Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system
Abstract Voice disorders resulting from various pathological vocal fold conditions or postoperative recovery of laryngeal cancer surgeries, are common causes of dysphonia. Here, we present a self-powered wearable sensing-actuation system based on soft magnetoelasticity that enables assisted speaking...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-45915-7 |
| _version_ | 1797259168898351104 |
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| author | Ziyuan Che Xiao Wan Jing Xu Chrystal Duan Tianqi Zheng Jun Chen |
| author_facet | Ziyuan Che Xiao Wan Jing Xu Chrystal Duan Tianqi Zheng Jun Chen |
| author_sort | Ziyuan Che |
| collection | DOAJ |
| description | Abstract Voice disorders resulting from various pathological vocal fold conditions or postoperative recovery of laryngeal cancer surgeries, are common causes of dysphonia. Here, we present a self-powered wearable sensing-actuation system based on soft magnetoelasticity that enables assisted speaking without relying on the vocal folds. It holds a lightweighted mass of approximately 7.2 g, skin-alike modulus of 7.83 × 105 Pa, stability against skin perspiration, and a maximum stretchability of 164%. The wearable sensing component can effectively capture extrinsic laryngeal muscle movement and convert them into high-fidelity and analyzable electrical signals, which can be translated into speech signals with the assistance of machine learning algorithms with an accuracy of 94.68%. Then, with the wearable actuation component, the speech could be expressed as voice signals while circumventing vocal fold vibration. We expect this approach could facilitate the restoration of normal voice function and significantly enhance the quality of life for patients with dysfunctional vocal folds. |
| first_indexed | 2024-04-24T23:05:09Z |
| format | Article |
| id | doaj.art-a650eb54f7fe4e39ad402b059612f518 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-24T23:05:09Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-a650eb54f7fe4e39ad402b059612f5182024-03-17T12:31:31ZengNature PortfolioNature Communications2041-17232024-03-0115111110.1038/s41467-024-45915-7Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation systemZiyuan Che0Xiao Wan1Jing Xu2Chrystal Duan3Tianqi Zheng4Jun Chen5Department of Bioengineering, University of California, Los AngelesDepartment of Bioengineering, University of California, Los AngelesDepartment of Bioengineering, University of California, Los AngelesDepartment of Bioengineering, University of California, Los AngelesDepartment of Bioengineering, University of California, Los AngelesDepartment of Bioengineering, University of California, Los AngelesAbstract Voice disorders resulting from various pathological vocal fold conditions or postoperative recovery of laryngeal cancer surgeries, are common causes of dysphonia. Here, we present a self-powered wearable sensing-actuation system based on soft magnetoelasticity that enables assisted speaking without relying on the vocal folds. It holds a lightweighted mass of approximately 7.2 g, skin-alike modulus of 7.83 × 105 Pa, stability against skin perspiration, and a maximum stretchability of 164%. The wearable sensing component can effectively capture extrinsic laryngeal muscle movement and convert them into high-fidelity and analyzable electrical signals, which can be translated into speech signals with the assistance of machine learning algorithms with an accuracy of 94.68%. Then, with the wearable actuation component, the speech could be expressed as voice signals while circumventing vocal fold vibration. We expect this approach could facilitate the restoration of normal voice function and significantly enhance the quality of life for patients with dysfunctional vocal folds.https://doi.org/10.1038/s41467-024-45915-7 |
| spellingShingle | Ziyuan Che Xiao Wan Jing Xu Chrystal Duan Tianqi Zheng Jun Chen Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system Nature Communications |
| title | Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system |
| title_full | Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system |
| title_fullStr | Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system |
| title_full_unstemmed | Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system |
| title_short | Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system |
| title_sort | speaking without vocal folds using a machine learning assisted wearable sensing actuation system |
| url | https://doi.org/10.1038/s41467-024-45915-7 |
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