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SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems
In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduct...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891700/ https://www.ncbi.nlm.nih.gov/pubmed/31766116 http://dx.doi.org/10.3390/s19224935 |
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author | McLamore, Eric S. Palit Austin Datta, Shoumen Morgan, Victoria Cavallaro, Nicholas Kiker, Greg Jenkins, Daniel M. Rong, Yue Gomes, Carmen Claussen, Jonathan Vanegas, Diana Alocilja, Evangelyn C. |
author_facet | McLamore, Eric S. Palit Austin Datta, Shoumen Morgan, Victoria Cavallaro, Nicholas Kiker, Greg Jenkins, Daniel M. Rong, Yue Gomes, Carmen Claussen, Jonathan Vanegas, Diana Alocilja, Evangelyn C. |
author_sort | McLamore, Eric S. |
collection | PubMed |
description | In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools. |
format | Online Article Text |
id | pubmed-6891700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68917002019-12-12 SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems McLamore, Eric S. Palit Austin Datta, Shoumen Morgan, Victoria Cavallaro, Nicholas Kiker, Greg Jenkins, Daniel M. Rong, Yue Gomes, Carmen Claussen, Jonathan Vanegas, Diana Alocilja, Evangelyn C. Sensors (Basel) Review In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools. MDPI 2019-11-13 /pmc/articles/PMC6891700/ /pubmed/31766116 http://dx.doi.org/10.3390/s19224935 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review McLamore, Eric S. Palit Austin Datta, Shoumen Morgan, Victoria Cavallaro, Nicholas Kiker, Greg Jenkins, Daniel M. Rong, Yue Gomes, Carmen Claussen, Jonathan Vanegas, Diana Alocilja, Evangelyn C. SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems |
title | SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems |
title_full | SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems |
title_fullStr | SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems |
title_full_unstemmed | SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems |
title_short | SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems |
title_sort | snaps: sensor analytics point solutions for detection and decision support systems |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891700/ https://www.ncbi.nlm.nih.gov/pubmed/31766116 http://dx.doi.org/10.3390/s19224935 |
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