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MEDICAL WASTE DETECTION AND SEGREGATION USING ROBOTIC ARM

Aarsha S S, Akshay L S, Amrutha S S,Athira Raj R S
Electronics and Communication Engineering
(Students)
St.Thomas Institute for Science and Technology

TVM

Dr.Anju S
Electronics and Communication Engineering
(Asst.Professor)
St.Thomas Institute for Science and Technology

TVM
anju.ec@stisttvm.edu.in

Abstract— In the present world, the amount of waste material in hospitals and other commercial places is increasing. A large number of laborer’s should be allotted for waste material segregation and collection. A huge amount of money needs to be spent on the equipment and salaries of the workers. The proper management of medical waste materials is critical for the sustainability of any modern city. Medical waste is very dangerous as it can give birth to serious diseases; therefore, It is a problem of global nature. Biomedical waste consists of human tissues, medicine, needles, masks, scraps, etc. In this project, we are proposing a robotic arm to segregate medical waste. The robotic arm identifies three objects (a medicine bottle, scissors, and knife). The segregation of medical waste materials is done by using the concepts of machine learning, image processing, and deep learning. The sorting and segregation of medical waste can be done using this arm, so physical contact with the waste materials can be avoided. And the number of workers in the hospital could be reduced. The waste materials that need to be segregated should be placed in a container at the arm’s base. A camera will be placed in front of the container. The camera will identify the components, and the information will be passed to the system. Here, we use a laptop as the brain of the robotic arm. Then the robotic arm will take the waste material which will be segregated into the respective containers. The arm will be trained in such a way that the scissors will be segregated at an angle of 30 degree right, the samples will be placed 90 degrees and the medicine bottle will be segregated at an angle of 120 degree left and knife at 180 degrees left. The position of the segregated baskets will be identified by the robotic arm by the angle measurements. The robotic arm is a hardware and software system based on image processing using machine learning and deep learning for the execution of the model we use.

Keywords— Machine learning and deep learning