Deep Learning‐Enabled MXene/PEDOT:PSS Acoustic Sensor for Speech Recognition and Skin‐Vibration Detection
Flexible acoustic sensors with high sensitivity, excellent mechanical strength, and easy integration are urgently needed for wearable electronics. MXene holds great promise as a sensing material for this application. However, low flexibility and stability limit the performance of MXene‐based composi...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-10-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202200140 |
| _version_ | 1828327195058110464 |
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| author | Huijun Ding Zhenping Zeng Ziwei Wang Xiaolin Li Tanju Yildirim Qinlin Xie Han Zhang Swelm Wageh Ahmed A. Al-Ghamdi Xi Zhang Bo Wen |
| author_facet | Huijun Ding Zhenping Zeng Ziwei Wang Xiaolin Li Tanju Yildirim Qinlin Xie Han Zhang Swelm Wageh Ahmed A. Al-Ghamdi Xi Zhang Bo Wen |
| author_sort | Huijun Ding |
| collection | DOAJ |
| description | Flexible acoustic sensors with high sensitivity, excellent mechanical strength, and easy integration are urgently needed for wearable electronics. MXene holds great promise as a sensing material for this application. However, low flexibility and stability limit the performance of MXene‐based composites. To alleviate the aforementioned issue, a flexible pressure sensor based on MXene/poly(3,4‐ethylenediox‐ythiophene)‐poly(styrenesulfonate) (PEDOT:PSS) is fabricated and used as an acoustic sensor inhibiting high sensitivity, fast response time (57 ms), ultra‐thin thickness (30 μm), and remarkable stability. Excellent performance enables the sensor to detect and identify weak muscle movements and skin vibrations, such as word pronunciation and carotid artery pulse. Furthermore, by combining the proposed deep learning model based on number recognition convolutional neural network (NR‐CNN), speech recognition toward different pronunciations of numbers that appear frequently in daily conversations can be realized. High recognition accuracy (91%) is achieved by training and testing the proposed NR‐CNN with large amounts of data recorded by the sensor. Results demonstrate that the flexible and wearable MXene/PEDOT:PSS acoustic sensor accelerates intelligent artificial acoustics and possesses great potential for applications involving speech recognition and health monitoring. |
| first_indexed | 2024-04-13T19:52:04Z |
| format | Article |
| id | doaj.art-adb55a5e919d4565b9884b6d9f1c870e |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-04-13T19:52:04Z |
| publishDate | 2022-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-adb55a5e919d4565b9884b6d9f1c870e2022-12-22T02:32:30ZengWileyAdvanced Intelligent Systems2640-45672022-10-01410n/an/a10.1002/aisy.202200140Deep Learning‐Enabled MXene/PEDOT:PSS Acoustic Sensor for Speech Recognition and Skin‐Vibration DetectionHuijun Ding0Zhenping Zeng1Ziwei Wang2Xiaolin Li3Tanju Yildirim4Qinlin Xie5Han Zhang6Swelm Wageh7Ahmed A. Al-Ghamdi8Xi Zhang9Bo Wen10Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging and Guangdong Provincial Key Laboratory of Regional Immunity and Diseases Department of Pathology Health Science Center Shenzhen University Shenzhen 518060 P. R. ChinaGuangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging and Guangdong Provincial Key Laboratory of Regional Immunity and Diseases Department of Pathology Health Science Center Shenzhen University Shenzhen 518060 P. R. ChinaGuangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging and Guangdong Provincial Key Laboratory of Regional Immunity and Diseases Department of Pathology Health Science Center Shenzhen University Shenzhen 518060 P. R. ChinaInstitute of Nanosurface Science and Engineering Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering Shenzhen University Shenzhen 518060 P. R. ChinaCenter for Functional Sensor and Actuator (CFSN) National Institute for Materials Science (NIMS) Tsukuba Ibaraki 305-0044 JapanGuangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging and Guangdong Provincial Key Laboratory of Regional Immunity and Diseases Department of Pathology Health Science Center Shenzhen University Shenzhen 518060 P. R. ChinaInstitute of Microscale Optoelectronics Collaborative Innovation Centre for Optoelectronic Science and Technology Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering, Shenzhen Key Laboratory of Micro-Nano Photonic Information Technology Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen University Shenzhen 518060 P. R. ChinaDepartment of Physics Faculty of Science King Abdulaziz University Jeddah 21589 Saudi ArabiaDepartment of Physics Faculty of Science King Abdulaziz University Jeddah 21589 Saudi ArabiaInstitute of Nanosurface Science and Engineering Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering Shenzhen University Shenzhen 518060 P. R. ChinaInstitute of Nanosurface Science and Engineering Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering Shenzhen University Shenzhen 518060 P. R. ChinaFlexible acoustic sensors with high sensitivity, excellent mechanical strength, and easy integration are urgently needed for wearable electronics. MXene holds great promise as a sensing material for this application. However, low flexibility and stability limit the performance of MXene‐based composites. To alleviate the aforementioned issue, a flexible pressure sensor based on MXene/poly(3,4‐ethylenediox‐ythiophene)‐poly(styrenesulfonate) (PEDOT:PSS) is fabricated and used as an acoustic sensor inhibiting high sensitivity, fast response time (57 ms), ultra‐thin thickness (30 μm), and remarkable stability. Excellent performance enables the sensor to detect and identify weak muscle movements and skin vibrations, such as word pronunciation and carotid artery pulse. Furthermore, by combining the proposed deep learning model based on number recognition convolutional neural network (NR‐CNN), speech recognition toward different pronunciations of numbers that appear frequently in daily conversations can be realized. High recognition accuracy (91%) is achieved by training and testing the proposed NR‐CNN with large amounts of data recorded by the sensor. Results demonstrate that the flexible and wearable MXene/PEDOT:PSS acoustic sensor accelerates intelligent artificial acoustics and possesses great potential for applications involving speech recognition and health monitoring.https://doi.org/10.1002/aisy.202200140acoustic sensorartificial throatcomposite filmdeep-learningmicro-electronic structure |
| spellingShingle | Huijun Ding Zhenping Zeng Ziwei Wang Xiaolin Li Tanju Yildirim Qinlin Xie Han Zhang Swelm Wageh Ahmed A. Al-Ghamdi Xi Zhang Bo Wen Deep Learning‐Enabled MXene/PEDOT:PSS Acoustic Sensor for Speech Recognition and Skin‐Vibration Detection Advanced Intelligent Systems acoustic sensor artificial throat composite film deep-learning micro-electronic structure |
| title | Deep Learning‐Enabled MXene/PEDOT:PSS Acoustic Sensor for Speech Recognition and Skin‐Vibration Detection |
| title_full | Deep Learning‐Enabled MXene/PEDOT:PSS Acoustic Sensor for Speech Recognition and Skin‐Vibration Detection |
| title_fullStr | Deep Learning‐Enabled MXene/PEDOT:PSS Acoustic Sensor for Speech Recognition and Skin‐Vibration Detection |
| title_full_unstemmed | Deep Learning‐Enabled MXene/PEDOT:PSS Acoustic Sensor for Speech Recognition and Skin‐Vibration Detection |
| title_short | Deep Learning‐Enabled MXene/PEDOT:PSS Acoustic Sensor for Speech Recognition and Skin‐Vibration Detection |
| title_sort | deep learning enabled mxene pedot pss acoustic sensor for speech recognition and skin vibration detection |
| topic | acoustic sensor artificial throat composite film deep-learning micro-electronic structure |
| url | https://doi.org/10.1002/aisy.202200140 |
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