An IoT Integrated Sensor Technologies for the Enhancement of Hospital Waste Segregation and Management

Hospitals create an enormous volume of potentially hazardous trash. Currently, bandages, plastic papers, wasted pus, syringes, glucose drip bottles, other dangerous medical wastes, and containers are separated by hand, resulting in long-term health consequences such as tuberculosis, cancer, and infectious diseases. Rag pickers are currently responsible for the majority of waste separation. In this proposed system, waste will be detected using the Deep Learning technique; a model will be trained to distinguish various forms of medical waste. With the use of a flip attachment to a motor, waste will be easily segregated to the waste bin. There are two flips that open depending on the sort of trash detected, and the garbage is divided into compartments having four specific chambers. IoT-based web applications can be used to update the waste level, with respective times and dates. As a result, the overflowing of waste in the bin can be avoided. Here, medical waste is quickly and properly segregated to reduce manual labor and stop diseases from spreading throughout hospitals