Sewage Environment and Workers Health Monitoring System Using IOT and ML

Abstract

Abstract: This project aims to provide intelligent solutions for sewage environment monitoring and is working on a live sewage level detection and monitoring system. Many sanitation workers suffered from serious health problems at a young age and died from constant exposure to toxic sewage. Various types of work have been undertaken to identify, maintain and manage the drainage system, but very little has been done. to protect the lives of the people who do it. The goal of this monitoring system is to have an effective, flexible and cost-effective solution to verify and maintain an update through sensors and to collect and analyze data using the Internet of Things and machine learning. The device monitors the person's pulse rate via heartbeat sensor, sewer temperature, water level and hazardous gases such as CH4, CO to warn the worker and the outdoor unit when the parameters deviate from the safe range. The information is sent along with various values indicating whether it is safe for the worker to clean or work in that environment. If the values exceed the threshold, it sends an alert to the connected mobile devices of authorized people remotely at work. This result will quickly alert the worker to stay safe and detect toxic gases before damage occurs. The machine learning model then uses the data obtained from the cloud to perform further systematic analysis and intelligently leverages this vast amount of data to predict worker health with considerable reliability and accuracy.