Exploring an Efficient Remote Biomedical Signal Monitoring Framework for Personal Health in the COVID-19 Pandemic.

Zhongyun Tang,Haiyang Hu,Kaidi Zhao,Chonghuan Xu

doi:10.3390/ijerph18179037

Abstract

Nowadays people are mostly focused on their work while ignoring their health which in turn is creating a drastic effect on their health in the long run. Remote health monitoring through telemedicine can help people discover potential health threats in time. In the COVID-19 pandemic, remote health monitoring can help obtain and analyze biomedical signals including human body temperature without direct body contact. This technique is of great significance to achieve safe and efficient health monitoring in the COVID-19 pandemic. Existing remote biomedical signal monitoring methods cannot effectively analyze the time series data. This paper designs a remote biomedical signal monitoring framework combining the Internet of Things (IoT), 5G communication and artificial intelligence techniques. In the constructed framework, IoT devices are used to collect biomedical signals at the perception layer. Subsequently, the biomedical signals are transmitted through the 5G network to the cloud server where the GRU-AE deep learning model is deployed. It is noteworthy that the proposed GRU-AE model can analyze multi-dimensional biomedical signals in time series. Finally, this paper conducts a 24-week monitoring experiment for 2000 subjects of different ages to obtain real data. Compared with the traditional biomedical signal monitoring method based on the AutoEncoder model, the GRU-AE model has better performance. The research has an important role in promoting the development of biomedical signal monitoring techniques, which can be effectively applied to some kinds of remote health monitoring scenario.

Highlights

Introduction published maps and institutional affilTelemedicine is the use of electronic information and telecommunication technology to provide the health care that people need while practicing social distancing [1]
In order to solve the problems of existing remote biomedical signal monitoring methods, this paper introduces the Internet of Things (IoT), 5G communication, artificial intelligence and other technologies and constructs a biomedical signal monitoring framework based on GRU-AE
This paper proposes a biomedical signal monitoring framework based on deep learning in combination with the IoT, 5G communication, and artificial intelligence technique

Summary

Introduction

Introduction published maps and institutional affilTelemedicine is the use of electronic information and telecommunication technology to provide the health care that people need while practicing social distancing [1]. The remote health monitoring services mainly diagnose and evaluate health issues by monitoring various biomedical signals. AutoEncoderis an unsupervised artificial neural network that is used to efficiently compress and encode data to achieve the goal of neural dimensionality The. AutoEncoder is an unsupervised artificial network reduction. That is used to schematic efficiently diagram of AutoEncoder model is shown in compress and encode data to achieve the goal of dimensionality reduction. Input and output layers have an equal number of nodes in AutoEncoder. This is because the purpose of the AutoEncoder is to initialize the hidden layer parameters that will reconstruct the multidimensional input data. Encoding is the process between the input layer and the hidden layer. The decoding constructs the process of the output using hidden layer output

Results

Discussion

Conclusion