Design of Human-computer Interaction System Using Gesture Recognition Algorithm from the Perspective of Machine Learning

Abstract

The human-computer interaction (HCI) system is the hub of conversation between man and machine. The previous HCI system is challenging to meet the needs of modern society with the continuous progress of computer technology. A new HCI technology different from the past needs to be put forward to solve the problem that the development of computer technology and outdated HCI technology lag behind the times. Machine learning aims to build a behavior method that can obtain information from data and predict the data. Machine learning features are used and integrated into gesture algorithms through the basic principle of a finger-guessing game. Gesture estimation is adopted to detect joint gesture features efficiently, and a convolutional neural network is employed to plan and process the joint features to solve the bottleneck of poor segmentation of gesture images in different environments. HCI system based on gesture recognition algorithm is designed from the perspective of machine learning. The final experimental results show that this method has good recognition accuracy for the performance of different scales of various gestures, and the accuracy of recognition results can reach 70%. It suggests that the system is theoretically conditional on making the recognition result reach the most authentic level. It is demonstrated that gesture recognition is of excellent use value in this HCI system, and the HCI system established here also has an efficient reference value.