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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    • "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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    Frontiers in Human Neuroscience 06/2014; 8:423. DOI:10.3389/fnhum.2014.00423 · 3.63 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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