iDispenser-big data enabled intelligent dispenser

Publication Date

6-8-2017

Document Type

Conference Proceeding

Publication Title

Proceedings - 3rd IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2017

DOI

10.1109/BigDataService.2017.53

First Page

124

Last Page

130

Abstract

With healthcare-associated infections (HAIs) in the U.S. accounting for an estimated 1.7 million infections and 99,000 deaths annually, reducing and preventing these infections is a top goal for healthcare facilities throughout the country [1]. Not only healthcare facilities, in other captive environments, for instance, ships and cruises, provide environment that may increase risk of infection. According to Minooee and Rickman [2], 'Ships provide an isolated environment that may increase the passenger's risk of infection if exposed to respiratory viruses. High attack rates of influenza, for example, are typically seen in closed settings such as cruises, military vessels, aircraft, and institutions.'The high rates of infection in captive areas are influenced by number of people entering and exiting the place. As per the research summarized by 'Hospital infection control: reducing airborne pathogens'[3], the number of people entering and exiting provides a contaminant source. It's known that the concentration of airborne bacteria is proportional to the number of personnel in the room. The amount of surface contamination is also related to airborne contamination from occupation and activity since these microbes settle continuously.' Fencl [3] suggests that the use of disinfection, to control infectious agents in healthcare settings, is one of its oldest and most cost effective ways to control airborne infection. Nichols [1], importantly, suggest that touchless dispensing solutions as an effective way to help reduce the spread of germs. In our view, with advent of machine learning and Internet of Things, combining automated & intelligent dispensing with touchless systems provides more holistic approach to control infection in healthcare and, more importantly, captive places such as hospitals, cruises, casinos, airports and other places.In this research paper, we propose an innovative approach to prevent spread of airborne diseases through the application of Big Data Technologies and IoT Sensing. Our goal is to cutting down the millions of dollars spent on infectious diseases, intelligent dispenser promises to keep hospitals smelling fresh soothing and disinfecting. The paper presents prototyping solution design as well as its application and certain experimental results.

Keywords

Airborne diseases, Android, CEP, Complex Event Processing, Decision Tree, Healthcare-associated infections (HAIs), Internet Of Things, iOS, IoT, IoT reference architecture, Machine Learning, Regression Analysis, Term Frequency and Inverse, Venue Analytics

Department

Computer Engineering

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