A Comparative Analysis of Real-Time Sign Language Recognition Methods for Training Surgical Robots

This project proposes a real-time robot that can interact with humans based on the gestures fed to it as input. The proposed proposal aims to develop a constructive design of a robot that has computer vision and is trained to read human gestures. There is a need for intelligent robots in the healthcare industry. The impact of this project will be on sophisticated healthcare systems, especially the surgical system. The implementation is achieved by training the robot using deep CNN and making the robot perform certain functions like moving the arm upwards and downwards as well as opening and closing the robot grippers. It is also important to mention that a comparative analysis has been made with the existing system and advanced technology called MediaPipe framework for the acquisition of input signals. The comparative analysis will give us a clear picture of the usage of different types of classifiers for training robotic models. With the impact of this project, it would be easy for the physicians to pick and place the medical equipment in a correct manner and provide assistance to the surgeon during surgery. This device can also be very useful in robot-assisted surgeries as it can be further developed to perform actions like drilling and making incisions.