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首页> 外文期刊>Industrial Electronics, IEEE Transactions on >An Accelerometer-Based Digital Pen With a Trajectory Recognition Algorithm for Handwritten Digit and Gesture Recognition
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An Accelerometer-Based Digital Pen With a Trajectory Recognition Algorithm for Handwritten Digit and Gesture Recognition

机译：具有轨迹识别算法的基于加速度计的数字笔，用于手写数字和手势识别

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摘要

This paper presents an accelerometer-based digital pen for handwritten digit and gesture trajectory recognition applications. The digital pen consists of a triaxial accelerometer, a microcontroller, and an RF wireless transmission module for sensing and collecting accelerations of handwriting and gesture trajectories. The proposed trajectory recognition algorithm composes of the procedures of acceleration acquisition, signal preprocessing, feature generation, feature selection, and feature extraction. The algorithm is capable of translating time-series acceleration signals into important feature vectors. Users can use the pen to write digits or make hand gestures, and the accelerations of hand motions measured by the accelerometer are wirelessly transmitted to a computer for online trajectory recognition. The algorithm first extracts the time- and frequency-domain features from the acceleration signals and, then, further identifies the most important features by a hybrid method: kernel-based class separability for selecting significant features and linear discriminant analysis for reducing the dimension of features. The reduced features are sent to a trained probabilistic neural network for recognition. Our experimental results have successfully validated the effectiveness of the trajectory recognition algorithm for handwritten digit and gesture recognition using the proposed digital pen.
机译：本文提出了一种基于加速度计的数字笔，用于手写数字和手势轨迹识别应用。数字笔包括一个三轴加速度计，一个微控制器和一个RF无线传输模块，用于感应和收集笔迹和手势轨迹的加速度。所提出的轨迹识别算法由加速度获取，信号预处理，特征生成，特征选择和特征提取过程组成。该算法能够将时序加速度信号转换为重要的特征向量。用户可以使用笔来写数字或做手势，并且由加速度计测量的手部动作的加速度会无线传输到计算机以进行在线轨迹识别。该算法首先从加速度信号中提取时域和频域特征，然后通过混合方法进一步识别最重要的特征：基于核的类可分离性以选择重要特征，以及线性判别分析以减小特征的维数。缩减后的特征将发送到经过训练的概率神经网络进行识别。我们的实验结果已成功验证了使用拟议的数字笔进行轨迹识别算法对手写数字和手势识别的有效性。

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