• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on industrial informatics >Hand Body Language Gesture Recognition Based on Signals From Specialized Glove and Machine Learning Algorithms
【24h】

Hand Body Language Gesture Recognition Based on Signals From Specialized Glove and Machine Learning Algorithms

机译：基于专业手套信号和机器学习算法的手势语言手势识别

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The man–machine interface (MMI) is one of the most exciting areas of contemporary research. To make the MMI as convenient for a human as possible, it is desirable that efficient algorithms for recognizing body language are developed. This paper presents a system for quick and effective recognition of gestures of hand body language, based on data from a specialized glove equipped with ten sensors. In the experiment, 10 people performed 22 hand body language gestures. Each of the 22 gestures was executed 10 times. Collected data were preprocessed in multiple ways and three machine learning algorithms were designed based on classifiers (probabilistic neural network, support vector machine, and k-nearest neighbors algorithm) trained and tested by a tenfold cross-validation technique. The best designed classifiers gained effectiveness of gesture recognition at = 98.24% with a very short time of testing, below 1 ms. The experiments confirm that efficient and quick recognition of hand body language is possible.
机译：人机界面（MMI）是当代研究中最令人兴奋的领域之一。为了使MMI对人类尽可能方便，期望开发出有效的识别肢体语言的算法。本文基于来自配备有十个传感器的专用手套的数据，提出了一种用于快速有效识别手部肢体语言手势的系统。在实验中，有10个人执行了22种手势语言手势。 22个手势中的每个手势均执行10次。通过多种方式对收集的数据进行预处理，并基于通过十倍交叉验证技术训练和测试的分类器（概率神经网络，支持向量机和k最近邻算法）设计了三种机器学习算法。最佳设计的分类器通过非常短的测试时间（小于1毫秒）获得了98.24％的手势识别效果。实验证实，可以高效，快速地识别手部肢体语言。

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