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Open AccessReview

SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems

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Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA
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MIT Auto-ID Labs, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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MDPnP Labs, Biomedical Engineering Program, Department of Anesthesiology, Massachusetts General Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA 02139, USA
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Molecular Biosciences and Bioengineering, University of Hawaii Manoa, Honolulu, HI 96822, USA
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Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
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Mechanical Engineering Department, Iowa State University, Ames, IA 50011, USA
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Ames Laboratory, Ames, IA 50011, USA
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Environmental Engineering and Earth Sciences, Clemson University, Clemson, SC 29634, USA
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Global Alliance for Rapid Diagnostics, Michigan State University, East Lansing, MI 48824, USA
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Nano-Biosensors Lab, Michigan State University, East Lansing, MI 48824, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(22), 4935; https://doi.org/10.3390/s19224935
Received: 3 September 2019 / Revised: 23 October 2019 / Accepted: 28 October 2019 / Published: 13 November 2019
(This article belongs to the Special Issue New Trends in Electrochemical Sensors for Biomedical Applications)
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. View Full-Text
Keywords: sensor; smart systems; data analytics; cyber-physical systems; artificial reasoning tools; ART; drag and drop analytics; DADA; sensor-analytics point solutions; SNAPS; sense-analyze-respond-actuate; SARA; machine-assisted tools; MAT; machine-assisted platform; MAP; knowledge graphs; trans-disciplinary convergence sensor; smart systems; data analytics; cyber-physical systems; artificial reasoning tools; ART; drag and drop analytics; DADA; sensor-analytics point solutions; SNAPS; sense-analyze-respond-actuate; SARA; machine-assisted tools; MAT; machine-assisted platform; MAP; knowledge graphs; trans-disciplinary convergence
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MDPI and ACS Style

McLamore, E.S.; Palit Austin Datta, S.; Morgan, V.; Cavallaro, N.; Kiker, G.; Jenkins, D.M.; Rong, Y.; Gomes, C.; Claussen, J.; Vanegas, D.; Alocilja, E.C. SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems. Sensors 2019, 19, 4935.

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