摘要
针对三维空间手写过程中采样装置横滚角变化较小的特点,提出一种基于双路MEMS加速度计的无陀螺硬件编排方案及相应的角速度、线加速度解算方法;根据解算得到的线加速度,同步计算得到笔画数量、笔画走向和归一化长度、总的笔画归一化长度等手势特征,并表示为手势特征码;手势特征模式库基于手势特征码集合定义,并基于手势特征码与不同模式间的差异化度量值实现用于手势识别的分类器。基于手势特征码的手写模式识别方法能够较全面描述字符的时域特征,且对无效笔画有较好的适应能力,仿真实验验证了该方法的可行性和有效性。
Considering the feature of 3D space handwriting process that the roll angle of sampling unit varies slightly, we present a dual MEMS accelerometers-based gyroscope-free hardware configuration scheme and the correlated solutions for angular velocity and linear acceleration calculation. The gesture features, including the number, the direction and normalised length, as well as the total normalised length of strokes are calculated synchronously according to the linear acceleration derived from the solution and are expressed as the feature codes of gestures as well, which will be collectively used to define the gesture patterns library; Moreover, based on differentiation metrics between a gesture feature code and the different gesture patterns the classifier for gesture recognition is implemented. The gesture feature codes-based handwriting pattern recognition method can make more comprehensive reflection of characters＇ time domain features, and has higher tolerance on redundant strokes. Simulation experiment verifies the feasibility and the effectiveness of the algorithm.
出处
《计算机应用与软件》
CSCD
2015年第5期193-197,共5页
Computer Applications and Software
基金
国家自然科学基金项目(61142002)
河南省人才培养联合基金项目(u1204614)
关键词
加速度计
角速度
手势特征
笔画
编辑距离
Accelerometer Angular velocity Gesture feature Stroke Levenshtein distance
作者简介
王祥雒，讲师，主研领域：机器学习，嵌入式微控制与传感技术。
杨春蕾，讲师。
郑瑞娟，副教授。
