AI-Based Hand Gesture Recognition Through Camera on Robot

This paper presents an innovative approach to real-time hand gesture recognition for robot control using Artificial Intelligence (AI). The core of this project is a machine learning model trained on a custom data set of hand gestures, which was meticulously hand-annotated to ensure accuracy. To enhance the model’s performance and generalization, data augmentation techniques were employed. Furthermore, the model leverages the power of transfer learning, with a ResNet backbone serving as the foundation, to efficiently learn from the data set. In addition to the development of the AI model, a custom robot was designed and built using Arduino and Raspberry Pi. This robot is equipped with a camera to capture images of hand gestures, which are then transmitted to the machine learning model for real-time analysis. The hardware of the robot was meticulously optimized to ensure smooth operation and accurate data capture. The resulting system enables real-time hand gesture recognition on the robot, opening up a plethora of applications, from industrial automation to smart home technology. By synergistically combining AI, computer vision, and robotics, this project not only demonstrates the potential for innovative solutions to real-world problems but also significantly enhances the functionality and usability of robots. It paves the way for improved human-computer interaction through the practical implementation of advanced AI and computer vision techniques.