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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.


Performance assessment of the weight predictor block in scenario 1. The plots depict histograms of the average (median ± interquartile range) correlation coefficient (r) and root mean square error (RMSE) between the estimated and experimental weightings.
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Figure 6: Performance assessment of the weight predictor block in scenario 1. The plots depict histograms of the average (median ± interquartile range) correlation coefficient (r) and root mean square error (RMSE) between the estimated and experimental weightings.

Mentions: Evaluation of the weighting predictor block output. Figure 6 summarizes the quality of estimation of the muscle weightings in unknown conditions for both the known and unknown groups using the two modes of the predictive model (SSM and SGM). For a total of 448 comparisons (i.e., seven subjects, four components, and 16 conditions) in the known subjects group, the SSM outperformed the SGM significantly (p < 0.01) in both outcome measures. However, for the unknown subject group, for a total of 128 comparisons (i.e., two subjects, four components, and 16 conditions) there was no statistically significant difference in the quality of estimation between the SGM and SSM. However, as seen in Figure 6 (right plots), the histograms show that SSM tends to be more skewed toward higher correlation in the estimation than 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)

Performance assessment of the weight predictor block in scenario 1. The plots depict histograms of the average (median ± interquartile range) correlation coefficient (r) and root mean square error (RMSE) between the estimated and experimental weightings.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 6: Performance assessment of the weight predictor block in scenario 1. The plots depict histograms of the average (median ± interquartile range) correlation coefficient (r) and root mean square error (RMSE) between the estimated and experimental weightings.
Mentions: Evaluation of the weighting predictor block output. Figure 6 summarizes the quality of estimation of the muscle weightings in unknown conditions for both the known and unknown groups using the two modes of the predictive model (SSM and SGM). For a total of 448 comparisons (i.e., seven subjects, four components, and 16 conditions) in the known subjects group, the SSM outperformed the SGM significantly (p < 0.01) in both outcome measures. However, for the unknown subject group, for a total of 128 comparisons (i.e., two subjects, four components, and 16 conditions) there was no statistically significant difference in the quality of estimation between the SGM and SSM. However, as seen in Figure 6 (right plots), the histograms show that SSM tends to be more skewed toward higher correlation in the estimation than 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.