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: 2.75). 10/2009; 43(3):412-9. DOI: 10.1016/j.jbiomech.2009.10.009
Source: PubMed


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.

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Available from: Steven A Kautz, Oct 07, 2015
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    • "g Frontiers in Computational Neuroscience | www . frontiersin . org 11 September 2015 | muscle excitation - driven musculoskeletal simulations of a large repertoire of human locomotion conditions ( also see Section Applicability of the developed MEP Model ) , a scientific area where the theory of muscle synergy is increasingly gaining importance ( McGowan et al . , 2010 ; Allen and Neptune , 2012 ; Rückert and d ' Avella , 2013 ; Sartori et al . , 2013 ; Gopalakrishnan et al . , 2014 ; Walter et al . , 2014 ) ."
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    ABSTRACT: Humans can efficiently walk across a large variety of terrains and locomotion conditions with little or no mental effort. It has been hypothesized that the nervous system simplifies neuromuscular control by using muscle synergies, thus organizing multi-muscle activity into a small number of coordinative co-activation modules. In the present study we investigated how muscle modularity is structured across a large repertoire of locomotion conditions including five different speeds and five different ground elevations. For this we have used the non-negative matrix factorization technique in order to explain EMG experimental data with a low-dimensional set of four motor components. In this context each motor components is composed of a non-negative factor and the associated muscle weightings. Furthermore, we have investigated if the proposed descriptive analysis of muscle modularity could be translated into a predictive model that could: (1) Estimate how motor components modulate across locomotion speeds and ground elevations. This implies not only estimating the non-negative factors temporal characteristics, but also the associated muscle weighting variations. (2) Estimate how the resulting muscle excitations modulate across novel locomotion conditions and subjects. The results showed three major distinctive features of muscle modularity: (1) the number of motor components was preserved across all locomotion conditions, (2) the non-negative factors were consistent in shape and timing across all locomotion conditions, and (3) the muscle weightings were modulated as distinctive functions of locomotion speed and ground elevation. Results also showed that the developed predictive model was able to reproduce well the muscle modularity of un-modeled data, i.e., novel subjects and conditions. Muscle weightings were reconstructed with a cross-correlation factor greater than 70% and a root mean square error less than 0.10. Furthermore, the generated muscle excitations matched well the experimental excitation with a cross-correlation factor greater than 85% and a root mean square error less than 0.09. The ability of synthetizing the neuromuscular mechanisms underlying human locomotion across a variety of locomotion conditions will enable solutions in the field of neurorehabilitation technologies and control of bipedal artificial systems. Open-access of the model implementation is provided for further analysis at
    Frontiers in Computational Neuroscience 10/2015; 9(114). DOI:10.3389/fncom.2015.00114 · 2.20 Impact Factor
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    • "For instance, with body weight unloading (Ivanenko et al., 2002), most muscles (e.g., gluteus maximus and distal leg extensors) decrease their activity, while other muscles demonstrate a “paradoxical” increment of activation (e.g., quadriceps) or considerable changes in the activation waveforms (hamstring muscles). Even the amplitude of EMG activity of “anatomical” synergists may diverge remarkably: lateral and medial gastrocnemius muscles at different walking speeds (Huang and Ferris, 2012), soleus and gastrocnemius muscles at different levels of limb loading (McGowan et al., 2010). In addition, muscle activity patterns are shaped by the direction of progression (e.g., forward vs. backward, Grasso et al., 1998, or walking along a curved path, Courtine et al., 2006). "
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    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 06/2014; 8:423. DOI:10.3389/fnhum.2014.00423 · 2.99 Impact Factor
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    • "The current study focuses mainly on identifying basic underlying patterns in EMG activity and quantifying temporal adjustments of these patterns to the splitbelt conditions. Previous work has suggested that 4 –5 basic patterns can account for ϳ90% of the variance in locomotor EMG activity, although the estimated number of patterns depends on the number and locations of the recorded muscles (Ivanenko et al. 2004; McGowan et al. 2010; Monaco et al. 2010; Olree and Vaughan 1995). It is thought that these patterns or " primitives " are present at birth and are refined through development into adulthood (Dominici et al. 2011). "
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    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; 111(8). DOI:10.1152/jn.00437.2013 · 2.89 Impact Factor
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