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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.


Two different scenarios were used in order to test the predictive model. The model in scenario 1 was developed using the data from seven subjects over nine conditions. The model in scenario 2 was developed using the data from seven subjects in full set of 25 conditions. The models developed in each scenario were assessed using the data of the seven subjects included in the training (known subjects group) as well as the two subjects excluded from the training (unknown subjects group).
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Figure 2: Two different scenarios were used in order to test the predictive model. The model in scenario 1 was developed using the data from seven subjects over nine conditions. The model in scenario 2 was developed using the data from seven subjects in full set of 25 conditions. The models developed in each scenario were assessed using the data of the seven subjects included in the training (known subjects group) as well as the two subjects excluded from the training (unknown subjects group).

Mentions: Two different scenarios were used in order to develop and test the predictive model (Figure 2). In both scenarios, the data obtained by the descriptive analysis of seven subjects were used to train the predictive model, i.e., to determine the XPs and regression equations. The two remaining subjects were used to further test both scenarios with novel subjects (unknown subject group). In scenario 1, the training included only the data from a subset of conditions, three elevations (−20, 0, and 20%) and three speeds (1, 3, and 5 km/h), for a total of nine conditions. This scenario yielded a predictive model trained on a reduced dataset. This enabled assessing the ability of a conservative model to generalize predictions of experimental data in novel locomotion conditions and subjects. Therefore, the model was tested with the remaining 16 conditions of the seven subjects (known subject group) as well as with the two unknown subjects excluded from the training (unknown subject group).


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)

Two different scenarios were used in order to test the predictive model. The model in scenario 1 was developed using the data from seven subjects over nine conditions. The model in scenario 2 was developed using the data from seven subjects in full set of 25 conditions. The models developed in each scenario were assessed using the data of the seven subjects included in the training (known subjects group) as well as the two subjects excluded from the training (unknown subjects group).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Two different scenarios were used in order to test the predictive model. The model in scenario 1 was developed using the data from seven subjects over nine conditions. The model in scenario 2 was developed using the data from seven subjects in full set of 25 conditions. The models developed in each scenario were assessed using the data of the seven subjects included in the training (known subjects group) as well as the two subjects excluded from the training (unknown subjects group).
Mentions: Two different scenarios were used in order to develop and test the predictive model (Figure 2). In both scenarios, the data obtained by the descriptive analysis of seven subjects were used to train the predictive model, i.e., to determine the XPs and regression equations. The two remaining subjects were used to further test both scenarios with novel subjects (unknown subject group). In scenario 1, the training included only the data from a subset of conditions, three elevations (−20, 0, and 20%) and three speeds (1, 3, and 5 km/h), for a total of nine conditions. This scenario yielded a predictive model trained on a reduced dataset. This enabled assessing the ability of a conservative model to generalize predictions of experimental data in novel locomotion conditions and subjects. Therefore, the model was tested with the remaining 16 conditions of the seven subjects (known subject group) as well as with the two unknown subjects excluded from the training (unknown subject group).

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.