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DALIAN UNIVERSITY OF TECHNOLOGY Login 中文
Wang Zhelong

Professor
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates


Main positions:Professor, Head of Lab of Intelligent System
Other Post:自动化技术研究所所长
Gender:Male
Alma Mater:University of Durham
Degree:Doctoral Degree
School/Department:School of Control Science and Engineering
Discipline:Control Theory and Control Engineering. Pattern Recognition and Intelligence System. Detection Technology and Automation Device
Business Address:Lab of Intelligent System
http://lis.dlut.edu.cn/

Contact Information:0411-84709010 wangzl@dlut.edu.cn
E-Mail:wangzl@dlut.edu.cn
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Networked Gesture Tracking System Based on Immersive Real-Time Interaction

Hits : Praise

Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:SCIE、EI、CPCI-S

Page Number:139-144

Key Words:gesture interaction; inertial navigation; body sensor network; data fusion; key frame

Abstract:Gesture as a natural and efficient interactive mode, which has been widely used in the field of human-computer collaboration, as the present existing gesture acquisition method is difficult to meet the users' immersion experience and ensure the real-time requirements, in this paper, we design a wearable interactive system which can meet the need of real-time hand gesture acquisition and 3D display. From the perspective of human ergonomics, we analysis the relationship between the movements of bones and joints during hand movement and establish a dynamic model about the skeletal structure of hand. On the basis of this theory, combining with the spatial navigation theory and data fusion method of heterogeneous sensors, a hand tree sensor network based on MEMS inertial sensor is established to realize the real-time tracking of gesture. At the same time, we make a comparison and verification of the gesture data by combining with the image processing method through extracting the key frame information in the gesture video Finally, we can find the system established in this paper can realize the real-time tracking of gestures through analysis and comparison of real gesture, which provides certain reference value.