目标:由重复性头部创伤的风险驱动,传感器已被集成到护口器中,以测量接触运动和军事活动中的头部撞击。这些可穿戴设备,各种研究小组和组织正在积极开发被称为“仪表化”或“智能”的护口器。这些仪器化的护口器提供了一个机会,以进一步研究和了解大脑生物力学,由于影响。在这项研究中,我们提供了一种大脑建模服务,可以使用这些传感器的信息以自动方式预测脑损伤指标。
方法:我们使用亚马逊的几个Web服务(AWS)构建了一个大脑建模平台,以实现云计算和可扩展性。我们使用定制的基于云的有限元建模代码来计算颅内脑组织的基于物理的非线性响应,并为从事头部碰撞传感器技术的小组提供前端Web应用程序和应用程序编程接口,以将模拟损伤预测纳入他们的研究管道。
结果:平台结果已根据文献中有关脑-颅骨相对位移的实验数据进行了验证,脑部劳损和颅内压.平台的并行处理能力也得到了测试和验证。我们还研究了Avatar3D生成的自定义头部表面的准确性。
结论:我们提出了一个经过验证的基于云的计算大脑建模平台,该平台使用传感器数据作为数值大脑模型的输入,并输出脑组织应变和损伤指标的定量描述。该平台有望产生透明度,可重复,和可追踪的大脑计算结果。
OBJECTIVE: Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as \"instrumented\" or \"smart\" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion.
METHODS: We have built a brain modeling platform using several of Amazon\'s Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline.
RESULTS: The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D.
CONCLUSIONS: We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.