FENG used to predict Angular Velocity Work

Here is presented the data used for the paper titled "Assessment of Head Dynamics using a Flexible Self-Powered Sensor and Machine Learning, capable of predicting probability of Brain Injury". Authors: Gerardo L. Morales-Torres*, Ian González-Afanador, Luis A. Colón-Santiago, Nelson Sepúlveda. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, 48824, Michigan, United States https://doi.org/10.1016/j.nwnano.2025.100076 Abstract This work presents the application of a flexible, self-powered sensor designed to predict angular velocity and acceleration during head kinematics associated with concussions. This paper-thin, flexible device, which exhibits piezoelectric-like properties, is strategically placed on the back of a human head substitute to capture stress and strain in this region during whiplash events. The mechanical energy generated by varying magnitudes of whiplash is converted into electrical pulses, which are then integrated with multiple machine learning models. These models were tested and compared, demonstrating their ability to accurately predict angular velocity and acceleration of the head. This predictive capability can be utilized to assess the probability of brain injury. The findings demonstrate that this system not only enhances the understanding of head impact dynamics, but also opens avenues for developing more effective injury risk assessment tools. By combining innovative sensor technology with advanced machine learning techniques, this study contributes to improved safety monitoring in high-risk environments, such as high-contact and automotive sports. The videos show the dummy head drop for three different heights with the FENG voltage, derivative and the angular velocity of the head. The FENG csv files contain the data of the ferro-electret nano-generator voltage. The HEAD csv files contain the data of the angular velocity captured by sensors inside the dummy head. The python code is also presented , where all the processing and training was done.本数据集对应论文《基于柔性自供电传感器与机器学习评估头部动力学、可预测脑损伤概率》（Assessment of Head Dynamics using a Flexible Self-Powered Sensor and Machine Learning, capable of predicting probability of Brain Injury）。 作者：Gerardo L. Morales-Torres*、Ian González-Afanador、Luis A. Colón-Santiago、Nelson Sepúlveda。 单位：密歇根州立大学电气与计算机工程系，美国密歇根州东兰辛，48824。 DOI链接：https://doi.org/10.1016/j.nwnano.2025.100076 ### 摘要 本研究展示了一款柔性自供电传感器的应用场景，该传感器可用于预测与脑震荡相关的头部运动学指标中的角速度与角加速度。这款薄如纸张的柔性器件具备类压电特性，被精准安置于人体头部替身的后枕区域，以捕捉挥鞭样损伤事件中该区域的应力与应变。不同强度挥鞭动作产生的机械能会被转化为电脉冲，随后将这些脉冲信号与多种机器学习模型进行融合分析。经测试与对比验证，这些模型能够精准预测头部的角速度与角加速度。该预测能力可用于评估脑损伤的发生概率。研究结果表明，这套系统不仅加深了学界对头部冲击动力学的理解，还为开发更高效的损伤风险评估工具开辟了新路径。通过将创新传感器技术与先进机器学习技术相结合，本研究有助于提升高风险场景（如高接触类运动与汽车运动）中的安全监测水平。 本次公开的演示视频展示了三种不同坠落高度下的假人头跌落实验，并同步呈现铁电驻极体纳米发电机（ferro-electret nano-generator，FENG）的电压、电压导数以及头部角速度数据。FENG相关CSV文件包含该铁电驻极体纳米发电机的电压采集数据；HEAD相关CSV文件包含假人头内部传感器采集的角速度数据。同时公开了本次研究使用的Python代码，涵盖全部数据处理与模型训练的完整流程。