基于多传感器信息检测和融合的中国手语识别研究  

Chinese Sign Language Recognition Based on Multiple Sensors Information Detection and Fusion

作　　者：王文会[1] 陈香[1] 阳平[1] 李云[1] 杨基海[1] 

机构地区：[1]中国科学技术大学电子科学与技术系,合肥230027

出　　处：《中国生物医学工程学报》2010年第5期665-671,共7页Chinese Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(60703069);国家高技术研究发展(863)计划(2009AA01Z322)

摘　　要：有效融合加速计、摄像头和表面肌电3种低成本传感器在手势动作信息捕获上的优势,是提高手语手势识别率和种类的重要研究内容。提出一种基于多传感器信息检测和融合的中国手语识别方法:先利用表面肌电的幅值信息,对3类传感器信号进行手势分割,并实现单双手词的划分;然后借助视觉信号,完成有遮挡和无遮挡双手词的划分;最后利用Sugeno模糊积分,实现不同特征匹配结果的决策融合。结果表明,对4位受试者、201个高频手语词开展手势识别实验,其识别率均在99%以上,证明该基于多传感器信息检测和融合的手势识别方法在中国手语识别上的有效性。The efficient fusion of hand gesture information captured by a three-axis accelerometer,a webcam and four surface electromyography sensors is an important research field for improving the performance of sign language recognition system.In this paper,a multi-sensor information detection and fusion method was proposed.Firstly,the amplitude information of myoelectric signal was utilized to extract active segments of hand gestures and divide sign gestures into single-hand type and double-hand type.Then double-hand sign words were further classified into occlusion or non-occlusion class by vision signal.Lastly,decision-level fusion approach with Sugeno fuzzy integral was applied on local matching results of multiple classifiers for improving classification performance.Experimental results for 201 high-frequency sign words from 4 signers obtained the classification accuracy of more than 99%,indicating the effectiveness of the proposed method for Chinese sign language recognition.

关 键 词：手语识别 多传感器融合 隐马尔科夫 模糊积分 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]