Prediction and Monitoring of Medical Storage System using Machine Learning and IoT

Abstract

This paper describes to design an IOT device which measures continuously air quality, temperature in medical storage and record the data of it by IOT server using wireless sensor network and stores the previous data in database and live data can be monitor through the Google firebase cloud. Poor air quality, change in temperature is extremely difficult to maintain the medicines in hospitals. When the data of these sensors were above or below the threshold limit the monitoring system automatically transmits the info to the medical storage maintainers hand phone on the mobile network as SMS via IOT. Thus it alerts to manage the toxic gas levels and temperature levels quickly. Along with this system measuring of Oxygen cylinders were also attached as a additional feature. Using load cell sensor the amount of oxygen(medical gases) usage for an operation done to a patient can be monitor and these cylinders are continuously monitor if the oxygen of the working cylinder is going to complete then alert messages automatically transmits the info to the oxygen cylinder maintainers handphone as sms via IOT. Using Artificial neural network algorithm the amount of oxygen usage for the operations in hospitals were collected and predicted the usage of oxygen(medical gases). so that it is easy to maintain the amount of oxygen cylinders required for the further registered operations. Currently the system used to monitor only temperature in the Medical storage .In the proposed system it monitor temperature levels , toxic gases,oxygen cylinder usage and prediction the oxygen cylinders.