenced mediolateral foot
placement, and that this swing-phase activity was predicted by
the mechanical state of the stance leg and torso.
The present results suggest that humans use a consistent
mediolateral foot placement strategy to ensure their stability
during both unperturbed and perturbed walking. During Unperturbed walking trials, we identified significant associations
between gait kinematics (e.g., mediolateral location of the
CoM) and active control (e.g., swing-phase GM activity) by
taking advantage of natural step-to-step variability in these
measures. However, such regression analyses cannot determine
causality, as both CoM location and swing-phase muscle activity could conceivably be adjusted in parallel by some other
independent factor (e.g., altered feedforward descending commands). The Perturbed walking trials permitted us to test the
causal relationships suggested by the Unperturbed associations. The applied perturbations altered the mediolateral location of the CoM relative to the new stance foot, allowing us to
quantify the subsequent effects on contralateral swing-phase
GM activity and foot placement. During both Unperturbed and
Perturbed walking trials, an increase in the CoM displacement
relative to the stance foot was accompanied by increased
contralateral swing-phase GM activity. Increased swing-phase
GM activity was followed by more lateral foot placement for
both types of walking trials. In contrast, swing-phase AM
activity had only a relatively small effect on foot placement
during Unperturbed walking and was not consistently influenced by the applied perturbations during Perturbed walking.
This modulation of GM activity to control foot placement, and
the relatively minor role of AM activity, are consistent with the
results of recent experiments in which lateral pushes were
applied to the CoM during walking (Hof and Duysens 2013).
Importantly, our results indicate that a similar stabilization
strategy is present even in the absence of external perturbations.
Unlike previous experiments in which the CoM or the stance
leg was directly perturbed (Chvatal and Ting 2012; Hof et al.
2010; Hof and Duysens 2013; Oddsson et al. 2004), our
perturbations mimicked the mechanical effects of foot placement errors that could be produced by inaccurate mediolateral
control of the swing leg. These perturbations influenced the
CoM mediolateral displacement relative to the stance foot at
the start of the next step, essentially having the effect of
introducing large errors in foot placement. While such perturbations are not as commonly investigated as slips, trips, or
pushes, a recent observational video study in a long-term care
setting found that internal perturbations in the form of incorrect
weight shifting (including improperly placed steps) were the
most frequent cause of falls (Robinovitch et al. 2013). Interestingly, in the present study the amplitude of the medial
perturbations was consistently smaller than the amplitude of
the lateral perturbations. We believe that this is likely due to
participants resisting the medial perturbations despite instructions not to, as evidenced by increased GM activity during the
perturbed swing phase. Additionally, the involved experimenter may have subconsciously reduced the amplitude of the
applied medial perturbations because of the perception that
these perturbations were more destabilizing.
Our results provide direct experimental evidence for a neuromechanical strategy used to actively control lateral stability
during human walking. Within each stride, the swing phase
allows control over the next point of contact with the environment. Such a foot placement strategy permits center of pressure
adjustments an order of magnitude larger than those possible
through control of the stance leg ankle (Hof et al. 2010).
During the swing phase, humans modulate hip muscle activity
to control the mediolateral trajectory of the swing leg, as
increased abductor activity early in swing causes the leg to
move more laterally. While this muscle action is logical,
standard gait analysis textbooks typically describe the GM as
being inactive during this phase of gait (Perry 1992; Vaughan
et al. 1999; Winter 1987). The present results suggest that GM
activity during early swing, while small compared with stancephase activity, is an important contributor to the production of
stable gait patterns even during unperturbed walking. The
slight increase in adductor activity observed with more lateral
foot placement during Unperturbed walking is likely a result of
using this muscle to slow the swing leg prior to the impact at
heel strike. A similar increase in both abductor and adductor
activity with more lateral steps was previously observed in
walking cats (Karayannidou et al. 2009).
The swing leg’s initial mechanical state (displacement, velocity, and acceleration) influenced its eventual placement
location, although not as strongly as GM activity. This influence may be partially attributable to passive mechanics, as
pendular movement of the swing leg would be expected to
depend on its initial state. The effect of the initial mechanical
state could help to explain some aspects of swing leg motion
during the Perturbed trials that may otherwise seem confusing.
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increase in GM activity (Fig. 5B, R1 stance). Perturbations also
had a significant (P ⫽ 0.013) effect on the next step with the
right foot, as lateral perturbations caused this step to exhibit
increased swing-phase GM activity (Fig. 5B, R1 swing), before
returning to baseline.
Of perhaps greater interest, perturbations also influenced
GM activity in the unperturbed leg (Fig. 5A). For the unperturbed (left) leg, perturbations significantly (P ⫽ ⬍0.0001)
influenced swing-phase GM activity during the next left step.
Medial perturbations of the right leg decreased the mediolateral
displacement of the CoM relative to the new stance foot (see
Fig. 4), which in turn caused a decrease in swing-phase GM
activity of the contralateral left leg (Fig. 5C, L0 swing).
Conversely, lateral perturbations of the right leg increased the
mediolateral displacement of the CoM relative to the new
stance foot, and subsequently increased swing-phase GM activity in the left leg (Fig. 5C, L0 swing). The perturbations also
significantly (P ⫽ 0.023) influenced GM activity of the unperturbed leg during the subsequent stance phase. Specifically,
lateral perturbations increased this contralateral stance-phase
GM activity (Fig. 5C, L1 stance). In contrast to the clear results
observed with bilateral GM activity, the perturbations did not
have a significant effect (P ⬎ 0.10) on subsequent swing-phase
AM activity for either leg.
NEUROMECHANICAL FOOT PLACEMENT STRATEGY
The proposed neuromechanical gait stabilization strategy
requires humans to develop a perception of the mechanical
state of their stance leg and torso. Our finding that the mediolateral displacement of the CoM plays a major role in the active
control of foot placement provides further support for the
proposed importance of CoM mechanics in motor control
(Scholz and Schoner 1999). While the body may not have
specific receptors to sense CoM mechanics, humans are able to
generate novel perceptual representations of the body’s state
through multisensory integration (Green and Angelaki 2010).
Future experiments could extend previous investigations of
sensory integration during standing posture (Bingham et al.
2011; Goodworth and Peterka 2012) to walking. Several types
of sensory feedback may contribute to the perceptual representation of CoM mechanics during gait, including proprioceptive
(e.g., spindle and Golgi tendon organ) feedback from the
stance hip (Popov et al. 1999), knee (Cammarata and Dhaher
2011), or ankle (Kavounoudias et al. 1999), cutaneous feedback from the sole of the stance foot (Kavounoudias et al.
2001), vestibular feedback (Bent et al. 2005), or visual feedback (O’Connor and Kuo 2009). While less well understood,
the sense of effort (McCloskey et al. 1974) required for the
stance leg abductors to support the pelvis may also contribute
to the perception of CoM mechanics, which could explain the
observed relationship between the simultaneous GM activity in
the stance and swing legs. Importantly, neural circuits have the
potential to differentiate and integrate sensory signals over
time (Aksay et al. 2001), so our observed primary importance
of CoM displacement does not necessarily implicate sensory
sources that directly provide positional information (e.g., secondary muscle spindles). In clinical populations, functional
gait could be limited by inaccuracy in any of the sensory
modalities described above, or by a reduced ability to integrate
this information.
Variation in mediolateral foot placement appears to be both
a cause of and a solution to lateral instability during human
walking. Our results indicate that errors in mediolateral foot
placement require clear subsequent adjustments in active control, which could be interpreted as suggesting that variability in
foot placement should be minimized to prevent the need for
such corrections. However, the corrections themselves often
take the form of adjustments to mediolateral foot placement,
suggesting that step-to-step variability is necessary. For example, externally applied lateral foot placement perturbations (the
error) are followed by more lateral foot placement with the
opposite leg (the correction). This is not a novel concept, as
both abnormally low and abnormally high step width variability have previously been associated with increased fall risk
(Brach et al. 2005). Additionally, recent work has begun to
categorize variability of frontal-plane gait motions as “good
variance” or “bad variance” with uncontrolled manifold analyses of kinematic measures (Krishnan et al. 2013; Rosenblatt et
al. 2014). The present results support the proposal that some
variation in mediolateral foot placement can be a beneficial
response to altered CoM motion and provide direct evidence
that such “good variance” is actively controlled by the nervous
system through appropriate muscle recruitment.
The anticipatory gait adaptations observed during Perturbed
walking trials were indicative of a more conservative gait
strategy, suggesting that participants were less confident in
their foot placement. When anticipating perturbations, partici-
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For example, after a lateral perturbation of the right leg, the
small change in mediolateral CoM position caused the left foot
to begin its swing phase slightly farther from the CoM (see Fig.
4A). Based on our Unperturbed associations, we would expect
this to lead to more medial movement of the left leg. Indeed,
after lateral perturbations we commonly observed the left leg to
move more medially during midswing (see Fig. 4A). The final
foot placement in a more lateral location can be attributed to
the intervening increased swing-phase GM activity. The opposite effect was often seen after medial perturbations. The
influence of the initial mechanical state of the swing leg could
also explain why we observed increased swing-phase GM
activity during stride R1 but this did not cause more lateral foot
placement; the initial large displacement of the right foot
relative to the CoM may have been sufficient to cancel out any
effects of increased GM activity.
Simultaneous with the active swing leg control, humans
must identify a mechanically appropriate foot placement target
for the ongoing step. Unlike typical upper extremity reaching
tasks, the ideal end point is not entirely determined by environmental constraints. Additionally, accurately moving the
swing leg to the identical frontal-plane angle for each step
would not allow for adjustments to variation in CoM motion.
Instead, the mediolateral foot placement must account for the
mechanical state of the body in order to redirect the CoM back
toward the midline (Hof et al. 2010). We found that humans
base their active control of mediolateral foot placement on the
mechanical state of the stance leg and torso, specifically the
mediolateral displacement of the CoM from the stance foot.
Similar strategies have previously been proposed for efficient
stabilization in model simulations (Kuo 1999) and have successfully stabilized bipedal walking robots (Hobbelen and
Wisse 2009). Additionally, swing-phase activity and subsequent foot placement are predictably influenced by the state of
the stance leg in walking cats (Musienko et al. 2012a, 2012b).
However, direct evidence for this type of mechanics-dependent
active neural control has not previously been demonstrated in
unperturbed walking humans.
While humans appear to partially control their mediolateral
foot placement by modulating swing-phase abductor and adductor muscle activity, the sensory information used to execute
this control is unclear. Humans may control the mediolateral
trajectory of the swing leg by monitoring proprioceptive feedback, as occurs during accurate upper extremity movements
(Sarlegna and Sainburg 2009). For example, muscle spindle
feedback from the swing leg hip musculature could be used by
the central nervous system to estimate frontal-plane hip position, followed by appropriate muscle activation to minimize
errors between the sensed and desired positions. Future experiments could provide insight into the neural mechanism underlying swing leg control by investigating whether foot placement is influenced by perturbing proprioceptive feedback from
the swing leg, such as by applying muscle vibration to activate
muscle spindles (Roll and Vedel 1982). In clinical populations,
gait function may be limited by an inability to modulate hip
abductor and adductor activity during swing to control foot
placement. Indeed, an inability to quickly recruit the GM has
previously been associated with fall risk (Brauer et al. 2000).
Function could also be limited by an inability to sense the
position of the swing leg, as proprioception accuracy at the hip
is reduced in many stroke patients (Connell et al. 2008).
381
382
NEUROMECHANICAL FOOT PLACEMENT STRATEGY
GRANTS
This study was partially supported by the Department of Veterans Affairs,
Office of Research and Development, Rehabilitation Research and Development Service through Grant 1IK2RX000750-01A1. This study was also
partially supported by the National Institute of Child Health and Human
Development through Grant 1R21 HD-064964-01A1.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
The contents of this manuscript do not represent the views of the Department
of Veterans Affairs or the United States Government.
AUTHOR CONTRIBUTIONS
Author contributions: B.L.R., S.K.B., and J.C.D. performed experiments;
B.L.R., S.K.B., and J.C.D. analyzed data; B.L.R., S.K.B., and J.C.D. interpreted results of experiments; B.L.R., S.K.B., and J.C.D. drafted manuscript;
B.L.R., S.K.B., and J.C.D. edited and revised manuscript; B.L.R., S.K.B., and
J.C.D. approved final version of manuscript; J.C.D. conception and design of
research; J.C.D. prepared figures.
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