A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time
Early on-site diagnosis of mild traumatic brain injury (mTBI) will provide the best guidance for clinical practice. However, existing methods and sensors cannot provide sufficiently detailed physical information related to the blunt force impact. In the present work, a smart helmet with a single emb...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Biosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6374/12/12/1159 |
| _version_ | 1797461185707114496 |
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| author | Yiyang Zhuang Taihao Han Qingbo Yang Ryan O’Malley Aditya Kumar Rex E. Gerald Jie Huang |
| author_facet | Yiyang Zhuang Taihao Han Qingbo Yang Ryan O’Malley Aditya Kumar Rex E. Gerald Jie Huang |
| author_sort | Yiyang Zhuang |
| collection | DOAJ |
| description | Early on-site diagnosis of mild traumatic brain injury (mTBI) will provide the best guidance for clinical practice. However, existing methods and sensors cannot provide sufficiently detailed physical information related to the blunt force impact. In the present work, a smart helmet with a single embedded fiber Bragg grating (FBG) sensor is developed, which can monitor complex blunt force impact events in real time under both wired and wireless modes. The transient oscillatory signal “fingerprint” can specifically reflect the impact-caused physical deformation of the local helmet structure. By combination with machine learning algorithms, the unknown transient impact can be recognized quickly and accurately in terms of impact magnitude, direction, and latitude. Optimization of the training dataset was also validated, and the boosted ML models, such as the S-SVM+ and S-IBK+, are able to predict accurately with complex databases. Thus, the ML-FBG smart helmet system developed by this work may become a crucial intervention alternative during a traumatic brain injury event. |
| first_indexed | 2024-03-09T17:16:47Z |
| format | Article |
| id | doaj.art-f466404e1a174da09403434adafcce7a |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-09T17:16:47Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-f466404e1a174da09403434adafcce7a2023-11-24T13:37:35ZengMDPI AGBiosensors2079-63742022-12-011212115910.3390/bios12121159A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real TimeYiyang Zhuang0Taihao Han1Qingbo Yang2Ryan O’Malley3Aditya Kumar4Rex E. Gerald5Jie Huang6Research Center for Optical Fiber Sensing, Zhejiang Laboratory, Hangzhou 311121, ChinaDepartment of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USACooperative Research, College of Agriculture, Environmental and Human Sciences, Lincoln University of Missouri, Jefferson City, MO 65102, USADepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USADepartment of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USADepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USADepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USAEarly on-site diagnosis of mild traumatic brain injury (mTBI) will provide the best guidance for clinical practice. However, existing methods and sensors cannot provide sufficiently detailed physical information related to the blunt force impact. In the present work, a smart helmet with a single embedded fiber Bragg grating (FBG) sensor is developed, which can monitor complex blunt force impact events in real time under both wired and wireless modes. The transient oscillatory signal “fingerprint” can specifically reflect the impact-caused physical deformation of the local helmet structure. By combination with machine learning algorithms, the unknown transient impact can be recognized quickly and accurately in terms of impact magnitude, direction, and latitude. Optimization of the training dataset was also validated, and the boosted ML models, such as the S-SVM+ and S-IBK+, are able to predict accurately with complex databases. Thus, the ML-FBG smart helmet system developed by this work may become a crucial intervention alternative during a traumatic brain injury event.https://www.mdpi.com/2079-6374/12/12/1159mild traumatic brain injuryfiber-optic sensorfiber Bragg gratingmachine learningbunt force impact |
| spellingShingle | Yiyang Zhuang Taihao Han Qingbo Yang Ryan O’Malley Aditya Kumar Rex E. Gerald Jie Huang A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time Biosensors mild traumatic brain injury fiber-optic sensor fiber Bragg grating machine learning bunt force impact |
| title | A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time |
| title_full | A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time |
| title_fullStr | A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time |
| title_full_unstemmed | A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time |
| title_short | A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time |
| title_sort | fiber optic sensor embedded and machine learning assisted smart helmet for multi variable blunt force impact sensing in real time |
| topic | mild traumatic brain injury fiber-optic sensor fiber Bragg grating machine learning bunt force impact |
| url | https://www.mdpi.com/2079-6374/12/12/1159 |
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