基于SVM-LSTM模型的三维动态手势交互设计

IF 0.2 Q4 COMPUTER SCIENCE, CYBERNETICS International Journal of Mobile Human Computer Interaction Pub Date : 2018-07-01 DOI:10.4018/IJMHCI.2018070104
Tao Wang, Xiaolong Cai, Liping Wang, Haoye Tian
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引用次数: 2

摘要

视觉手势交互是人机交互的主要方式之一,为用户提供了更大的交互自由度和更逼真的交互体验。提出了一种基于SVM-LSTM的混合模型,设计了一个三维动态手势交互系统。系统利用Leap Motion捕捉手势信息,结合SVM强大的静态手势分类能力和LSTM强大的变长时间序列手势处理能力,实现对用户手势的实时识别。该手势交互方法可以自动定义手势的开始和结束,识别准确率达到96.4%,大大降低了学习成本。实验表明,本文提出的手势交互方法是有效的。在模拟的移动环境中,手势预测平均只需要0.15秒,普通用户可以快速掌握该方法。
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Interactive Design of 3D Dynamic Gesture Based on SVM-LSTM Model
Visual hand gesture interaction is one of the main ways of human-computer interaction, and provides users more interactive degrees of freedom and more realistic interactive experience. Authors present a hybrid model based on SVM-LSTM, and design a three-dimensional dynamic gesture interaction system. The system uses Leap Motion to capture gesture information, combined with SVM powerful static gesture classification ability and LSTM powerful variable-length time series gesture processing ability, enabling real-time recognition of user gestures. The gesture interaction method can automatically define the start and end of gestures, recognition accuracy reached 96.4%, greatly reducing the cost of learning. Experiments have shown that the gesture interaction method proposed by authors is effective. In the simulated mobile environment, the average gesture prediction only takes 0.15 seconds, and ordinary users can quickly grasp this method.
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CiteScore
4.70
自引率
0.00%
发文量
5
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