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.

中文翻译：

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光纤传感器嵌入式智能头盔可通过机器学习进行实时冲击感测和分析

背景

轻度创伤性脑损伤（mTBI）与慢性神经退行性损害（如创伤后应激障碍（PTSD）和轻度认知损害）密切相关。早期发现脑震荡事件将大大增进对头部损伤的了解，并为紧急诊断和更好的临床实践提供更好的指导，以实现完全康复。

新方法

开发了一种带有单个嵌入式光纤布拉格光栅（FBG）传感器的智能头盔，用于实时感知对头盔的钝力撞击事件。瞬态信号提供有关冲击事件的幅度和方向信息，并且该数据可用于训练机器学习（ML）模型。

结果

嵌入FBG的智能头盔原型成功实现了对震荡事件的实时感测。发现由冲击的大小和方向组成的瞬态数据“指纹”与钝力冲击器的类型相关。受过训练的ML模型是能够准确地预测（R ²〜0.90）从没有用于模型的训练数据钝力冲击事件的大小和方向。

与现有方法的比较

智能头盔数据与使用ML模型的分析相结合，可以准确预测造成事件的冲击器的类型，以及冲击力的大小和方向，而这是使用现有设备无法获得的。

结论

这项工作产生了一种使用ML辅助，FBG嵌入式的智能头盔，该头盔使用高度精确的多指标策略实时识别脑震荡事件。ML-FBG智能头盔系统的使用可以在发生脑震荡事件期间或之后立即作为早期干预策略。