Design of human-computer interaction control system based on hand-gesture recognition | |
Wang ZH(王志恒); Cao, Jiangtao; Liu JG(刘金国)![]() | |
作者部门 | 空间自动化技术研究室 |
会议名称 | 32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017 |
会议日期 | May 19-21, 2017 |
会议地点 | Hefei, China |
会议录名称 | Proceedings - 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017 |
出版者 | IEEE |
出版地 | New York |
2017 | |
页码 | 143-147 |
收录类别 | EI ; CPCI(ISTP) |
EI收录号 | 20173204024209 |
WOS记录号 | WOS:000425862800027 |
产权排序 | 2 |
ISBN号 | 9781538629017 |
关键词 | Human-computer Interaction Control System Sample Robot Gesture Recognition Improved Pso-svm Algorithm |
摘要 | A gesture recognition based Human-Computer Interaction control system is developed via LabVIEW in this paper. Furthermore, to solve the existing problems of lower precision and poor real-time ability in gesture recognition algorithm, an improved PSO-SVM classification algorithm of hand-gesture recognition is proposed. Firstly, the gesture sample data is collected by using five bending sensor of data glove, then, in order to improve the recognized precision, the data collected is preprocessed and the optimize the SVM kernel parameter value is found by using the improved PSO algorithm. Finally, the recognized hand-gesture is divided into numbered 1-11 control status values, and sent to the sample robot controller by wireless transmission module and achieving the realization of sampling robot motion control. The simulation results illustrate the effectiveness of designed method on. |
语种 | 英语 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.sia.cn/handle/173321/20823 |
专题 | 空间自动化技术研究室 |
通讯作者 | Wang ZH(王志恒) |
作者单位 | 1.School of Information and Control Engineering, Liaoning Shihua University, Liaoning FuShun, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Science, Liaoning Shenyang, China |
推荐引用方式 GB/T 7714 | Wang ZH,Cao, Jiangtao,Liu JG,et al. Design of human-computer interaction control system based on hand-gesture recognition[C]. New York:IEEE,2017:143-147. |
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