A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks
This work introduces a real-time intention decoding algorithm grounded in muscle synergies (Syn-ID). The algorithm detects the electromyographic (EMG) onset and infers the direction of the movement during reaching tasks to control a powered shoulder–elbow exoskeleton. Features related to muscle syne...
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Cambridge University Press
2024-01-01
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| Series: | Wearable Technologies |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2631717624000161/type/journal_article |
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| author | Michele Francesco Penna Luca Giordano Stefano Tortora Davide Astarita Lorenzo Amato Filippo Dell’Agnello Emanuele Menegatti Emanuele Gruppioni Nicola Vitiello Simona Crea Emilio Trigili |
| author_facet | Michele Francesco Penna Luca Giordano Stefano Tortora Davide Astarita Lorenzo Amato Filippo Dell’Agnello Emanuele Menegatti Emanuele Gruppioni Nicola Vitiello Simona Crea Emilio Trigili |
| author_sort | Michele Francesco Penna |
| collection | DOAJ |
| description | This work introduces a real-time intention decoding algorithm grounded in muscle synergies (Syn-ID). The algorithm detects the electromyographic (EMG) onset and infers the direction of the movement during reaching tasks to control a powered shoulder–elbow exoskeleton. Features related to muscle synergies are used in a Gaussian Mixture Model and probability accumulation-based logic to infer the user’s movement direction. The performance of the algorithm was verified by a feasibility study including eight healthy participants. The experiments comprised a transparent session, during which the exoskeleton did not provide any assistance, and an assistive session in which the Syn-ID strategy was employed. Participants were asked to reach eight targets equally spaced on a circumference of 25 cm radius (adjusted chance level: 18.1%). The results showed an average accuracy of 48.7% after 0.6 s from the EMG onset. Most of the confusion of the estimate was found along directions adjacent to the actual one (type 1 error: 33.4%). Effects of the assistance were observed in a statistically significant reduction in the activation of Posterior Deltoid and Triceps Brachii. The final positions of the movements during the assistive session were on average 1.42 cm far from the expected ones, both when the directions were estimated correctly and when type 1 errors occurred. Therefore, combining accurate estimates with type 1 errors, we computed a modified accuracy of 82.10±6.34%. Results were benchmarked with respect to a purely kinematics-based approach. The Syn-ID showed better performance in the first portion of the movement (0.14 s after EMG onset). |
| format | Article |
| id | doaj-art-52616e28c4194d15a6313a1e3aef4980 |
| institution | Kabale University |
| issn | 2631-7176 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Wearable Technologies |
| spelling | doaj-art-52616e28c4194d15a6313a1e3aef49802024-11-15T08:29:49ZengCambridge University PressWearable Technologies2631-71762024-01-01510.1017/wtc.2024.16A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasksMichele Francesco Penna0https://orcid.org/0000-0002-2296-3743Luca Giordano1Stefano Tortora2Davide Astarita3Lorenzo Amato4Filippo Dell’Agnello5Emanuele Menegatti6Emanuele Gruppioni7Nicola Vitiello8Simona Crea9https://orcid.org/0000-0001-9833-4401Emilio Trigili10The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, ItalyThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, ItalyDepartment of Information Engineering, University of Padova, Padova, Italy Padova Neuroscience Center, University of Padova, Padova, ItalyThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, ItalyThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, ItalyThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, ItalyDepartment of Information Engineering, University of Padova, Padova, Italy Padova Neuroscience Center, University of Padova, Padova, ItalyCentro Protesi Inail di Vigorso di Budrio, Bologna, ItalyThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, ItalyThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, ItalyThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, ItalyThis work introduces a real-time intention decoding algorithm grounded in muscle synergies (Syn-ID). The algorithm detects the electromyographic (EMG) onset and infers the direction of the movement during reaching tasks to control a powered shoulder–elbow exoskeleton. Features related to muscle synergies are used in a Gaussian Mixture Model and probability accumulation-based logic to infer the user’s movement direction. The performance of the algorithm was verified by a feasibility study including eight healthy participants. The experiments comprised a transparent session, during which the exoskeleton did not provide any assistance, and an assistive session in which the Syn-ID strategy was employed. Participants were asked to reach eight targets equally spaced on a circumference of 25 cm radius (adjusted chance level: 18.1%). The results showed an average accuracy of 48.7% after 0.6 s from the EMG onset. Most of the confusion of the estimate was found along directions adjacent to the actual one (type 1 error: 33.4%). Effects of the assistance were observed in a statistically significant reduction in the activation of Posterior Deltoid and Triceps Brachii. The final positions of the movements during the assistive session were on average 1.42 cm far from the expected ones, both when the directions were estimated correctly and when type 1 errors occurred. Therefore, combining accurate estimates with type 1 errors, we computed a modified accuracy of 82.10±6.34%. Results were benchmarked with respect to a purely kinematics-based approach. The Syn-ID showed better performance in the first portion of the movement (0.14 s after EMG onset).https://www.cambridge.org/core/product/identifier/S2631717624000161/type/journal_articleExoskeletonsIntention decodingelectromyographywearable robotics |
| spellingShingle | Michele Francesco Penna Luca Giordano Stefano Tortora Davide Astarita Lorenzo Amato Filippo Dell’Agnello Emanuele Menegatti Emanuele Gruppioni Nicola Vitiello Simona Crea Emilio Trigili A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks Wearable Technologies Exoskeletons Intention decoding electromyography wearable robotics |
| title | A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks |
| title_full | A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks |
| title_fullStr | A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks |
| title_full_unstemmed | A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks |
| title_short | A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks |
| title_sort | muscle synergies based controller to drive a powered upper limb exoskeleton in reaching tasks |
| topic | Exoskeletons Intention decoding electromyography wearable robotics |
| url | https://www.cambridge.org/core/product/identifier/S2631717624000161/type/journal_article |
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