论文标题
通过卷积神经网络的固定仪器的齿轮测量
Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network
论文作者
论文摘要
由于与身体不完美的接口，用于测量头运动学的可穿戴传感器可能会嘈杂。在创伤性脑损伤（TBI）研究的影响期间，使用弹壳来测量头部运动学，但是由于潜在的松散性，仍可能发生与参考运动学的偏差。在这项研究中，深度学习用于补偿不完善的界面并提高测量精度。开发了一组一维卷积神经网络（1D-CNN）模型，以沿三个线性加速度和角速度的三个空间轴沿三个空间轴进行齿状齿轮运动测量。与参考运动学相比，脱氧运动学的误差显着降低，并且通过有限元元素建模计算出的脑损伤标准和组织应变和应变率的误差降低。还对1D-CNN模型进行了在大学橄榄球影响和验尸后的人类主题数据集的现场数据集上进行了测试，并观察到了类似的DeNoising效应。这些模型可用于改善对头部影响和TBI风险评估的检测，并可能扩展到测量运动学的其他传感器。
Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body. Mouthguards are used to measure head kinematics during impacts in traumatic brain injury (TBI) studies, but deviations from reference kinematics can still occur due to potential looseness. In this study, deep learning is used to compensate for the imperfect interface and improve measurement accuracy. A set of one-dimensional convolutional neural network (1D-CNN) models was developed to denoise mouthguard kinematics measurements along three spatial axes of linear acceleration and angular velocity. The denoised kinematics had significantly reduced errors compared to reference kinematics, and reduced errors in brain injury criteria and tissue strain and strain rate calculated via finite element modeling. The 1D-CNN models were also tested on an on-field dataset of college football impacts and a post-mortem human subject dataset, with similar denoising effects observed. The models can be used to improve detection of head impacts and TBI risk evaluation, and potentially extended to other sensors measuring kinematics.
