In next-generation network architecture, the Cybertwin drove the sixth generation of cellular networks sixth-generation (6G) to play an active role in many applications, such as healthcare and computer vision. Although the previous sixth-generation (5G) network provides the concept of edge cloud and core cloud, the internal communication mechanism has not been explained with a specific application. This article introduces a possible Cybertwin based multimodal network (beyond 5G) for electrocardiogram (ECG) patterns monitoring during daily activity. This network paradigm consists of a cloud-centric network and several Cybertwin communication ends. The Cybertwin nodes combine support locator/identifier identification, data caching, behavior logger, and communications assistant in the edge cloud. The application focuses on monitoring the ECG patterns during daily activity because few studies analyze them under different motions. We present a novel deep convolutional neural network based human activity recognition classifier to enhance identification accuracy. The healthcare monitoring values and potential clinical medicine are provided by the Cybertwin based network for ECG patterns observing.

中文翻译：

---

基于 Cyber​​twin 的多模态网络，利用深度学习进行心电图模式监测


在下一代网络架构中，Cyber​​twin驱动第六代蜂窝网络第六代（6G）在医疗保健、计算机视觉等众多应用中发挥积极作用。此前的第六代（5G）网络虽然提供了边缘云和核心云的概念，但内部的通信机制并没有用具体的应用来解释。本文介绍了一种可能的基于 Cyber​​twin 的多模式网络（超越 5G），用于日常活动期间的心电图 (ECG) 模式监测。该网络范式由以云为中心的网络和多个 Cyber​​twin 通信端组成。 Cyber​​twin 节点结合了边缘云中的支持定位器/标识符识别、数据缓存、行为记录器和通信助手。该应用程序的重点是监测日常活动期间的心电图模式，因为很少有研究分析不同运动下的心电图模式。我们提出了一种新颖的基于深度卷积神经网络的人体活动识别分类器，以提高识别精度。医疗保健监测值和潜在的临床医学由基于 Cyber​​twin 的心电图模式观察网络提供。