WITHDRAWN: Internet of Things (IoT) based predictive analytics for Medical services and telemedicine applications

Sravanthi, Sumukham; ganesan, G. Vithya

doi:10.1016/j.matpr.2021.01.738

Cited by 1 publication

(2 citation statements)

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“…Compared to existing studies, such as the studies of [12,19,22], reference [12] used an IoT-based health monitoring platform to track heart rate and body temperature, and sent email/SMS warnings when these readings were higher than the main value, and no biomedical fusion analysis was implemented. In reference [19], they extracted the time series data of various biological signals separately, and then used CNN to perform abnormality detections separately.…”

Section: Discussionmentioning

confidence: 99%

“…And The server was used as an online IoT platform, using applications to perform data calculations and displayed stress reports. Sravanthi and Ganesan [12] proposed an IoT-based health monitoring device that tracked the heart rate and body heat and also sent an email/SMS warning when these readings went above the main values. Thingspeak and Google sheets logged heart rate and body temperature measurements so that patient wellbeing could be tracked on the internet everywhere in the world.…”

Section: Related Workmentioning

confidence: 99%

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

Tang

et al. 2021

IJERPH

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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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