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An Inertial Pen With Dynamic Time Warping Recognizer for Handwriting and Gesture Recognition

机译:带有动态时间规整识别器的惯性笔,用于手写和手势识别

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摘要

This paper presents an inertial-sensor-based digital pen (inertial pen) and its associated dynamic time warping (DTW)-based recognition algorithm for handwriting and gesture recognition. Users hold the inertial pen to write numerals or English lowercase letters and make hand gestures with their preferred handheld style and speed. The inertial signals generated by hand motions are wirelessly transmitted to a computer for online recognition. The proposed DTW-based recognition algorithm includes the procedures of inertial signal acquisition, signal preprocessing, motion detection, template selection, and recognition. We integrate signals collected from an accelerometer, a gyroscope, and a magnetometer into a quaternion-based complementary filter for reducing the integral errors caused by the signal drift or intrinsic noise of the gyroscope, which might reduce the accuracy of the orientation estimation. Furthermore, we have developed a minimal intra-class to maximal inter-class based template selection method (min-max template selection method) for a DTW recognizer to obtain a superior class separation for improved recognition. Experimental results have successfully validated the effectiveness of the DTW-based recognition algorithm for online handwriting and gesture recognition using the inertial pen.
机译:本文提出了一种基于惯性传感器的数字笔(惯性笔)及其相关的基于动态时间规整(DTW)的手写和手势识别算法。用户握住惯性笔来写数字或英文小写字母,并以自己喜欢的手持式样式和速度进行手势。手势产生的惯性信号被无线传输到计算机以进行在线识别。提出的基于DTW的识别算法包括惯性信号采集,信号预处理,运动检测,模板选择和识别的过程。我们将从加速度计,陀螺仪和磁力计收集的信号集成到基于四元数的互补滤波器中,以减少由陀螺仪的信号漂移或固有噪声引起的积分误差,这可能会降低方向估计的准确性。此外,我们为DTW识别器开发了基于最小类间到最大类间的模板选择方法(最小-最大模板选择方法),以获得用于改进识别的上级分类。实验结果已成功验证了基于DTW的识别算法对使用惯性笔进行在线手写和手势识别的有效性。

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