Accurate and Noninvasive Dysphagia Assessment via a Soft High‐Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles
Abstract Accurate, noninvasive dysphagia assessment is important for rehabilitation therapy but current clinical diagnostic methods are either invasive or subjective. Surface electromyography (sEMG) that monitors muscle activity during swallowing, offers a promising alternative. However, existing sE...
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Wiley
2025-07-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202500472 |
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| author | Weijie Hong Lin Mao Kai Lin Chongyuan Huang Yanyan Su Shun Zhang Chengjun Wang Daming Wang Jizhou Song Zuobing Chen |
| author_facet | Weijie Hong Lin Mao Kai Lin Chongyuan Huang Yanyan Su Shun Zhang Chengjun Wang Daming Wang Jizhou Song Zuobing Chen |
| author_sort | Weijie Hong |
| collection | DOAJ |
| description | Abstract Accurate, noninvasive dysphagia assessment is important for rehabilitation therapy but current clinical diagnostic methods are either invasive or subjective. Surface electromyography (sEMG) that monitors muscle activity during swallowing, offers a promising alternative. However, existing sEMG electrode arrays for dysphagia assessment remain challenging in combining the advantages of a large coverage area and strong compliance to the entire swallowing muscles. Here, we report a stretchable, breathable, large‐area high‐density sEMG (HD‐sEMG) electrode array, which enables intimate contact to complex surface of the submental and infrahyoid muscles to detect high‐fidelity HD‐sEMG signals during swallowing. The electrode array features a 64‐channel soft on‐skin sensing array for comprehensive data capture, and a stiff connector for simple and reliable connection to an external acquisition setup. Systemically experimental studies revealed the easy operability of the soft HD‐sEMG electrode array for effortless integration with the skin, as well as the excellent mechanical and electrical characteristics even subject to substantial skin deformations. By comparing HD‐sEMG signals collected from 38 participants, three objective indicators for quantitative dysphagia evaluation were discussed. Finally, a machine learning model was developed to accurately and automatically classify the severity of dysphagia, and the factors affecting the recognition accuracy of the model were discussed in depth. |
| format | Article |
| id | doaj-art-bcf66254f6844d448bbd238c1c6ef77f |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-bcf66254f6844d448bbd238c1c6ef77f2025-08-20T03:28:58ZengWileyAdvanced Science2198-38442025-07-011225n/an/a10.1002/advs.202500472Accurate and Noninvasive Dysphagia Assessment via a Soft High‐Density sEMG Electrode Array Conformal to the Submental and Infrahyoid MusclesWeijie Hong0Lin Mao1Kai Lin2Chongyuan Huang3Yanyan Su4Shun Zhang5Chengjun Wang6Daming Wang7Jizhou Song8Zuobing Chen9Department of Rehabilitation Medicine The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou 310003 ChinaDepartment of Rehabilitation Medicine The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou 310003 ChinaKey Laboratory of Soft Machines and Smart Devices of Zhejiang Province State Key Laboratory of Brain‐Machine Intelligence Department of Engineering Mechanics Zhejiang University Hangzhou 310027 ChinaKey Laboratory of Soft Machines and Smart Devices of Zhejiang Province State Key Laboratory of Brain‐Machine Intelligence Department of Engineering Mechanics Zhejiang University Hangzhou 310027 ChinaKey Laboratory of Soft Machines and Smart Devices of Zhejiang Province State Key Laboratory of Brain‐Machine Intelligence Department of Engineering Mechanics Zhejiang University Hangzhou 310027 ChinaDepartment of Rehabilitation Medicine The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou 310003 ChinaDepartment of Rehabilitation Medicine The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou 310003 ChinaDepartment of Rehabilitation Medicine The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou 310003 ChinaDepartment of Rehabilitation Medicine The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou 310003 ChinaDepartment of Rehabilitation Medicine The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou 310003 ChinaAbstract Accurate, noninvasive dysphagia assessment is important for rehabilitation therapy but current clinical diagnostic methods are either invasive or subjective. Surface electromyography (sEMG) that monitors muscle activity during swallowing, offers a promising alternative. However, existing sEMG electrode arrays for dysphagia assessment remain challenging in combining the advantages of a large coverage area and strong compliance to the entire swallowing muscles. Here, we report a stretchable, breathable, large‐area high‐density sEMG (HD‐sEMG) electrode array, which enables intimate contact to complex surface of the submental and infrahyoid muscles to detect high‐fidelity HD‐sEMG signals during swallowing. The electrode array features a 64‐channel soft on‐skin sensing array for comprehensive data capture, and a stiff connector for simple and reliable connection to an external acquisition setup. Systemically experimental studies revealed the easy operability of the soft HD‐sEMG electrode array for effortless integration with the skin, as well as the excellent mechanical and electrical characteristics even subject to substantial skin deformations. By comparing HD‐sEMG signals collected from 38 participants, three objective indicators for quantitative dysphagia evaluation were discussed. Finally, a machine learning model was developed to accurately and automatically classify the severity of dysphagia, and the factors affecting the recognition accuracy of the model were discussed in depth.https://doi.org/10.1002/advs.202500472dysphagia assessmentmachine learningstretchable high‐density sEMGswallowing |
| spellingShingle | Weijie Hong Lin Mao Kai Lin Chongyuan Huang Yanyan Su Shun Zhang Chengjun Wang Daming Wang Jizhou Song Zuobing Chen Accurate and Noninvasive Dysphagia Assessment via a Soft High‐Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles Advanced Science dysphagia assessment machine learning stretchable high‐density sEMG swallowing |
| title | Accurate and Noninvasive Dysphagia Assessment via a Soft High‐Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles |
| title_full | Accurate and Noninvasive Dysphagia Assessment via a Soft High‐Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles |
| title_fullStr | Accurate and Noninvasive Dysphagia Assessment via a Soft High‐Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles |
| title_full_unstemmed | Accurate and Noninvasive Dysphagia Assessment via a Soft High‐Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles |
| title_short | Accurate and Noninvasive Dysphagia Assessment via a Soft High‐Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles |
| title_sort | accurate and noninvasive dysphagia assessment via a soft high density semg electrode array conformal to the submental and infrahyoid muscles |
| topic | dysphagia assessment machine learning stretchable high‐density sEMG swallowing |
| url | https://doi.org/10.1002/advs.202500472 |
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