Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural Model
The growing interest in wearable robots opens the challenge for developing intuitive and natural control strategies. Among several human–machine interaction approaches, myoelectric control consists of decoding the motor intention from muscular activity (or EMG signals) with the aim of driving prosth...
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| Format: | Article |
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MDPI AG
2020-04-01
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| Online Access: | https://www.mdpi.com/2078-2489/11/4/219 |
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| author | Domenico Buongiorno Giacomo Donato Cascarano Cristian Camardella Irio De Feudis Antonio Frisoli Vitoantonio Bevilacqua |
| author_facet | Domenico Buongiorno Giacomo Donato Cascarano Cristian Camardella Irio De Feudis Antonio Frisoli Vitoantonio Bevilacqua |
| author_sort | Domenico Buongiorno |
| collection | DOAJ |
| description | The growing interest in wearable robots opens the challenge for developing intuitive and natural control strategies. Among several human–machine interaction approaches, myoelectric control consists of decoding the motor intention from muscular activity (or EMG signals) with the aim of driving prosthetic or assistive robotic devices accordingly, thus establishing an intimate human–machine connection. In this scenario, bio-inspired approaches, e.g., synergy-based controllers, are revealed to be the most robust. However, synergy-based myo-controllers already proposed in the literature consider muscle patterns that are computed considering only the total variance reconstruction rate of the EMG signals, without taking into account the performance of the controller in the task (or application) space. In this work, extending a previous study, the authors presented an autoencoder-based neural model able to extract muscles synergies for motion intention detection while optimizing the task performance in terms of force/moment reconstruction. The proposed neural topology has been validated with EMG signals acquired from the main upper limb muscles during planar isometric reaching tasks performed in a virtual environment while wearing an exoskeleton. The presented model has been compared with the non-negative matrix factorization algorithm (i.e., the most used approach in the literature) in terms of muscle synergy extraction quality, and with three techniques already presented in the literature in terms of goodness of shoulder and elbow predicted moments. The results of the experimental comparisons have showed that the proposed model outperforms the state-of-art synergy-based joint moment estimators at the expense of the quality of the EMG signals reconstruction. These findings demonstrate that a trade-off, between the capability of the extracted muscle synergies to better describe the EMG signals variability and the task performance in terms of force reconstruction, can be achieved. The results of this study might open new horizons on synergies extraction methodologies, optimized synergy-based myo-controllers and, perhaps, reveals useful hints about their origin. |
| first_indexed | 2024-03-10T20:25:29Z |
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| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T20:25:29Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-c08bb0801232480e9e3998def13b47b82023-11-19T21:53:00ZengMDPI AGInformation2078-24892020-04-0111421910.3390/info11040219Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural ModelDomenico Buongiorno0Giacomo Donato Cascarano1Cristian Camardella2Irio De Feudis3Antonio Frisoli4Vitoantonio Bevilacqua5Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, ItalyPercro Laboratory, Tecip Institute, Scuola Superiore Sant’Anna, 56127 Pisa, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, ItalyPercro Laboratory, Tecip Institute, Scuola Superiore Sant’Anna, 56127 Pisa, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, ItalyThe growing interest in wearable robots opens the challenge for developing intuitive and natural control strategies. Among several human–machine interaction approaches, myoelectric control consists of decoding the motor intention from muscular activity (or EMG signals) with the aim of driving prosthetic or assistive robotic devices accordingly, thus establishing an intimate human–machine connection. In this scenario, bio-inspired approaches, e.g., synergy-based controllers, are revealed to be the most robust. However, synergy-based myo-controllers already proposed in the literature consider muscle patterns that are computed considering only the total variance reconstruction rate of the EMG signals, without taking into account the performance of the controller in the task (or application) space. In this work, extending a previous study, the authors presented an autoencoder-based neural model able to extract muscles synergies for motion intention detection while optimizing the task performance in terms of force/moment reconstruction. The proposed neural topology has been validated with EMG signals acquired from the main upper limb muscles during planar isometric reaching tasks performed in a virtual environment while wearing an exoskeleton. The presented model has been compared with the non-negative matrix factorization algorithm (i.e., the most used approach in the literature) in terms of muscle synergy extraction quality, and with three techniques already presented in the literature in terms of goodness of shoulder and elbow predicted moments. The results of the experimental comparisons have showed that the proposed model outperforms the state-of-art synergy-based joint moment estimators at the expense of the quality of the EMG signals reconstruction. These findings demonstrate that a trade-off, between the capability of the extracted muscle synergies to better describe the EMG signals variability and the task performance in terms of force reconstruction, can be achieved. The results of this study might open new horizons on synergies extraction methodologies, optimized synergy-based myo-controllers and, perhaps, reveals useful hints about their origin.https://www.mdpi.com/2078-2489/11/4/219autoencoderEMG signalsmuscle synergiesroboticsexoskeletonmotor intention detection |
| spellingShingle | Domenico Buongiorno Giacomo Donato Cascarano Cristian Camardella Irio De Feudis Antonio Frisoli Vitoantonio Bevilacqua Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural Model Information autoencoder EMG signals muscle synergies robotics exoskeleton motor intention detection |
| title | Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural Model |
| title_full | Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural Model |
| title_fullStr | Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural Model |
| title_full_unstemmed | Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural Model |
| title_short | Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural Model |
| title_sort | task oriented muscle synergy extraction using an autoencoder based neural model |
| topic | autoencoder EMG signals muscle synergies robotics exoskeleton motor intention detection |
| url | https://www.mdpi.com/2078-2489/11/4/219 |
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