Learning dynamics of muscle synergies during non-biomimetic control maps
Advanced myoelectric prostheses feature multiple degrees of freedom (DoFs) and sophisticated control algorithms that interpret user motor intentions as commands. While enhancing their capability to assist users in a wide range of daily activities, these control solutions still pose challenges. Among...
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| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Wearable Technologies |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2631717624000240/type/journal_article |
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| author | King Chun Tse Patricia Capsi-Morales Cristina Piazza |
| author_facet | King Chun Tse Patricia Capsi-Morales Cristina Piazza |
| author_sort | King Chun Tse |
| collection | DOAJ |
| description | Advanced myoelectric prostheses feature multiple degrees of freedom (DoFs) and sophisticated control algorithms that interpret user motor intentions as commands. While enhancing their capability to assist users in a wide range of daily activities, these control solutions still pose challenges. Among them, the need for extensive learning periods and users’ limited control proficiency. To investigate the relationship between these challenges and the limited alignment of such methods with human motor control strategies, we examine motor learning processes in two different control maps testing a synergistic myoelectric system. In particular, this work employs a DoF-wise synergies control algorithm tested in both intuitive and non-intuitive control mappings. Intuitive mapping aligns body movements with control actions to replicate natural limb control, whereas non-intuitive mapping (or non-biomimetic) lacks a direct correlation between aspects, allowing one body movement to influence multiple DoFs. The latter offers increased design flexibility through redundancy, which can be especially advantageous for individuals with motor disabilities. The study evaluates the effectiveness and learning process of both control mappings with 10 able-bodied participants. The results revealed distinct patterns observed while testing the two maps. Furthermore, muscle synergies exhibited greater stability and distinction by the end of the experiment, indicative of varied learning processes. |
| format | Article |
| id | doaj-art-3c93567dc18b4fcdab9de31f73eea2c9 |
| institution | Kabale University |
| issn | 2631-7176 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Wearable Technologies |
| spelling | doaj-art-3c93567dc18b4fcdab9de31f73eea2c92025-01-20T08:55:53ZengCambridge University PressWearable Technologies2631-71762025-01-01610.1017/wtc.2024.24Learning dynamics of muscle synergies during non-biomimetic control mapsKing Chun Tse0Patricia Capsi-Morales1https://orcid.org/0000-0001-6498-9231Cristina Piazza2Department of Computer Engineering, School of Computation, Information and Technology, Technical University of Munich, Munich, GermanyDepartment of Computer Engineering, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, Munich, GermanyDepartment of Computer Engineering, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, Munich, GermanyAdvanced myoelectric prostheses feature multiple degrees of freedom (DoFs) and sophisticated control algorithms that interpret user motor intentions as commands. While enhancing their capability to assist users in a wide range of daily activities, these control solutions still pose challenges. Among them, the need for extensive learning periods and users’ limited control proficiency. To investigate the relationship between these challenges and the limited alignment of such methods with human motor control strategies, we examine motor learning processes in two different control maps testing a synergistic myoelectric system. In particular, this work employs a DoF-wise synergies control algorithm tested in both intuitive and non-intuitive control mappings. Intuitive mapping aligns body movements with control actions to replicate natural limb control, whereas non-intuitive mapping (or non-biomimetic) lacks a direct correlation between aspects, allowing one body movement to influence multiple DoFs. The latter offers increased design flexibility through redundancy, which can be especially advantageous for individuals with motor disabilities. The study evaluates the effectiveness and learning process of both control mappings with 10 able-bodied participants. The results revealed distinct patterns observed while testing the two maps. Furthermore, muscle synergies exhibited greater stability and distinction by the end of the experiment, indicative of varied learning processes.https://www.cambridge.org/core/product/identifier/S2631717624000240/type/journal_articlemuscle synergiesmyoelectric controlprostheses |
| spellingShingle | King Chun Tse Patricia Capsi-Morales Cristina Piazza Learning dynamics of muscle synergies during non-biomimetic control maps Wearable Technologies muscle synergies myoelectric control prostheses |
| title | Learning dynamics of muscle synergies during non-biomimetic control maps |
| title_full | Learning dynamics of muscle synergies during non-biomimetic control maps |
| title_fullStr | Learning dynamics of muscle synergies during non-biomimetic control maps |
| title_full_unstemmed | Learning dynamics of muscle synergies during non-biomimetic control maps |
| title_short | Learning dynamics of muscle synergies during non-biomimetic control maps |
| title_sort | learning dynamics of muscle synergies during non biomimetic control maps |
| topic | muscle synergies myoelectric control prostheses |
| url | https://www.cambridge.org/core/product/identifier/S2631717624000240/type/journal_article |
| work_keys_str_mv | AT kingchuntse learningdynamicsofmusclesynergiesduringnonbiomimeticcontrolmaps AT patriciacapsimorales learningdynamicsofmusclesynergiesduringnonbiomimeticcontrolmaps AT cristinapiazza learningdynamicsofmusclesynergiesduringnonbiomimeticcontrolmaps |