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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.
Afiliação
  • McLamore ES; Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
  • Palit Austin Datta S; Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
  • Morgan V; MIT Auto-ID Labs, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Cavallaro N; MDPnP Labs, Biomedical Engineering Program, Department of Anesthesiology, Massachusetts General Hospital, Harvard Medical School, 65 Landsdowne Street, Cambridge, MA 02139, USA.
  • Kiker G; Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
  • Jenkins DM; Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
  • Rong Y; Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
  • Gomes C; Molecular Biosciences and Bioengineering, University of Hawaii Manoa, Honolulu, HI 96822, USA.
  • Claussen J; Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
  • Vanegas D; Mechanical Engineering, Iowa State University, Ames, IA 50011, USA.
  • Alocilja EC; Mechanical Engineering Department, Iowa State University, Ames, IA 50011, USA.
Sensors (Basel) ; 19(22)2019 Nov 13.
Article em En | MEDLINE | ID: mdl-31766116
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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Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Qualitative_research Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Qualitative_research Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos