An electromyographic (EMG) activity pattern for individual muscles in the gait cycle exhibits a great deal of intersubject, intermuscle and context-dependent variability. Here we examined the issue of common underlying patterns by applying factor analysis to the set of EMG records obtained at different walking speeds and gravitational loads. To this end healthy subjects were asked to walk on a treadmill at speeds of 1, 2, 3 and 5 km h −1 as well as when 35-95% of the body weightwassupportedusingaharness.Werecordedfrom12-16ipsilaterallegandtrunkmuscles using both surface and intramuscular recording and determined the average, normalized EMG of each record for 10-15 consecutive step cycles. We identified five basic underlying factors or component waveforms that can account for about 90% of the total waveform variance across different muscles during normal gait. Furthermore, while activation patterns of individual muscles could vary dramatically with speed and gravitational load, both the limb kinematics and the basic EMG components displayed only limited changes. Thus, we found a systematic phase shift of all five factors with speed in the same direction as the shift in the onset of the swing phase. This tendency for the factors to be timed according to the lift-off event supports the idea that the origin of the gait cycle generation is the propulsion rather than heel strike event. The basic invariance of the factors with walking speed and with body weight unloading implies that a few oscillating circuits drive the active muscles to produce the locomotion kinematics. A flexible and dynamic distribution of these basic components to the muscles may result from various descending and proprioceptive signals that depend on the kinematic and kinetic demands of the movements.

. 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.

How rudimentary movements evolve into sophisticated ones during development remains unclear. It is often assumed that the primitive patterns of neural control are suppressed during development, replaced by entirely new patterns. Here we identified the basic patterns of lumbosacral motoneuron activity from multimuscle recordings in stepping neonates, toddlers, preschoolers, and adults. Surprisingly, we found that the two basic patterns of stepping neonates are retained through development, augmented by two new patterns first revealed in toddlers. Markedly similar patterns were observed also in the rat, cat, macaque, and guineafowl, consistent with the hypothesis that, despite substantial phylogenetic distances and morphological differences, locomotion in several animal species is built starting from common primitives, perhaps related to a common ancestral neural network.

Muscle activity occurring during human locomotion can be accounted for by five basic temporal activation patterns in a variety of locomotion conditions. Here, we examined how these activation patterns interact with muscle activity required for a voluntary movement. Subjects produced a voluntary movement during locomotion, and we examined the resulting kinematics, kinetics, and EMG activity in 16 -31 ipsilateral limb and trunk muscles during the tasks. There were four voluntary tasks added to overground walking (ϳ5 km/h) in which subjects kicked a ball, stepped over an obstacle, or reached down and grasped an object on the floor (weight support on either the right or the left foot). Statistical analyses of EMG waveforms showed that the five basic locomotion patterns were invariantly present in each task, although they could be differently weighted across muscles, suggesting a characteristic locomotion timing of muscle activations. We also observed a separate activation that was timed to the voluntary task. The coordination of locomotion with the voluntary task was accomplished by combining activation timings that were associated separately with the voluntary task and locomotion. Activation associated with the voluntary tasks was either synchronous with the timing for locomotion or had additional activations not represented in the basic locomotion timing. We propose that this superposition of an invariant locomotion timing pattern with a voluntary activation timing may be consistent with the proposal suggesting that compound movements are produced through a superposition of motor programs.

There is much experimental evidence for the existence of biomechanical constraints which simplify the problem of control of multi-segment movements. In addition, it has been hypothesized that movements are controlled using a small set of basic temporal components or activation patterns, shared by several different muscles and reflecting global kinematic and kinetic goals. Here we review recent studies on human locomotion showing that muscle activity is accounted for by a combination of few basic patterns, each one timed at a different phase of the gait cycle. Similar patterns are involved in walking and running at different speeds, walking forwards or backwards, and walking under different loading conditions. The corresponding weights of distribution to different muscles may change as a function of the condition, allowing highly flexible control. Biomechanical correlates of each activation pattern have been described, leading to the hypothesis that the co-ordination of limb and body segments arises from the coupling of neural oscillators between each other and with limb mechanical oscillators. Muscle activations need only intervene during limited time epochs to force intrinsic oscillations of the system when energy is lost.

The idea that the CNS may control complex interactions by modular decomposition has received considerable attention. We explored this idea for human locomotion by examining limb kinematics. The coordination of limb segments during human locomotion has been shown to follow a planar law for walking at different speeds, directions, and levels of body unloading. We compared the coordination for different gaits. Eight subjects were asked to walk and run on a treadmill at different speeds or to walk, run, and hop over ground at a preferred speed. To explore various constraints on limb movements, we also recorded stepping over an obstacle, walking with the knees flexed, and air-stepping with body weight support. We found little difference among covariance planes that depended on speed, but there were differences that depended on gait. In each case, we could fit the planar trajectories with a weighted sum of the limb length and orientation trajectories. This suggested that limb length and orientation might provide independent predictors of limb coordination. We tested this further by having the subjects step, run, and hop in place, thereby varying only limb length and maintaining limb orientation fixed, and also by marching with knees locked to maintain limb length constant while varying orientation. The results were consistent with a modular control of limb kinematics where limb movements result from a superposition of separate length-and orientation-related angular covariance. The hypothesis finds support in the animal findings that limb proprioception may also be encoded in terms of these global limb parameters.

Functional MRI (fMRI) imaging of motoneuron activity in the human spinal cord is still in its infancy, and it will remain difficult to apply to walking. Here we present a viable alternative for documenting the spatiotemporal maps of alpha-motorneuron (MN) activity in the human spinal cord during walking, similar to the method recently reported for the cat. We recorded EMG activity from 16 to 32 ipsilateral limb and trunk muscles in 13 healthy subjects walking on a treadmill at different speeds (1-7 km/h) and mapped the recorded patterns onto the spinal cord in approximate rostrocaudal locations of the motoneuron pools. This approach can provide information about pattern generator output during locomotion in terms of segmental control rather than in terms of individual muscle control. A striking feature we found is that nearly every spinal segment undergoes at least two cycles of activation in the step cycle, thus supporting the idea of half-center oscillators controlling MN activation at any segmental level. The resulting spatiotemporal map patterns seem highly stereotyped over the range of walking speeds studied, although there were also some systematic redistributions of MN activity with speed. Bursts of MN activity were either temporally aligned across several spinal segments or switched between different segments. For example, the center of mass of MN activity in the lumbosacral levels generally shifted from rostral to caudal positions in two cycles for each step, revealing four major activation foci: two in the upper lumbar segments and two in the sacral segments. The results are consistent with the presence of at least two and possibly more pattern generators controlling the activation of lumbosacral MNs.

Prevailing views on how we time the interception of a moving object assume that the visual inputs are informationally sufficient to estimate the time-to-contact from the object's kinematics. Here we present evidence in favor of a different view: the brain makes the best estimate about target motion based on measured kinematics and an a priori guess about the causes of motion. According to this theory, a predictive model is used to extrapolate time-to-contact from expected dynamics (kinetics). We projected a virtual target moving vertically downward on a wide screen with different randomized laws of motion. In the first series of experiments, subjects were asked to intercept this target by punching a real ball that fell hidden behind the screen and arrived in synchrony with the visual target. Subjects systematically timed their motor responses consistent with the assumption of gravity effects on an object's mass, even when the visual target did not accelerate. With training, the gravity model was not switched off but adapted to nonaccelerating targets by shifting the time of motor activation. In the second series of experiments, there was no real ball falling behind the screen. Instead the subjects were required to intercept the visual target by clicking a mousebutton. In this case, subjects timed their responses consistent with the assumption of uniform motion in the absence of forces, even when the target actually accelerated. Overall, the results are in accord with the theory that motor responses evoked by visual kinematics are modulated by a prior of the target dynamics. The prior appears surprisingly resistant to modifications based on performance errors.