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1.Motor Patterns in Human Walking and Running
G. Cappellini, Y. P. Ivanenko, R. E. Poppele and F. Lacquaniti
J Neurophysiol 95:3426-3437, 2006. First published Mar 22, 2006; doi:10.1152/jn.00081.2006
You might find this additional information useful...
This article cites 65 articles, 27 of which you can access free at:
http://jn.physiology.org/cgi/content/full/95/6/3426#BIBL
This article has been cited by 1 other HighWire hosted article:
[Full Text] [PDF]
J. Exp. Biol., October 1, 2006; 209 (19): iv-iv.
E. Tytell
ONE PROGRAM FOR WALKING AND RUNNING
Updated information and services including high-resolution figures, can be found at:
http://jn.physiology.org/cgi/content/full/95/6/3426
Additional material and information about Journal of Neurophysiology can be found at:
http://www.the-aps.org/publications/jn
This information is current as of November 21, 2006 .
2.Motor Patterns in Human Walking and Running
G. Cappellini,1 Y. P. Ivanenko,1 R. E. Poppele,2 and F. Lacquaniti1,3,4
1Department of Neuromotor Physiology, Scientific Institute Foundation Santa Lucia, Rome, Italy; 2Department of Neuroscience,
University of Minnesota, Minneapolis, Minnesota; and 3Department of Neuroscience and 4Centre of Space Bio-medicine,
University of Rome Tor Vergata, Rome, Italy
Submitted 24 January 2006; accepted in final form 1 March 2006
Cappellini, G., Y. P. Ivanenko, R. E. Poppele, and F. Lacquaniti.
Motor patterns in human walking and running. J Neurophysiol 95:
3426–3437, 2006; doi:10.1152/jn.00081.2006. Despite distinct differences
between walking and running, the two types of human locomotion
are likely to be controlled by shared pattern-generating networks.
However, the differences between their kinematics and kinetics imply
that corresponding muscle activations may also be quite different. We
examined the differences between walking and running by recording
kinematics and electromyographic (EMG) activity in 32 ipsilateral
limb and trunk muscles during human locomotion, and compared the
effects of speed (3–12 km/h) and gait. We found that the timing of
muscle activation was accounted for by five basic temporal activation
components during running as we previously found for walking. Each
component was loaded on similar sets of leg muscles in both gaits but
generally on different sets of upper trunk and shoulder muscles. The
major difference between walking and running was that one temporal
component, occurring during stance, was shifted to an earlier phase in
the step cycle during running. These muscle activation differences
between gaits did not simply depend on locomotion speed as shown
by recordings during each gait over the same range of speeds (5–9
km/h). The results are consistent with an organization of locomotion
motor programs having two parts, one that organizes muscle activation
during swing and another during stance and the transition to
swing. The timing shift between walking and running reflects therefore
the difference in the relative duration of the stance phase in the
two gaits.
I N T R O D U C T I O N
Walking and running are generally considered as distinct
gait modes with strikingly different mechanics and energetics.
Humans change gait to increase locomotion speed while saving
energy. Thus oxygen consumption is lower for walking than
for running below the transition speed, whereas it is higher
above this speed (Margaria 1976). In walking, the body vaults
up and over each stiff leg in an arc, analogous to an inverted
pendulum (Cavagna et al. 1976) (Fig. 1A). Kinetic energy in
the first half of the stance phase is transformed into gravitational
potential energy, which is partially recovered as the body
falls forward and downward in the second half of the stance
phase. Running, instead, is analogous to bouncing on a pogo
stick (Full and Koditschek 1999; Raibert 1986) (Fig. 1A). As a
leg strikes the ground, kinetic and gravitational potential energy
is temporarily stored as elastic strain energy in muscles,
tendons, and ligaments and then is nearly all recovered during
the propulsive second half of the stance phase. The walking
gait may also be defined by the existence of a double support
phase during stance, whereas running has a “flight” phase
during which neither limb is in ground contact.
Walking and running are the two most common forms of
human gait. Although they share some basic kinetics and
kinematics, the two gaits are also distinctly different so the
transition from walking to running is obvious. In fact, both
kinematics and kinetics change abruptly in going from a
walking gait to a running gait (Hreljac 1993; Minetti et al.
1994; Nilsson et al. 1985). For example, the gait transition is
accompanied by an abrupt decrease in ground contact time by
35% and an 50% increase in peak ground reaction force.
There are also a number of gait parameters that change monotonically
with increasing speed during both walking and running,
including increased step length and cycle duration and
decreased stance duration (Nilsson et al. 1985). Many of these
changes are associated with increasing intensity of muscle
activation (Ivanenko et al. 2006; Prilutsky and Gregor 2001;
Winter and Yack 1987).
The mechanisms that underlie the changes associated with
speed and gait changes are still not well understood. In general,
the mechanics of locomotion and the associated muscle activity
have been more thoroughly studied in the human than has the
neural control. For the latter, we must rely largely on extrapolations
from animal models. A number of experimental findings
from animals and humans suggest that different locomotion
patterns may result primarily from peripheral factors, like
muscle performance or sensory feedback (Pearson 2004; Smith
et al. 1993) and/or by a reconfiguration of the neural network
(Gillis and Biewener 2001; Grillner 1981) or by modulating the
parameters of a basic network (Collins 2003; de Leon et al.
1994; Golubitsky et al. 1999; Grillner and Zangger 1979;
Orlovsky et al. 1999; Pribe et al. 1997).
An important result from the animal studies was the demonstration
that the entire range of speeds and locomotion gaits
can be generated in the cat by varying only the intensity of
stimulation of the mesencephalic locomotor region (MLR)
(Mori et al. 1989; Shik 1983; Shik et al. 1966). The gait
determined by this “central command” was automatically
adapted to the external conditions (such as slope of the road or
body loading) by “peripheral” mechanisms. Increasing only the
strength of the MLR stimulation could increase the speed of
forward progression, and at stronger stimulation levels, the
animal changed the locomotion gait from out-of-phase coordination
(walk or trot) to in-phase coordination (run or gallop).
Areas anatomically similar to the MLR in the cat have also
Address for reprint requests and other correspondence: Y. P. Ivanenko,
Dept. of Neuromotor Physiology, Scientific Institute Foundation Santa Lucia,
306 via Ardeatina, 00179 Rome, Italy (E-mail: [email protected]).
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.
3.combined with various voluntary motor tasks that required
significantly different muscle usage (Ivanenko et al., 2005). If
there is a common set of oscillators for walking and running,
we might anticipate that muscle activation during running
might also be accounted for, at least in part, by the same basic
muscle activation components. If, however, separate sets of
oscillators underlie the different gaits, we might expect that
this would be associated with distinctly different timings of
muscle activation. Although the analysis of individual EMG
patterns of human running has been performed in numerous
studies (see Prilutsky and Gregor 2001), the underlying common
structure of the motor output has not been previously
investigated.
Therefore the aim of this study was to examine how muscle
activation depends on locomotion speed and on locomotion
gait. The experimental design was to record kinematics and
EMG activity from human subjects as they either walked or ran
on a treadmill at each of several different speeds. We used
statistical methods incorporating a linear decomposition of the
EMG data to determine the general design of the motor output
in walking and running.
M E T H O D S
Subjects
Eight healthy subjects [6 males and 2 females, between 26 and 44
yr of age, 69 10 kg (mean SD), 1.75 0.07 m] volunteered for
the experiments. All subjects were right-leg dominant. The studies
conformed to the Declaration of Helsinki, and informed consent was
obtained from all participants according to the procedures of the
Ethics Committee of the Santa Lucia Institute.
Experimental setup
Subjects walked or ran on a treadmill (EN-MILL 3446.527, Bonte
Zwolle BV, Netherlands) at different controlled speeds (3–12 km/h).
They were asked to swing their arms normally and to look straight
ahead. Subjects walked with their shoes on. Before the recording
session, subjects practiced for a few minutes in walking and running
on the treadmill at different speeds. In a standard protocol, subjects
were asked to walk at 3, 5, 7, and 9 km/h and to run at 5, 7, 9, and 12
km/h so that we could compare walking and running at the same
speeds (5, 7, and 9 km/h).
We also used a computer-controlled speed program that linearly
increased and then decreased treadmill speed between 1 and 12 km/h
to observe the recorded parameters during continuous speed changes
(ramp speed condition, acceleration, and deceleration was set to 0.4
km h1 s1). Three subjects participated in this experiment. They
were instructed to follow the changes in speed by remaining in place
with respect to the treadmill when the belt velocity was changing. We
also recorded during overground walking [at 5.6 1.1 (SD) km/h]
and running (at 9.6 0.9 km/h). This allowed us to calculate the
moments of forces (see following text) by asking subjects to step on
a force plate located in the middle of a 7-m walkway.
Data recording
We recorded kinematic data bilaterally at 100 Hz by means of the
Vicon-612 system (Oxford, UK) with nine TV cameras spaced around
the walkway. Infrared reflective markers (diameter: 1.4 cm) were
attached on each side of the subject to the skin overlying the following
landmarks: gleno-humeral joint (GH), the midpoint between the
anterior and the posterior superior iliac spine (ilium, IL), greater
trochanter (GT), lateral femur epicondyle (LE), lateral malleolusFIG. 1. General characteristics of walking and running. A: schematic representation
of walking by vaulting (inverted pendulum) and running by a
“bouncing” gait (leg spring-loaded behavior during stance). B: ankle, knee, and
hip moments of force and vertical ground reaction force (normalized to the
subject’s weight) of the right leg during overground walking (5.4 km/h) and
running (9.4 km/h) in 1 representative subject. C: relative stance duration and
cycle duration (SD) in walking and running. D: foot (fifth metatarsophalangeal
joint, VM) trajectory characteristics (mean SD, normalized to the
limb length L): horizontal excursion of the VM marker [relative to greater
trochanter (GT)] and vertical VM displacements (in the laboratory reference
frame).been found to serve a similar function in fish, reptiles, birds,
and primates (Jordan 1991; Mori et al. 1996).
The question remains, however, as to whether this descending
control modulates a basic “motor program” for walking and
running (for instance, a set of nonlinear oscillators with bifurcations
at critical transitional points) or a separate set of
oscillators for each distinct gait. In either case, a gait transition
might be subject to feedback concerning critical peripheral
factors. A recent line of investigation relevant to the neural
generation of locomotor patterns is based on the identification
of elementary units of muscle activation (Bizzi et al. 1991,
2002; Davis and Vaughan 1993; d’Avella and Bizzi 2005;
Giszter et al. 2001; Hart and Giszter 2004; Hultborn 2001;
Ivanenko et al. 2003; Kargo and Giszter 2000; Patla et al. 1985;
Ting and Macpherson 2005; Tresch and Bizzi 1999). According
to this approach, locomotor programs may be considered as
a set of characteristic timings of muscle activation (Ivanenko et
al. 2005). In fact, the same five basic activation components
can account for the electromyographic (EMG) waveforms of
32 ipsilateral leg, trunk, and shoulder muscles during walking
at speeds between 1 and 5 km/h (Ivanenko et el., 2004b). The
same five components were also present when walking was
1.Motor Patterns in Human Walking and Running
G. Cappellini, Y. P. Ivanenko, R. E. Poppele and F. Lacquaniti
J Neurophysiol 95:3426-3437, 2006. First published Mar 22, 2006; doi:10.1152/jn.00081.2006
You might find this additional information useful...
This article cites 65 articles, 27 of which you can access free at:
http://jn.physiology.org/cgi/content/full/95/6/3426#BIBL
This article has been cited by 1 other HighWire hosted article:
[Full Text] [PDF]
J. Exp. Biol., October 1, 2006; 209 (19): iv-iv.
E. Tytell
ONE PROGRAM FOR WALKING AND RUNNING
Updated information and services including high-resolution figures, can be found at:
http://jn.physiology.org/cgi/content/full/95/6/3426
Additional material and information about Journal of Neurophysiology can be found at:
http://www.the-aps.org/publications/jn
This information is current as of November 21, 2006 .
2.Motor Patterns in Human Walking and Running
G. Cappellini,1 Y. P. Ivanenko,1 R. E. Poppele,2 and F. Lacquaniti1,3,4
1Department of Neuromotor Physiology, Scientific Institute Foundation Santa Lucia, Rome, Italy; 2Department of Neuroscience,
University of Minnesota, Minneapolis, Minnesota; and 3Department of Neuroscience and 4Centre of Space Bio-medicine,
University of Rome Tor Vergata, Rome, Italy
Submitted 24 January 2006; accepted in final form 1 March 2006
Cappellini, G., Y. P. Ivanenko, R. E. Poppele, and F. Lacquaniti.
Motor patterns in human walking and running. J Neurophysiol 95:
3426–3437, 2006; doi:10.1152/jn.00081.2006. Despite distinct differences
between walking and running, the two types of human locomotion
are likely to be controlled by shared pattern-generating networks.
However, the differences between their kinematics and kinetics imply
that corresponding muscle activations may also be quite different. We
examined the differences between walking and running by recording
kinematics and electromyographic (EMG) activity in 32 ipsilateral
limb and trunk muscles during human locomotion, and compared the
effects of speed (3–12 km/h) and gait. We found that the timing of
muscle activation was accounted for by five basic temporal activation
components during running as we previously found for walking. Each
component was loaded on similar sets of leg muscles in both gaits but
generally on different sets of upper trunk and shoulder muscles. The
major difference between walking and running was that one temporal
component, occurring during stance, was shifted to an earlier phase in
the step cycle during running. These muscle activation differences
between gaits did not simply depend on locomotion speed as shown
by recordings during each gait over the same range of speeds (5–9
km/h). The results are consistent with an organization of locomotion
motor programs having two parts, one that organizes muscle activation
during swing and another during stance and the transition to
swing. The timing shift between walking and running reflects therefore
the difference in the relative duration of the stance phase in the
two gaits.
I N T R O D U C T I O N
Walking and running are generally considered as distinct
gait modes with strikingly different mechanics and energetics.
Humans change gait to increase locomotion speed while saving
energy. Thus oxygen consumption is lower for walking than
for running below the transition speed, whereas it is higher
above this speed (Margaria 1976). In walking, the body vaults
up and over each stiff leg in an arc, analogous to an inverted
pendulum (Cavagna et al. 1976) (Fig. 1A). Kinetic energy in
the first half of the stance phase is transformed into gravitational
potential energy, which is partially recovered as the body
falls forward and downward in the second half of the stance
phase. Running, instead, is analogous to bouncing on a pogo
stick (Full and Koditschek 1999; Raibert 1986) (Fig. 1A). As a
leg strikes the ground, kinetic and gravitational potential energy
is temporarily stored as elastic strain energy in muscles,
tendons, and ligaments and then is nearly all recovered during
the propulsive second half of the stance phase. The walking
gait may also be defined by the existence of a double support
phase during stance, whereas running has a “flight” phase
during which neither limb is in ground contact.
Walking and running are the two most common forms of
human gait. Although they share some basic kinetics and
kinematics, the two gaits are also distinctly different so the
transition from walking to running is obvious. In fact, both
kinematics and kinetics change abruptly in going from a
walking gait to a running gait (Hreljac 1993; Minetti et al.
1994; Nilsson et al. 1985). For example, the gait transition is
accompanied by an abrupt decrease in ground contact time by
35% and an 50% increase in peak ground reaction force.
There are also a number of gait parameters that change monotonically
with increasing speed during both walking and running,
including increased step length and cycle duration and
decreased stance duration (Nilsson et al. 1985). Many of these
changes are associated with increasing intensity of muscle
activation (Ivanenko et al. 2006; Prilutsky and Gregor 2001;
Winter and Yack 1987).
The mechanisms that underlie the changes associated with
speed and gait changes are still not well understood. In general,
the mechanics of locomotion and the associated muscle activity
have been more thoroughly studied in the human than has the
neural control. For the latter, we must rely largely on extrapolations
from animal models. A number of experimental findings
from animals and humans suggest that different locomotion
patterns may result primarily from peripheral factors, like
muscle performance or sensory feedback (Pearson 2004; Smith
et al. 1993) and/or by a reconfiguration of the neural network
(Gillis and Biewener 2001; Grillner 1981) or by modulating the
parameters of a basic network (Collins 2003; de Leon et al.
1994; Golubitsky et al. 1999; Grillner and Zangger 1979;
Orlovsky et al. 1999; Pribe et al. 1997).
An important result from the animal studies was the demonstration
that the entire range of speeds and locomotion gaits
can be generated in the cat by varying only the intensity of
stimulation of the mesencephalic locomotor region (MLR)
(Mori et al. 1989; Shik 1983; Shik et al. 1966). The gait
determined by this “central command” was automatically
adapted to the external conditions (such as slope of the road or
body loading) by “peripheral” mechanisms. Increasing only the
strength of the MLR stimulation could increase the speed of
forward progression, and at stronger stimulation levels, the
animal changed the locomotion gait from out-of-phase coordination
(walk or trot) to in-phase coordination (run or gallop).
Areas anatomically similar to the MLR in the cat have also
Address for reprint requests and other correspondence: Y. P. Ivanenko,
Dept. of Neuromotor Physiology, Scientific Institute Foundation Santa Lucia,
306 via Ardeatina, 00179 Rome, Italy (E-mail: [email protected]).
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.
3.combined with various voluntary motor tasks that required
significantly different muscle usage (Ivanenko et al., 2005). If
there is a common set of oscillators for walking and running,
we might anticipate that muscle activation during running
might also be accounted for, at least in part, by the same basic
muscle activation components. If, however, separate sets of
oscillators underlie the different gaits, we might expect that
this would be associated with distinctly different timings of
muscle activation. Although the analysis of individual EMG
patterns of human running has been performed in numerous
studies (see Prilutsky and Gregor 2001), the underlying common
structure of the motor output has not been previously
investigated.
Therefore the aim of this study was to examine how muscle
activation depends on locomotion speed and on locomotion
gait. The experimental design was to record kinematics and
EMG activity from human subjects as they either walked or ran
on a treadmill at each of several different speeds. We used
statistical methods incorporating a linear decomposition of the
EMG data to determine the general design of the motor output
in walking and running.
M E T H O D S
Subjects
Eight healthy subjects [6 males and 2 females, between 26 and 44
yr of age, 69 10 kg (mean SD), 1.75 0.07 m] volunteered for
the experiments. All subjects were right-leg dominant. The studies
conformed to the Declaration of Helsinki, and informed consent was
obtained from all participants according to the procedures of the
Ethics Committee of the Santa Lucia Institute.
Experimental setup
Subjects walked or ran on a treadmill (EN-MILL 3446.527, Bonte
Zwolle BV, Netherlands) at different controlled speeds (3–12 km/h).
They were asked to swing their arms normally and to look straight
ahead. Subjects walked with their shoes on. Before the recording
session, subjects practiced for a few minutes in walking and running
on the treadmill at different speeds. In a standard protocol, subjects
were asked to walk at 3, 5, 7, and 9 km/h and to run at 5, 7, 9, and 12
km/h so that we could compare walking and running at the same
speeds (5, 7, and 9 km/h).
We also used a computer-controlled speed program that linearly
increased and then decreased treadmill speed between 1 and 12 km/h
to observe the recorded parameters during continuous speed changes
(ramp speed condition, acceleration, and deceleration was set to 0.4
km h1 s1). Three subjects participated in this experiment. They
were instructed to follow the changes in speed by remaining in place
with respect to the treadmill when the belt velocity was changing. We
also recorded during overground walking [at 5.6 1.1 (SD) km/h]
and running (at 9.6 0.9 km/h). This allowed us to calculate the
moments of forces (see following text) by asking subjects to step on
a force plate located in the middle of a 7-m walkway.
Data recording
We recorded kinematic data bilaterally at 100 Hz by means of the
Vicon-612 system (Oxford, UK) with nine TV cameras spaced around
the walkway. Infrared reflective markers (diameter: 1.4 cm) were
attached on each side of the subject to the skin overlying the following
landmarks: gleno-humeral joint (GH), the midpoint between the
anterior and the posterior superior iliac spine (ilium, IL), greater
trochanter (GT), lateral femur epicondyle (LE), lateral malleolusFIG. 1. General characteristics of walking and running. A: schematic representation
of walking by vaulting (inverted pendulum) and running by a
“bouncing” gait (leg spring-loaded behavior during stance). B: ankle, knee, and
hip moments of force and vertical ground reaction force (normalized to the
subject’s weight) of the right leg during overground walking (5.4 km/h) and
running (9.4 km/h) in 1 representative subject. C: relative stance duration and
cycle duration (SD) in walking and running. D: foot (fifth metatarsophalangeal
joint, VM) trajectory characteristics (mean SD, normalized to the
limb length L): horizontal excursion of the VM marker [relative to greater
trochanter (GT)] and vertical VM displacements (in the laboratory reference
frame).been found to serve a similar function in fish, reptiles, birds,
and primates (Jordan 1991; Mori et al. 1996).
The question remains, however, as to whether this descending
control modulates a basic “motor program” for walking and
running (for instance, a set of nonlinear oscillators with bifurcations
at critical transitional points) or a separate set of
oscillators for each distinct gait. In either case, a gait transition
might be subject to feedback concerning critical peripheral
factors. A recent line of investigation relevant to the neural
generation of locomotor patterns is based on the identification
of elementary units of muscle activation (Bizzi et al. 1991,
2002; Davis and Vaughan 1993; d’Avella and Bizzi 2005;
Giszter et al. 2001; Hart and Giszter 2004; Hultborn 2001;
Ivanenko et al. 2003; Kargo and Giszter 2000; Patla et al. 1985;
Ting and Macpherson 2005; Tresch and Bizzi 1999). According
to this approach, locomotor programs may be considered as
a set of characteristic timings of muscle activation (Ivanenko et
al. 2005). In fact, the same five basic activation components
can account for the electromyographic (EMG) waveforms of
32 ipsilateral leg, trunk, and shoulder muscles during walking
at speeds between 1 and 5 km/h (Ivanenko et el., 2004b). The
same five components were also present when walking was
