论文标题
多任务临时注意力网络，用于开发非接触式活力测量
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
论文作者
论文摘要
在SARS-COV-2大流行期间，远程医疗和远程健康监测变得越来越重要，人们普遍认为这将对医疗保健实践产生持久的影响。这些工具可以帮助降低暴露患者和医务人员感染，使医疗服务更容易获得的风险，并允许提供者看到更多的患者。但是，如果没有与患者直接接触，对生命体征的客观测量是具有挑战性的。我们提出了一种基于视频的和设备的光学心肺生命体征测量方法。它利用了一种新型的多任务暂时性转移卷积注意网络（MTTS-CAN），并在移动平台上实现了心血管和呼吸测量。我们在高级RISC机器（ARM）CPU上评估系统，并以每秒超过150帧的速度运行，实现了实时应用程序。在大型基准数据集上进行系统的实验表明，我们的方法可导致误差的大量（20％-50％）的降低，并在整个数据集中概括。
Telehealth and remote health monitoring have become increasingly important during the SARS-CoV-2 pandemic and it is widely expected that this will have a lasting impact on healthcare practices. These tools can help reduce the risk of exposing patients and medical staff to infection, make healthcare services more accessible, and allow providers to see more patients. However, objective measurement of vital signs is challenging without direct contact with a patient. We present a video-based and on-device optical cardiopulmonary vital sign measurement approach. It leverages a novel multi-task temporal shift convolutional attention network (MTTS-CAN) and enables real-time cardiovascular and respiratory measurements on mobile platforms. We evaluate our system on an Advanced RISC Machine (ARM) CPU and achieve state-of-the-art accuracy while running at over 150 frames per second which enables real-time applications. Systematic experimentation on large benchmark datasets reveals that our approach leads to substantial (20%-50%) reductions in error and generalizes well across datasets.
