Evidence suggests that the nervous system controls motor tasks using a low-dimensional modular organization of muscle activation. However, it is not clear if such an organization applies to coordination of human walking, nor how nervous system injury may alter the organization of motor modules and their biomechanical outputs. We first tested the hypothesis that muscle activation patterns during walking are produced through the variable activation of a small set of motor modules. In 20 healthy control subjects, EMG signals from eight leg muscles were measured across a range of walking speeds. Four motor modules identified through nonnegative matrix factorization were sufficient to account for variability of muscle activation from step to step and across speeds. Next, consistent with the clinical notion of abnormal limb flexion-extension synergies post-stroke, we tested the hypothesis that subjects with post-stroke hemiparesis would have altered motor modules, leading to impaired walking performance. In post-stroke subjects (n = 55), a less complex coordination pattern was shown. Fewer modules were needed to account for muscle activation during walking at preferred speed compared with controls. Fewer modules resulted from merging of the modules observed in healthy controls, suggesting reduced independence of neural control signals. The number of modules was correlated to preferred walking speed, speed modulation, step length asymmetry, and propulsive asymmetry. Our results suggest a common modular organization of muscle coordination underlying walking in both healthy and post-stroke subjects. Identification of motor modules may lead to new insight into impaired locomotor coordination and the underlying neural systems.

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Recent evidence suggests that performance of complex locomotor tasks such as walking may be accomplished using a simple underlying organization of co-active muscles, or “modules”, which have been assumed to be structured to perform task-specific biomechanical functions. However, no study has explicitly tested whether the modules would actually produce the biomechanical functions associated with them or even produce a well-coordinated movement. In this study, we generated muscle-actuated forward dynamics simulations of normal walking using muscle activation modules (identified using non-negative matrix factorization) as the muscle control inputs to identify the contributions of each module to the biomechanical sub-tasks of walking (i.e., body support, forward propulsion and leg swing). The simulation analysis showed a simple neural control strategy involving five muscle activation modules was sufficient to perform the basic sub-tasks of walking. Module 1 (gluteus medius, vasti and rectus femoris) primarily contributed to body support in early stance while Module 2 (soleus and gastrocnemius) contributed to both body support and propulsion in late stance. Module 3 (rectus femoris and tibialis anterior) acted to decelerate the leg in early and late swing while generating energy to the trunk throughout swing. Module 4 (hamstrings) acted to absorb leg energy (i.e., decelerate it) in late swing while increasing the leg energy in early stance. Post-hoc analysis revealed an additional module (Module 5: iliopsoas) acted to accelerate the leg forward in pre- and early swing. These results provide evidence that the identified modules can act as basic neural control elements that generate task-specific biomechanical functions to produce well-coordinated walking.

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Modulating speed over a large range is important in walking, yet understanding how the neuromotor patterns adapt to the changing energetic demands of different speeds is not well understood. The purpose of this study was to identify functional and energetic adaptations in individual muscles in response to walking at faster steady-state speeds using muscle-actuated forward dynamics simulations. The simulation data were invariant with speed as to whether muscles contributed to trunk support, forward propulsion or leg swing. Trunk support (vertical acceleration) was provided primarily by the hip and knee extensors in early stance and the plantar flexors in late stance, while trunk propulsion (horizontal acceleration) was provided primarily by the soleus and rectus femoris in late stance, and these muscle contributions all systematically increased with speed. The results also highlighted the importance of initiating and controlling leg swing as there was a dramatic increase at the higher walking speeds in iliopsoas muscle work to accelerate the leg in pre- and early swing, and an increase in the biarticular hamstring muscle work to decelerate the leg in late swing. In addition, walking near self-selected speeds (1.2m/s) improves the utilization of elastic energy storage and recovery in the uniarticular ankle plantar flexors and reduces negative fiber work, when compared to faster or slower speeds. These results provide important insight into the neuromotor mechanisms underlying speed regulation in walking and provide the foundation on which to investigate the influence of walking speed on various neuromotor measures of interest in pathological populations.

Background and Purpose-Walking after stroke is characterized by slow gait speed, poor endurance, reduced quality and adaptability of walking patterns, and an inability to coordinate the legs. Estimates based on mechanical work calculations have suggested that the paretic leg does 30% to 40% of the total mechanical work over the gait cycle, regardless of hemiparetic severity, but these work estimates may not describe the contribution of each leg to forward propulsion. The purpose of this study was to establish a quantifiable link between hemiparetic severity and paretic leg contribution to propulsion during walking, which we propose to quantify using a measure based on the anterior-posterior ground reaction forces (A-P GRFs). Methods-A total of 47 participants with chronic hemiparesis walked at self-selected speeds to collect spatiotemporal parameters and 3D GRFs. A 16-person subset also participated in a pedaling protocol to compare A-P GRF measures to established measures of paretic leg output. Results-A-P GRF measures were correlated with both walking speed and hemiparetic severity. These measures were also strongly correlated with positive work and net work values obtained during the pedaling task. The percentage of total propulsion generated by the paretic leg (P P ) was calculated and found to be 16%, 36%, and 49% for those with high, moderate, and low hemiparetic severity, respectively. Conclusion-P

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