Design of Bionic Robotic Hand Gesture Recognition System Based on Machine Vision

Abstract

Gesture recognition technology is a natural human-computer interaction method with a wide range of applications, among which using bionic robotic hands to achieve gesture recognition is more meaningful and valuable for research. To improve the accuracy and the speed of gesture recognition, and also to facilitate the deployment of gesture recognition algorithm on edge device, a bionic robotic hand gesture recognition system is designed based on machine vision. Firstly, a bionic robot with six degrees of freedom based on Raspberry Pi 3B+ is chosen as the development platform of the system, including five finger degrees of freedom and one wrist degree of freedom; Secondly, a gesture recognition method is proposed based on a combination of feature extraction model, Inception v3 network, and migration learning, and it is trained and tested on a self-built gesture dataset. The results show that the method can accurately recognize gesture types with an average accuracy of 0.92; Finally, the trained model is deployed on a Raspberry Pi 3B+ bionic manipulator and tested on a physical system, and the average recognition speed is 0.17s after the system is stabilized, which shows that the system can effectively recognize and imitate hand gesture.