HRI (Human-Robot Interaction) Using Machine Learning based Automatic Gesture Recognition with KLT tracker

The Human-Robot Interaction abbreviated as HRI is about the study of efficient and effective interaction or communication between humans and robots. HRI is the multidisciplinary concept where natural language processing, computer vision, robotics, artificial intelligence, and psychology are commonly used. In this world that is evolving around robots, there is a necessity to develop gesture-controlled robots that are immensely used for different purposes. Right from simplifying the life of disabled people, giving an advanced application at industrial heights, giving a higher experience for gamers, there is a need to monitor and control the robots while being able to get our works done at a smother phase. In spite of having the ability to detect the gestures through computer vision, there has always been a challenge to coordinate the robots with these gestures. So, in this paper we have come up with a theory that can be used to control the robots with the help of pointed gestures [1]. In this, we studied the gesture identification and as per gesture instruction is provided to human as well as action is taken by the robot. Here for segmentation hand colour pixels are used and segmentation is applied to input pre-processed data to get only the hand region [1].The proposed theory works on the basis of the KLT tracker where we first feed the data to the camera of the robot. This is physically performed by the user. The input that is fed to the robot will be pre-processed and segmentation of the user's hand is performed [2-4]. Once we perform segmentation, tracking of our hand will be done using the KLT tracker while being able to extract the features of the detected user's hand. This tracker will be efficient enough to capture the human gestures that are given as commands. Classification analysis is performed and instruction fed is notified to the user. The robot takes in these commands and performs the required action. A detailed analysis to give more insights on the theory and algorithm is proposed in this paper. Statistical and geometric features are extracted which helps in classification using a machine learning algorithm. SVM (Support Vector Machine) classifier is used for the classification of detected hand gestures [1]. The integration with the system that is developed is performed with the help of few parameters that will make the interaction smooth. The robots are controlled with the help of pre-defined actions through detected gestures. Performance analysis of the proposed model shows the superior performance of proposed work over state of art techniques.We aim to fully automate the robot with the help of gestures and perform more complex tasks like industrial applications, automobiles, and advanced automated systems [1]. The proposed system can be used in different applications such as home automation and recent technologies can be integrated for better performance easy use by users such as Internet of Things (IOT) and Android app design [2-4].Finally, we conclude by stating that the developed gesture-controlled system proposes the model along with the algorithm to advance the aspects of developing robots that are not only limited to gesture controls, but also to the speech and mechanical input devices.