Smart-phone-assisted Human Motion Recognition Based on Wavelet Transform
首发时间:2016-11-10
Abstract:Human motion recognition is becoming a research upsurge, which aims at understanding human behavior, and plays an increasingly important role in a number of applications, such as health care and smart home. In this paper, we collect datasets by using the built-in sensors of a mobile phone and propose an approach to extract features based on wavelet transform. In contrast to the existing related works, our work intends to recognize the physical activities when the phone's orientation and position are varying. The activities' true acceleration is inferred by using the phone's pitch, yaw and roll angles. After preprocessing, the continuous original time series data is segmented into discrete training samples by the sliding windows of proper size. Then statistical features such as wavelet coefficients are extracted through the wavelet transform. Support Vector Machine (SVM) is employed as classifier to recognize five types of motion: jumping, walking, running, stepping upstairs and stepping downstairs. We find a proper wavelet basis function to extract the features and achieve an average recognition accuracy of 90.71%. We can distinguish the five kinds of motion clearly, so the results show that it is feasible to use wavelet transform to extract features in human motion recognition.
keywords: Human motion recognition Wavelet transform Support vector machines (SVM)
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基于智能手机和小波变换的人体动作识别
摘要:人体运动识别正在成为一个研究热潮,其目的是了解人类的行为,在一些应用中起着越来越重要的作用,如健康保养和智能家居。本文中,我们通过内置传感器的智能手机收集数据集,并提出了一种基于小波变换的特征提取方法。与现存的相关研究相比,我们打算当手机的方向和位置是变化时仍然正确识别人体动作。所以通过使用手机的俯仰角,偏航角和横滚角推断出动作的真实加速度。经过预处理后,连续的原始时间序列数据被适当大小的滑动窗口分割成离散的训练样本,然后通过小波变换提取的统计特征,如小波系数等。最后采用支持向量机作为分类器来识别五种类型的运动:跳跃,行走,跑步,上楼和下楼。我们采取一个适当的小波基函数来提取特征,并实现90.71%的平均识别精度。实验结果可以清楚地分辨这五种运动,因此使用小波变换提取特征来识别人体运动是可行的。
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No.4708069116719214****
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