A Cybertwin-based multimodal network utilizing a deep convolutional neural network classifier was proposed to monitor ECG patterns and enhance human activity recognition during daily activities.
A novel Cybertwin based multimodal network and deep learning classifier may enhance the accuracy of ECG pattern monitoring during daily activities.
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.
Qi et al. (2022) studied ECG patterns monitoring. Cybertwin based multimodal network and deep CNN classifier was evaluated. A Cybertwin-based multimodal network utilizing a deep convolutional neural network classifier was proposed to monitor ECG patterns and enhance human activity recognition during daily activities.