Toward a unifying framework for the modeling and identification of motor primitives
A large body of evidence suggests that human and animal movements, despite their apparent complexity and flexibility, are remarkably structured. Quantitative analyses of various classes of motor behaviors consistently identify spatial and temporal features that are invariant across movements. Such i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2022.926345/full |
| _version_ | 1798003097287524352 |
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| author | Enrico Chiovetto Alessandro Salatiello Andrea d'Avella Andrea d'Avella Martin A. Giese |
| author_facet | Enrico Chiovetto Alessandro Salatiello Andrea d'Avella Andrea d'Avella Martin A. Giese |
| author_sort | Enrico Chiovetto |
| collection | DOAJ |
| description | A large body of evidence suggests that human and animal movements, despite their apparent complexity and flexibility, are remarkably structured. Quantitative analyses of various classes of motor behaviors consistently identify spatial and temporal features that are invariant across movements. Such invariant features have been observed at different levels of organization in the motor system, including the electromyographic, kinematic, and kinetic levels, and are thought to reflect fixed modules—named motor primitives—that the brain uses to simplify the construction of movement. However, motor primitives across space, time, and organization levels are often described with ad-hoc mathematical models that tend to be domain-specific. This, in turn, generates the need to use model-specific algorithms for the identification of both the motor primitives and additional model parameters. The lack of a comprehensive framework complicates the comparison and interpretation of the results obtained across different domains and studies. In this work, we take the first steps toward addressing these issues, by introducing a unifying framework for the modeling and identification of qualitatively different classes of motor primitives. Specifically, we show that a single model, the anechoic mixture model, subsumes many popular classes of motor primitive models. Moreover, we exploit the flexibility of the anechoic mixture model to develop a new class of identification algorithms based on the Fourier-based Anechoic Demixing Algorithm (FADA). We validate our framework by identifying eight qualitatively different classes of motor primitives from both simulated and experimental data. We show that, compared to established model-specific algorithms for the identification of motor primitives, our flexible framework reaches overall comparable and sometimes superior reconstruction performance. The identification framework is publicly released as a MATLAB toolbox (FADA-T, https://tinyurl.com/compsens) to facilitate the identification and comparison of different motor primitive models. |
| first_indexed | 2024-04-11T12:02:35Z |
| format | Article |
| id | doaj.art-15de1a82892a40ceb6c0de11c025576e |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-11T12:02:35Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-15de1a82892a40ceb6c0de11c025576e2022-12-22T04:24:48ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882022-09-011610.3389/fncom.2022.926345926345Toward a unifying framework for the modeling and identification of motor primitivesEnrico Chiovetto0Alessandro Salatiello1Andrea d'Avella2Andrea d'Avella3Martin A. Giese4Section for Computational Sensomotorics, Centre for Integrative Neuroscience, Hertie Institute for Clinical Brain Research, University Clinic Tübingen, Tübingen, GermanySection for Computational Sensomotorics, Centre for Integrative Neuroscience, Hertie Institute for Clinical Brain Research, University Clinic Tübingen, Tübingen, GermanyLaboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, ItalyDepartment of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, ItalySection for Computational Sensomotorics, Centre for Integrative Neuroscience, Hertie Institute for Clinical Brain Research, University Clinic Tübingen, Tübingen, GermanyA large body of evidence suggests that human and animal movements, despite their apparent complexity and flexibility, are remarkably structured. Quantitative analyses of various classes of motor behaviors consistently identify spatial and temporal features that are invariant across movements. Such invariant features have been observed at different levels of organization in the motor system, including the electromyographic, kinematic, and kinetic levels, and are thought to reflect fixed modules—named motor primitives—that the brain uses to simplify the construction of movement. However, motor primitives across space, time, and organization levels are often described with ad-hoc mathematical models that tend to be domain-specific. This, in turn, generates the need to use model-specific algorithms for the identification of both the motor primitives and additional model parameters. The lack of a comprehensive framework complicates the comparison and interpretation of the results obtained across different domains and studies. In this work, we take the first steps toward addressing these issues, by introducing a unifying framework for the modeling and identification of qualitatively different classes of motor primitives. Specifically, we show that a single model, the anechoic mixture model, subsumes many popular classes of motor primitive models. Moreover, we exploit the flexibility of the anechoic mixture model to develop a new class of identification algorithms based on the Fourier-based Anechoic Demixing Algorithm (FADA). We validate our framework by identifying eight qualitatively different classes of motor primitives from both simulated and experimental data. We show that, compared to established model-specific algorithms for the identification of motor primitives, our flexible framework reaches overall comparable and sometimes superior reconstruction performance. The identification framework is publicly released as a MATLAB toolbox (FADA-T, https://tinyurl.com/compsens) to facilitate the identification and comparison of different motor primitive models.https://www.frontiersin.org/articles/10.3389/fncom.2022.926345/fullmotor primitivesmuscle synergiesFourier-based Anechoic Demixing Algorithm (FADA)anechoic mixture modeldimensionality reductionmotor redundancy |
| spellingShingle | Enrico Chiovetto Alessandro Salatiello Andrea d'Avella Andrea d'Avella Martin A. Giese Toward a unifying framework for the modeling and identification of motor primitives Frontiers in Computational Neuroscience motor primitives muscle synergies Fourier-based Anechoic Demixing Algorithm (FADA) anechoic mixture model dimensionality reduction motor redundancy |
| title | Toward a unifying framework for the modeling and identification of motor primitives |
| title_full | Toward a unifying framework for the modeling and identification of motor primitives |
| title_fullStr | Toward a unifying framework for the modeling and identification of motor primitives |
| title_full_unstemmed | Toward a unifying framework for the modeling and identification of motor primitives |
| title_short | Toward a unifying framework for the modeling and identification of motor primitives |
| title_sort | toward a unifying framework for the modeling and identification of motor primitives |
| topic | motor primitives muscle synergies Fourier-based Anechoic Demixing Algorithm (FADA) anechoic mixture model dimensionality reduction motor redundancy |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2022.926345/full |
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