首页> 外文期刊>Expert Systems with Application >Expert system for gesture recognition in terminal's user interface
【24h】

Expert system for gesture recognition in terminal's user interface

机译:终端用户界面中手势识别的专家系统

获取原文
获取原文并翻译 | 示例
       

摘要

This paper presents and describes a soft computing based expert system for gesture recognition procedure, as a part of intelligent user interface of a mobile terminal. In the presented solution, a terminal includes three acceleration sensors positioned like xyz co-ordinate system in order to get three-dimensional (3D) acceleration vector, xyz. The 3D acceleration vector is, after Doppler spectrum definition, used as an input vector to a fuzzy reasoning unit of embedded expert system, which classifies gestures (time series of acceleration vectors). In the reasoning unit fuzzy rule aided method is used to classification. The method is compared to the fuzzy c-means classification with feature extraction, to the hidden Markov model (HMM) classification and SOM classification. Fuzzy methods classified successfully the test sets. The advantages of the fuzzy methods are computational effectiveness, simple implementation, lower data sample rate requirement and reliability. Moreover, fuzzy methods do not require training like SOM and HMM. Therefore, the methods can be applied to the real time systems where different gestures can be used, for example, instead of the keyboard functions. The computational effectiveness and low sample rate requirement also increases the operational time of device compared to computationally heavy HMM method. Furthermore, the easy implementation and reliability are important factors for the success of the new technology's spreading on the mass market of terminals.
机译:本文介绍并描述了一种基于软计算的手势识别专家系统,作为移动终端智能用户界面的一部分。在提出的解决方案中,终端包括三个加速度传感器,它们的位置类似于xyz坐标系,以便获得三维(3D)加速度矢量xyz。在多普勒频谱定义之后,将3D加速度矢量用作嵌入式专家系统的模糊推理单元的输入矢量,该模糊推理单元对手势进行分类(加速度矢量的时间序列)。在推理单元中,使用模糊规则辅助方法进行分类。该方法与具有特征提取的模糊c均值分类,隐马尔可夫模型(HMM)分类和SOM分类进行了比较。模糊方法成功地将测试集分类。模糊方法的优点是计算效率高,实现简单,数据采样率要求低和可靠性高。此外,模糊方法不需要像SOM和HMM这样的训练。因此,这些方法可以应用于例如可以使用不同手势代替键盘功能的实时系统。与计算量大的HMM方法相比,计算效率和低采样率要求也增加了设备的运行时间。此外,易于实施和可靠性是新技术在终端大众市场上成功推广的重要因素。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号