Limits...
A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions.

Gonzalez-Vargas J, Sartori M, Dosen S, Torricelli D, Pons JL, Farina D - Front Comput Neurosci (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council Madrid, Spain.

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 https://simtk.org/home/p-mep/.

No MeSH data available.


Comparison of the generic XPs and the estimated the weightings in scenario 2 to the experimental data of the known subject group. Experimentally obtained muscle modularity (non-negative factors and weightings) was well approximated by the predictive model. The estimation of weightings was better for highly active muscles in each component (yellow shaded plots). The y axes in all the plots are normalized between 0 and 1. The plots in the first row depict the XPs and the other plots show the weightings. In the plots for the elevations (speeds), the experimental weightings were averaged across speeds (elevations). For the GM and SM, the mean weightings are shown.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4585276&req=5

Figure 8: Comparison of the generic XPs and the estimated the weightings in scenario 2 to the experimental data of the known subject group. Experimentally obtained muscle modularity (non-negative factors and weightings) was well approximated by the predictive model. The estimation of weightings was better for highly active muscles in each component (yellow shaded plots). The y axes in all the plots are normalized between 0 and 1. The plots in the first row depict the XPs and the other plots show the weightings. In the plots for the elevations (speeds), the experimental weightings were averaged across speeds (elevations). For the GM and SM, the mean weightings are shown.

Mentions: Evaluation of the weightings predictor block output. Figure 8 shows a summary of the weighting prediction in scenario 2 for the known subject group. The yellow shaded areas highlight the predominantly recruited muscles in each component. Across a total of 700 comparisons (i.e., seven subjects, four components, and 25 conditions), the SSM showed a significant higher correlation and a significant lower RMSE (p < 0.01) than the SGM.


A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions.

Gonzalez-Vargas J, Sartori M, Dosen S, Torricelli D, Pons JL, Farina D - Front Comput Neurosci (2015)

Comparison of the generic XPs and the estimated the weightings in scenario 2 to the experimental data of the known subject group. Experimentally obtained muscle modularity (non-negative factors and weightings) was well approximated by the predictive model. The estimation of weightings was better for highly active muscles in each component (yellow shaded plots). The y axes in all the plots are normalized between 0 and 1. The plots in the first row depict the XPs and the other plots show the weightings. In the plots for the elevations (speeds), the experimental weightings were averaged across speeds (elevations). For the GM and SM, the mean weightings are shown.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585276&req=5

Figure 8: Comparison of the generic XPs and the estimated the weightings in scenario 2 to the experimental data of the known subject group. Experimentally obtained muscle modularity (non-negative factors and weightings) was well approximated by the predictive model. The estimation of weightings was better for highly active muscles in each component (yellow shaded plots). The y axes in all the plots are normalized between 0 and 1. The plots in the first row depict the XPs and the other plots show the weightings. In the plots for the elevations (speeds), the experimental weightings were averaged across speeds (elevations). For the GM and SM, the mean weightings are shown.
Mentions: Evaluation of the weightings predictor block output. Figure 8 shows a summary of the weighting prediction in scenario 2 for the known subject group. The yellow shaded areas highlight the predominantly recruited muscles in each component. Across a total of 700 comparisons (i.e., seven subjects, four components, and 25 conditions), the SSM showed a significant higher correlation and a significant lower RMSE (p < 0.01) than the SGM.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council Madrid, Spain.

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 https://simtk.org/home/p-mep/.

No MeSH data available.