首页> 外文会议>Symposium on Intelligent Manufacturing and Mechatronics >Ontological Framework of Arm Gesture Information for the Human Upper Body
【24h】

Ontological Framework of Arm Gesture Information for the Human Upper Body

机译:人类上半身的手臂姿态信息的本体论框架

获取原文

摘要

In the research of the human motion analysis, the characteristic movements of the human upper body are intensively investigated for many applications such as sign language recognition, robot control and gait analysis. The human upper body consists of many body parts such as both arms including fingers, facials and head movements. Previously, many researches proposed various sensors to record arm movements and the acquired data are used to train the computer understand the behavioral motion of arms movements by using various algorithmic approaches. However, the current challenge is to increase the knowledge level of the computational systems to recognize gestural information containing in arm movements. The objective of this paper is to construct and derive the arm movement's model based on the conceptual of ontology. The gestural information is investigated from characteristic features of arm movements. The knowledge of the computational systems about gestural information is developed by describing the characteristic features of arm movements in the form of the ontological framework. The ontological framework is defined as a structure containing characteristic features placed in mathematical order and has the relationship among them. Based on the mathematical model as proposed in this paper, the ontology framework could be used to describe knowledge of the arm gesture and could recognize it with a higher accuracy.
机译:在对人体运动分析的研究中,对许多应用的诸如手语识别,机器人控制和步态分析的许多应用进行了集中研究。人的上身由许多身体部位组成,例如两个臂,包括手指,面部护理和头部运动。此前,许多研究提出了用于记录臂运动的各种传感器,并且所获取的数据用于训练计算机通过使用各种算法方法来训练臂运动的行为运动。然而,目前的挑战是增加计算系统的知识水平,以识别包含在ARM运动中的识别信息。本文的目的是基于本体论的概念构建和推导武器运动模型。从臂运动的特征调查了姿态信息。通过描述本体框架形式的臂运动的特征特征,开发了关于识别信息的计算系统的知识。本体框架被定义为包含以数学顺序放置的特征特征的结构,并且具有它们之间的关系。基于本文提出的数学模型,本体框架可用于描述手臂手势的知识,并且可以以更高的准确性识别它。

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