EnViBo: Embedded network for vital sign and Biomedical Signal Monitoring

2014 ◽  
Author(s):  
Gustavo Meneses Benavides ◽  
Juan Diego Lemos Duque
Keyword(s):  
Vital Sign ◽  
Biomedical Signal ◽  
Embedded Network ◽  
Signal Monitoring

LOW-POWER BIOMEDICAL SIGNAL MONITORING SYSTEM FOR IMPLANTABLE SENSOR APPLICATIONS

2013 ◽  
Author(s):  
MOHAMMAD RAFIQUL HAIDER ◽  
JEREMY HOLLEMAN ◽  
SALWA MOSTAFA ◽  
SYED KAMRUL ISLAM
Keyword(s):  
Low Power ◽  
Monitoring System ◽  
Biomedical Signal ◽  
Sensor Applications ◽  
Signal Monitoring ◽  
Implantable Sensor

Low-power wireless sensor nodes for ubiquitous long-term biomedical signal monitoring

2012 ◽  
Vol 50 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Christian Bachmann ◽  
Maryam Ashouei ◽  
Valer Pop ◽  
Maja Vidojkovic ◽  
Harmke Groot ◽  
...  
Keyword(s):  
Low Power ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Biomedical Signal ◽  
Signal Monitoring

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

2021 ◽  
Vol 18 (17) ◽  
pp. 9037
Author(s):  
Zhongyun Tang ◽  
Haiyang Hu ◽  
Chonghuan Xu ◽  
Kaidi Zhao
Keyword(s):  
Time Series ◽  
Health Monitoring ◽  
Series Data ◽  
Remote Health Monitoring ◽  
Potential Health ◽  
Biomedical Signal ◽  
Biomedical Signals ◽  
Monitoring Framework ◽  
Signal Monitoring ◽  
Remote Health

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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