Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3021-3024. doi: 10.1109/EMBC46164.2021.9630216.
Computer-assisted tools for preterm infants' movement monitoring in neonatal intensive care unit (NICU) could support clinicians in highlighting preterm-birth complications. With such a view, in this work we propose a deep-learning framework for preterm infants' pose estimation from depth videos acquired in the actual clinical practice. The pipeline consists of two consecutive convolutional neural networks (CNNs). The first CNN (inherited from our previous work) acts to roughly predict joints and joint-connections position, while the second CNN (Asy-regression CNN) refines such predictions to trace the limb pose. Asy-regression relies on asymmetric convolutions to temporally optimize both the training and predictions phase. Compared to its counterpart without asymmetric convolutions, Asy-regression experiences a reduction in training and prediction time of 66% , while keeping the root mean square error, computed against manual pose annotation, merely unchanged. Research mostly works to develop highly accurate models, few efforts have been invested to make the training and deployment of such models time-effective. With a view to make these monitoring technologies sustainable, here we focused on the second aspect and addressed the problem of designing a framework as trade-off between reliability and efficiency.
计算机辅助工具可用于监测新生儿重症监护病房（NICU）中早产儿的运动，有助于临床医生发现早产儿出生并发症。有鉴于此，我们在这项工作中提出了一种从实际临床实践中获取的深度视频中估算早产儿姿势的深度学习框架。该流水线由两个连续的卷积神经网络（CNN）组成。第一个 CNN（源自我们之前的工作）用于粗略预测关节和关节连接的位置，而第二个 CNN（Asy-regression CNN）则对这些预测进行细化，以跟踪肢体姿势。Asy-regression 依赖于非对称卷积，在训练和预测阶段都能进行时间优化。与没有非对称卷积的对应模型相比，Asy-regression 的训练和预测时间减少了 66%，而与手动姿势标注计算的均方根误差基本保持不变。研究主要致力于开发高度精确的模型，很少有精力致力于使这些模型的训练和部署具有时间效率。为了使这些监测技术具有可持续性，我们在这里关注第二个方面，并解决了在可靠性和效率之间进行权衡的框架设计问题。
