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IoT in Radiology: Using Raspberry Pi to Automatically Log Telephone Calls in the Reading Room

The work environment for medical imaging such as distractions, ergonomics, distance, temperature, humidity, and lighting conditions generates a paucity of data and is difficult to analyze. The emergence of Internet of Things (IoT) with decreasing cost of single-board computers like Raspberry Pi make...

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Detalles Bibliográficos
Autores principales: Chen, Po-Hao, Cross, Nathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5959834/
https://www.ncbi.nlm.nih.gov/pubmed/29725966
http://dx.doi.org/10.1007/s10278-018-0081-z
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author Chen, Po-Hao
Cross, Nathan
author_facet Chen, Po-Hao
Cross, Nathan
author_sort Chen, Po-Hao
collection PubMed
description The work environment for medical imaging such as distractions, ergonomics, distance, temperature, humidity, and lighting conditions generates a paucity of data and is difficult to analyze. The emergence of Internet of Things (IoT) with decreasing cost of single-board computers like Raspberry Pi makes creating customized hardware to collect data from the clinical environment within the reach of a clinical imaging informaticist. This article will walk the reader through a series of basic project using a variety sensors and devices in conjunction with a Pi to gather data, culminating in a complex example designed to automatically detect and log telephone calls. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10278-018-0081-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-59598342018-05-25 IoT in Radiology: Using Raspberry Pi to Automatically Log Telephone Calls in the Reading Room Chen, Po-Hao Cross, Nathan J Digit Imaging Article The work environment for medical imaging such as distractions, ergonomics, distance, temperature, humidity, and lighting conditions generates a paucity of data and is difficult to analyze. The emergence of Internet of Things (IoT) with decreasing cost of single-board computers like Raspberry Pi makes creating customized hardware to collect data from the clinical environment within the reach of a clinical imaging informaticist. This article will walk the reader through a series of basic project using a variety sensors and devices in conjunction with a Pi to gather data, culminating in a complex example designed to automatically detect and log telephone calls. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10278-018-0081-z) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-05-03 2018-06 /pmc/articles/PMC5959834/ /pubmed/29725966 http://dx.doi.org/10.1007/s10278-018-0081-z Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Chen, Po-Hao
Cross, Nathan
IoT in Radiology: Using Raspberry Pi to Automatically Log Telephone Calls in the Reading Room
title IoT in Radiology: Using Raspberry Pi to Automatically Log Telephone Calls in the Reading Room
title_full IoT in Radiology: Using Raspberry Pi to Automatically Log Telephone Calls in the Reading Room
title_fullStr IoT in Radiology: Using Raspberry Pi to Automatically Log Telephone Calls in the Reading Room
title_full_unstemmed IoT in Radiology: Using Raspberry Pi to Automatically Log Telephone Calls in the Reading Room
title_short IoT in Radiology: Using Raspberry Pi to Automatically Log Telephone Calls in the Reading Room
title_sort iot in radiology: using raspberry pi to automatically log telephone calls in the reading room
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5959834/
https://www.ncbi.nlm.nih.gov/pubmed/29725966
http://dx.doi.org/10.1007/s10278-018-0081-z
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