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Title: An IoT-based framework of Webvr visualization for medical big data in connected health
Authors: Xu, Gaowei
Lan, Yisha
Zhou, Wen
Huang, Chenxi
Li, Weibin
Zhang, Wei
Zhang, Guokai
Ng, Eddie Yin Kwee
Cheng, Yongqiang
Peng, Yonghong
Che, Wenliang
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Xu, G., Lan, Y., Zhou, W., Huang, C., Li, W., Zhang, W., . . . Che, W. (2019). An IoT-based framework of Webvr visualization for medical big data in connected health. IEEE Access, 7, 173866-173874. doi:10.1109/ACCESS.2019.2957149
Journal: IEEE Access
Abstract: Recently, telemedicine has been widely applied in remote diagnosis, treatment and counseling, where the Internet of Things (IoT) technology plays an important role. In the process of telemedicine, data are collected from remote medical equipment, such as CT machine and MRI machine, and then transmitted and reconstructed locally in three-dimensions. Due to the large amount of data to be transmitted in the reconstructed model and the small storage capacity, data need to be compressed progressively before transmission. On this basis, we proposed a lightweight progressive transmission algorithm based on large data visualization in telemedicine to improve transmission efficiency and achieve lossless transmission of original data. Moreover, a novel four-layer system architecture based on IoT has been introduced, including the sensing layer, analysis layer, network layer and application layer. In this way, the three-dimensional reconstructed data at the local end is compressed and transmitted to the remote end, and then visualized at the remote end to show reconstructed 3D models. Thus, it is conducive to doctors in remote real-time diagnosis and treatment, and then realize the data processing and transmission between doctors, patients and medical equipment.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2957149
Rights: © 2019 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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