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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
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.
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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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