Fiber Optic Sensor Embedded Smart Helmet for Real-Time Impact Sensing and Analysis through Machine Learning

Background: Mild traumatic brain injury (mTBI) strongly associates with chronic neurodegenerative impairments such as post-traumatic stress disorder (PTSD) and mild cognitive impairment. Early detection of concussive events would significantly enhance the understanding of head injuries and provide better guidance for urgent diagnoses and the best clinical practices for achieving full recovery. New method: A smart helmet was developed with a single embedded fiber Bragg grating (FBG) sensor for real-time sensing of blunt-force impact events to helmets. The transient signals provide both magnitude and directional information about the impact event, and the data can be used for training machine learning (ML) models. Results: The FBG-embedded smart helmet prototype successfully achieved real-time sensing of concussive events. Transient data “fingerprints” consisting of both magnitude and direction of impact, were found to correlate with types of blunt-force impactors. Trained ML models were able to accurately predict (R2 ∼ 0.90) the magnitudes and directions of blunt-force impact events from data not used for model training. Comparison with existing methods: The combination of the smart helmet data with analyses using ML models provides accurate predictions of the types of impactors that caused the events, as well as the magnitudes and the directions of the impact forces, which are unavailable using existing devices. Conclusion: This work resulted in an ML-assisted, FBG-embedded smart helmet for real-time identification of concussive events using a highly accurate multi-metric strategy. The use of ML-FBG smart helmet systems can serve as an early-stage intervention strategy during and immediately following a concussive event

Opto-tactile sensor for surface texture pattern identification using support vector machine

Experimental application of a recently developed opto-tactile sensor in object surface texture pattern recognition using soft computational techniques has been successfully demonstrated in this article. Design and working principles of a number of optical type sensors have been illustrated and explained. Using the opto-tactile sensor multiple surface texture patterns of a number of objects like a carpet, stone, rough sheet metal, paper carton and a table surface have been captured and saved in MATLAB environment. The captured data have been adopted to soft computational techniques like Support Vector Machine (SVM) technique, Decision Tree (DT) C4.5 algorithm, and Naive Bayes (NB) algorithm for their learning. Testing with unknown surfaces using these techniques shows promising results at this stage and demonstrates its potential industrial use with further development. Results suggest that the methodology and procedures presented here are well suited for applications in intelligent robotic grasping

Opto-tactile sensor for surface texture pattern identification using support vector machine

Experimental application of a recently developed opto-tactile sensor in object surface texture pattern recognition using soft computational techniques has been successfully demonstrated in this article. Design and working principles of a number of optical type sensors have been illustrated and explained. Using the opto-tactile sensor multiple surface texture patterns of a number of objects like a carpet, stone, rough sheet metal, paper carton and a table surface have been captured and saved in MATLAB environment. The captured data have been adopted to soft computational techniques like Support Vector Machine (SVM) technique, Decision Tree (DT) C4.5 algorithm, and Naive Bayes (NB) algorithm for their learning. Testing with unknown surfaces using these techniques shows promising results at this stage and demonstrates its potential industrial use with further development. Results suggest that the methodology and procedures presented here are well suited for applications in intelligent robotic grasping

Opto-tactile sensor for surface texture pattern identification using support vector machine

Experimental application of a recently developed opto-tactile sensor in object surface texture pattern recognition using soft computational techniques has been successfully demonstrated in this article. Design and working principles of a number of optical type sensors have been illustrated and explained. Using the opto-tactile sensor multiple surface texture patterns of a number of objects like a carpet, stone, rough sheet metal, paper carton and a table surface have been captured and saved in MATLAB environment. The captured data have been adopted to soft computational techniques like Support Vector Machine (SVM) technique, Decision Tree (DT) C4.5 algorithm, and Naive Bayes (NB) algorithm for their learning. Testing with unknown surfaces using these techniques shows promising results at this stage and demonstrates its potential industrial use with further development. Results suggest that the methodology and procedures presented here are well suited for applications in intelligent robotic grasping

Fiber optic sensors for industry and military applications

Fiber optic sensors (FOSs) have been widely used for measuring various physical and chemical measurands owing to their unique advantages over traditional sensors such as small size, high resolution, distributed sensing capabilities, and immunity to electromagnetic interference. This dissertation focuses on the development of robust FOSs with ultrahigh sensitivity and their applications in industry and military areas. Firstly, novel fiber-optic extrinsic Fabry-Perot interferometer (EFPI) inclinometers for one- and two-dimensional tilt measurements with 20 nrad resolution were demonstrated. Compared to in-line fiber optic inclinometers, an extrinsic sensing motif was used in our prototype inclinometer. The variations in tilt angle of the inclinometer was converted into the cavity length changes of the EFPI which can be accurately measured with high resolution. The developed fiber optic inclinometers showed high resolution and great temperature stability in both experiments and practical applications. Secondly, a smart helmet was developed with a single embedded fiber Bragg grating (FBG) sensor for real-time sensing of blunt-force impact events to helmets. The combination of the transient impact data from FBG and the analyses using machine-learning model provides accurate predictions of the magnitudes, the directions and the types of the impact events. The use of the developed smart helmet system can serve as an early-stage intervention strategy for mitigating and managing traumatic brain injuries within the Golden Hour --Abstract, page iv