Gesture Recognition Based on Time-of-Flight Sensor and Residual Neural Network  

作　　者：Yuqian Ma Zitong Fang Wen Jiang Chang Su Yuankun Zhang Junyu Wu Zhengjie Wang Yuqian Ma;Zitong Fang;Wen Jiang;Chang Su;Yuankun Zhang;Junyu Wu;Zhengjie Wang(College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China)

机构地区：[1]College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China

出　　处：《Journal of Computer and Communications》2024年第6期103-114,共12页电脑和通信（英文）

摘　　要：With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.

关 键 词：Hand Posture Recognition Human-Computer Interaction Deep Learning Gesture Datasets Real-Time Processing 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]