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.
Y. Zhuang et al., "A Fiber-Optic Sensor-Embedded And Machine Learning Assisted Smart Helmet For Multi-Variable Blunt Force Impact Sensing In Real Time," Biosensors, vol. 12, no. 12, article no. 1159, MDPI, Dec 2022.
The definitive version is available at https://doi.org/10.3390/bios12121159
Materials Science and Engineering
Electrical and Computer Engineering
Keywords and Phrases
bunt force impact; fiber Bragg grating; fiber-optic sensor; machine learning; mild traumatic brain injury
International Standard Serial Number (ISSN)
Article - Journal
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01 Dec 2022