A Novel Approach Machine Learning and Gesture, Pose Estimation Based Industrial Automation

Abstract

In recent years, there has been a growing interest in leveraging machine learning techniques and gesture/pose estimation algorithms for industrial automation. This novel approach holds the potential to revolutionize the way industrial processes are performed by enabling intuitive human-machine interactions and improving overall system efficiency. This research paper presents a comprehensive study on the integration of machine learning and gesture/pose estimation techniques for industrial automation applications. The objective is to develop a robust and reliable system that can accurately interpret human gestures and poses, allowing operators to control machines and perform complex tasks in a more intuitive and efficient manner. The proposed approach utilizes to recognizing and interpreting human gestures and poses. These algorithms analyze input data from cameras or depth sensors to accurately estimate the position and orientation of human body parts or the overall body pose. The estimated gestures and poses are then mapped to specific commands or actions within the industrial automation system. To evaluate the effectiveness of the proposed approach, several experiments are conducted using a simulated industrial environment. The experiments involve different tasks, such as machine control, object manipulation, and assembly operations, which are performed by operators using gesture-based interaction. The performance of the system is measured in terms of accuracy, response time, and overall system efficiency. The results of the experiments demonstrate the feasibility and potential benefits of the novel approach. The integrated system effectively recognizes a wide range of gestures and poses, enabling operators to control machines and perform tasks with high precision and efficiency. The real-time gesture and pose estimation algorithms exhibit robustness and accuracy, even in dynamic industrial environments with varying lighting conditions and occlusions. In this paper, we show motor speed control using hand gestures for industrial use, same as this method can also use for switching or other automation task. Overall, this research contributes to the advancement of industrial automation by proposing a novel approach that combines machine learning and gesture/pose estimation techniques. The developed system has the potential to enhance human-machine interactions, improve productivity, and reduce the learning curve associated with operating complex industrial systems.