Journal
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
Volume 18, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/ijerph18179037
Keywords
telemedicine; biomedical signal monitoring framework; GRU-AE; COVID-19 pandemic; healthy monitoring
Funding
- Project of China (Hangzhou) Cross-border E-commerce College [2021KXYJ06]
- Philosophy and Social Science Foundation of Zhejiang Province [21NDJC083YB]
- National Natural Science Foundation of China [61802095, 71702164]
- Natural Science Foundation of Zhejiang Province [LY20G010001]
- Scientific Research Projects of Zhejiang Education Department [Y201737967]
- Contemporary Business and Trade Research Center of Zhejiang Gongshang University of China [2021SMYJ05LL]
- Center for Collaborative Innovation Studies of Modern Business of Zhejiang Gongshang University of China [2021SMYJ05LL]
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This paper proposes a remote biomedical signal monitoring framework combining IoT, 5G communication, and artificial intelligence techniques, which can effectively analyze multi-dimensional biomedical signals in time series, playing an important role in promoting the development of biomedical signal monitoring techniques.
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.
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