In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Monitoring player impacts is vitally important to understanding and protecting from injuries like concussion. Typically, to analyze this data, a combination of video analysis and sensor data is used to ascertain the recorded events are true impacts and not false positives. In fact, due to the nature of using wearable devices in sports, false positives vastly outnumber the true positives. Yet, manual video analysis is time-consuming. This imbalance leads traditional machine learning approaches to exhibit poor performance in both detecting true positives and preventing false negatives. Here, we show that by simulating head impacts numerically using a standard Finite Element head-neck model, a large dataset of synthetic impacts can be created to augment the gathered, verified, impact data from mouthguards. This combined physics-informed machine learning impact detector reported improved performance on test datasets compared to traditional impact detectors with negative predictive value and positive predictive values of 88% and 87% respectively. Consequently, this model reported the best results to date for an impact detection algorithm for American Football, achieving an F1 score of 0.95. In addition, this physics-informed machine learning impact detector was able to accurately detect true and false impacts from a test dataset at a rate of 90% and 100% relative to a purely manual video analysis workflow. Saving over 12 hours of manual video analysis for a modest dataset, at an overall accuracy of 92%, these results indicate that this model could be used in place of, or alongside, traditional video analysis to allow for larger scale and more efficient impact detection in sports such as American Football.

中文翻译：

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基于物理的机器学习改进了对头部撞击的检测

在这项工作中，我们提出了一种新的基于物理的机器学习模型，可用于分析来自仪器化护齿器的运动学数据并检测对头部的影响。监测球员的影响对于了解和防止脑震荡等伤害至关重要。通常，为了分析这些数据，会结合使用视频分析和传感器数据来确定记录的事件是真实的影响而不是误报。事实上，由于在体育运动中使用可穿戴设备的性质，误报远远超过真阳性。然而，手动视频分析非常耗时。这种不平衡导致传统的机器学习方法在检测真阳性和防止假阴性方面表现不佳。在这里，我们表明，通过使用标准有限元头颈部模型对头部撞击进行数值模拟，可以创建一个大型合成撞击数据集，以增强从护齿器收集、验证的撞击数据。与传统的撞击检测器相比，这种结合物理信息的机器学习撞击检测器在测试数据集上的性能有所提高，阴性预测值和阳性预测值分别为 88% 和 87%。因此，该模型报告了迄今为止美式足球撞击检测算法的最佳结果，F1 得分为 0.95。此外，相对于纯手动视频分析工作流程，这种基于物理的机器学习影响检测器能够以 90% 和 100% 的比率准确检测来自测试数据集的真假影响。