东南大学学报（自然科学版）Issue(2)：239-243,5.DOI:10.3969/j.issn.1001-0505.2014.02.003
基于混合高斯模型的非固定握持姿势手势识别
Gesture recognition with unfixed holding position based on Gaussian mixture model
摘要
Abstract
In most gesture recognition researches,participants are asked to hold the data collecting device in fixed position strictly,which causes poor user experience.To solve this problem,an en-hancement algorithm based on the Gaussian mixture model (GMM)is presented.It can achieve ges-ture recognition with unfixed holding position and improve the interaction comfortability.First,the holding position information is abstracted from raw acceleration data by the GMM.Then the coordi-nate transformation is conducted and the gesture operating information is separated with the holding position information.To meet the requirements for stability and recognition speed,the parameter up-dating strategy of the GMM is improved by adding backup component and optimizing the priority consideration.The experimental results show that when the roll angle and the pitching angle are be-tween -60°to +60°,the yaw angle is between -20°to +20°,the holding position has no signifi-cant impact on recognition accuracy.So the gesture recognition algorithm is improved without fixed holding position,thus achieve better user experience.关键词
手势识别/混合高斯模型/用户体验Key words
gesture recognition/Gaussian mixture model/user experience分类
信息技术与安全科学引用本文复制引用
王原,汤勇明,王保平..基于混合高斯模型的非固定握持姿势手势识别[J].东南大学学报（自然科学版）,2014,(2):239-243,5.基金项目
国家高技术研究发展计划（863计划）资助项目（2012AA03A302）、高等学校学科创新引智计划资助项目（B07027）． （）
