Evidence map›Paper›PMID 31766116›Full record

ReviewSensors (Basel, Switzerland)2019

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

Eric S McLamore, Shoumen Palit Austin Datta, Victoria Morgan, Nicholas Cavallaro, Greg Kiker, Daniel M Jenkins, Yue Rong, Carmen Gomes, Jonathan Claussen, Diana Vanegas and 1 more

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Prevalence ofFrontiers in microbiology · 2021
    Article
  8. Article
  9. Article
  10. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Eric S McLamoreAgricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.ORCID 0000-0002-1662-7372
Shoumen Palit Austin DattaAgricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
Victoria MorganAgricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
Nicholas CavallaroAgricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
Greg KikerAgricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
Daniel M JenkinsMolecular Biosciences and Bioengineering, University of Hawaii Manoa, Honolulu, HI 96822, USA.ORCID 0000-0003-1616-0845
Yue RongAgricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.ORCID 0000-0003-2456-8584
Carmen GomesMechanical Engineering, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0003-0095-6478
Jonathan ClaussenMechanical Engineering Department, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0001-7065-1077
Diana VanegasEnvironmental Engineering and Earth Sciences, Clemson University, Clemson, SC 29634, USA.
Evangelyn C AlociljaGlobal Alliance for Rapid Diagnostics, Michigan State University, East Lansing, MI 48824, USA.

Funding

National Institute of Food and Agriculture 2018-67016-27578National Science Foundation 1511953National Science Foundation 1805512
6 · The paper itself

Abstract

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.

Indexed as

ARTartificial reasoning toolscyber-physical systemsDADAdata analyticsdrag and drop analyticsknowledge graphsmachine-assisted platformmachine-assisted toolsMAPMATSARAsense-analyze-respond-actuatesensorsensor-analytics point solutionssmart systemsSNAPStrans-disciplinary convergence

Identifiers

PMID31766116
PMCPMC6891700

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.