Modular control of human walking: Adaptations to altered mechanical demands.

Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
Journal of biomechanics (Impact Factor: 2.66). 10/2009; 43(3):412-9. DOI: 10.1016/j.jbiomech.2009.10.009
Source: PubMed

ABSTRACT Studies have suggested that the nervous system may adopt a control scheme in which synergistic muscle groups are controlled by common excitation patters, or modules, to simplify the coordination of movement tasks such as walking. A recent computer modeling and simulation study of human walking using experimentally derived modules as the control inputs provided evidence that individual modules are associated with specific biomechanical subtasks, such as generating body support and forward propulsion. The present study tests whether the modules identified during normal walking could produce simulations of walking when the mechanical demands were substantially altered. Walking simulations were generated that emulated human subjects who had their body weight and/or body mass increased and decreased by 25%. By scaling the magnitude of five module patterns, the simulations could emulate the subjects' response to each condition by simply scaling the mechanical output from modules associated with specific biomechanical subtasks. Specifically, the modules associated with providing body support increased (decreased) their contribution to the vertical ground reaction force when body weight was increased (decreased) and the module associated with providing forward propulsion increased its contribution to the positive anterior-posterior ground reaction force and positive trunk power when the body mass was increased. The modules that contribute to controlling leg swing were unaffected by the perturbations. These results support the idea that the nervous system may use a modular control strategy and that flexible modulation of module recruitment intensity may be sufficient to meet large changes in mechanical demand.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Locomotion can be investigated by factorization of electromyographic (EMG) signals, e.g., with non-negative matrix factorization (NMF). This approach is a convenient concise representation of muscle activities as distributed in motor modules, activated in specific gait phases. For applying NMF, the EMG signals are analyzed either as single trials, or as averaged EMG, or as concatenated EMG (data structure). The aim of this study is to investigate the influence of the data structure on the extracted motor modules. Twelve healthy men walked at their preferred speed on a treadmill while surface EMG signals were recorded for 60s from 10 lower limb muscles. Motor modules representing relative weightings of synergistic muscle activations were extracted by NMF from 40 step cycles separately (EMGSNG), from averaging 2, 3, 5, 10, 20, and 40 consecutive cycles (EMGAVR), and from the concatenation of the same sets of consecutive cycles (EMGCNC). Five motor modules were sufficient to reconstruct the original EMG datasets (reconstruction quality >90%), regardless of the type of data structure used. However, EMGCNC was associated with a slightly reduced reconstruction quality with respect to EMGAVR. Most motor modules were similar when extracted from different data structures (similarity >0.85). However, the quality of the reconstructed 40-step EMGCNC datasets when using the muscle weightings from EMGAVR was low (reconstruction quality ~40%). On the other hand, the use of weightings from EMGCNC for reconstructing this long period of locomotion provided higher quality, especially using 20 concatenated steps (reconstruction quality ~80%). Although EMGSNG and EMGAVR showed a higher reconstruction quality for short signal intervals, these data structures did not account for step-to-step variability. The results of this study provide practical guidelines on the methodological aspects of synergistic muscle activation extraction from EMG during locomotion.
    Frontiers in Human Neuroscience 01/2014; 8. · 2.91 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Neuroprosthetic technology and robotic exoskeletons are being developed to facilitate stepping, reduce muscle efforts, and promote motor recovery. Nevertheless, the guidance forces of an exoskeleton may influence the sensory inputs, sensorimotor interactions and resulting muscle activity patterns during stepping. The aim of this study was to report the muscle activation patterns in a sample of intact and injured subjects while walking with a robotic exoskeleton and, in particular, to quantify the level of muscle activity during assisted gait. We recorded electromyographic (EMG) activity of different leg and arm muscles during overground walking in an exoskeleton in six healthy individuals and four spinal cord injury (SCI) participants. In SCI patients, EMG activity of the upper limb muscles was augmented while activation of leg muscles was typically small. Contrary to our expectations, however, in neurologically intact subjects, EMG activity of leg muscles was similar or even larger during exoskeleton-assisted walking compared to normal overground walking. In addition, significant variations in the EMG waveforms were found across different walking conditions. The most variable pattern was observed in the hamstring muscles. Overall, the results are consistent with a non-linear reorganization of the locomotor output when using the robotic stepping devices. The findings may contribute to our understanding of human-machine interactions and adaptation of locomotor activity patterns.
    Frontiers in Human Neuroscience 01/2014; 8:423. · 2.91 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: There is growing evidence that human locomotion is controlled by flexibly combining a set of basic muscle activity patterns. To explore how these patterns are modified to cope with environmental constraints, ten healthy young adults first walked on a split-belt treadmill at symmetric speeds of 4 and 6 km/h for 2 min. An asymmetric condition was then performed for 10 min. in which treadmill speeds for the dominant (fast) and non-dominant (slow) sides were 6 and 4 km/h respectively. This was immediately followed by a symmetric speed condition of 4 km/h for 5 min. Gait kinematics and ground reaction forces were recorded. Electromyography (EMG) was collected from 12 lower limb muscles on each side of the body. Non-negative matrix factorization was applied to the EMG signals bilaterally and unilaterally to obtain basic activation patterns. A cross-correlation analysis was then used to quantify temporal changes in the activation patterns. During the early (first 10 strides) and late (final 10 strides) phases of the asymmetric condition, the patterns related to ankle plantar flexor (push-off) of the fast limb and quadriceps muscle (contralateral heel contact) of the slow limb occurred earlier in the gait cycle when compared to the symmetric conditions. Moreover, a bilateral temporal alignment of basic patterns between limbs was still maintained in the split-belt condition, since a similar shift was observed in the unilateral patterns. The results suggest that the temporal structure of these locomotor patterns is shaped by sensory feedback and that the patterns are bilaterally linked.
    Journal of Neurophysiology 01/2014; · 3.30 Impact Factor

Full-text (2 Sources)

Available from
May 21, 2014