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首页> 外文期刊>IEEE sensors journal >Accelerometer-Based Hand Gesture Recognition by Neural Network and Similarity Matching
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Accelerometer-Based Hand Gesture Recognition by Neural Network and Similarity Matching

机译：神经网络和相似度匹配的基于加速度计的手势识别

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摘要

In this paper, we present an accelerometer-based pen-type sensing device and a user-independent hand gesture recognition algorithm. Users can hold the device to perform hand gestures with their preferred handheld styles. Gestures in our system are divided into two types: the basic gesture and the complex gesture, which can be represented as a basic gesture sequence. A dictionary of 24 gestures, including 8 basic gestures and 16 complex gestures, is defined. An effective segmentation algorithm is developed to identify individual basic gesture motion intervals automatically. Through segmentation, each complex gesture is segmented into several basic gestures. Based on the kinematics characteristics of the basic gesture, 25 features are extracted to train the feedforward neural network model. For basic gesture recognition, the input gestures are classified directly by the feedforward neural network classifier. Nevertheless, the input complex gestures go through an additional similarity matching procedure to identify the most similar sequences. The proposed recognition algorithm achieves almost perfect user-dependent and user-independent recognition accuracies for both basic and complex gestures. Experimental results based on 5 subjects, totaling 1600 trajectories, have successfully validated the effectiveness of the feedforward neural network and similarity matching-based gesture recognition algorithm.
机译：在本文中，我们提出了一种基于加速度计的笔型感测设备和与用户无关的手势识别算法。用户可以握住设备以其首选的手持式样式执行手势。我们系统中的手势分为两种类型：基本手势和复杂手势，它们可以表示为基本手势序列。定义了24个手势的字典，其中包括8个基本手势和16个复杂手势。开发了一种有效的分割算法来自动识别各个基本手势运动间隔。通过分割，每个复杂的手势都被分割成几个基本手势。根据基本手势的运动学特征，提取25个特征以训练前馈神经网络模型。对于基本手势识别，输入手势由前馈神经网络分类器直接分类。但是，输入的复杂手势会通过其他相似度匹配过程来识别最相似的序列。所提出的识别算法对于基本手势和复杂手势均实现了几乎完美的用户相关和用户独立的识别精度。基于5个主题（共1600个轨迹）的实验结果已成功验证了前馈神经网络和基于相似匹配的手势识别算法的有效性。

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