基于层次分类的手机位置无关的动作识别

王昌海; 许昱玮; 张建忠

doi:10.11999/JEIT160253

基于层次分类的手机位置无关的动作识别

doi: 10.11999/JEIT160253

基金项目:

天津市重大科技专项(13ZCZDGX01098)，天津市自然科学基金(16JCQNJC00700)

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- 被引次数: 0
出版历程
- 收稿日期: 2016-03-17
- 修回日期: 2016-08-01
- 刊出日期: 2017-01-19

Hierarchical Classification-based Smartphone Displacement Free Activity Recognition

Funds:

The Key Project in Tianjin Science Technology Pillar Program (13ZCZDGX01098), The Natural Science Foundation of Tianjin (16JCQNJC00700)

摘要

摘要: 使用智能手机中集成的加速度传感器识别用户日常动作在惯性定位、个性化推荐、运动量评估等领域有重要的应用。手机位置不固定导致的动作识别率低下是该领域面临的主要问题。为了提高手机位置不固定时的动作识别率，该文提出一种基于层次分类的动作识别方法。该方法将动作识别分为多层，每一层包含一个分类器。在训练某一层分类器时，首先根据本层训练样本集进行特征选择并训练分类器。然后使用训练得到的分类器对训练样本分类，并计算分类结果的可信度。最后通过对低可信度的样本进行剪枝得到下层分类器的训练样本。对未知类别的样本分类时，首先使用第1层分类器分类。如果分类结果可信度较高，则分类结束；否则使用下层分类器分类，直至所有分类器遍历完。实验部分通过对采集的动作数据进行仿真，验证了该文方法的有效性。结果表明，与单层分类器相比，该方法可以将动作识别率由85.2%提高至89.2%。
- 动作识别 /
- 加速度传感器 /
- 层次分类 /
- 特征选择
Abstract: Human activity recognition based on accelerometer embedded in smartphones is wildly applied to inertial positioning, personalized recommendation, daily exercise estimating and other fields. The low recognition rate which caused by varying phone displacement is a crucial problem which needs to solve. To improve the recognition rate when the phones displacement is unfixed, a hierarchical classification-based activity recognition method is proposed. The activity recognition process is divided into multiple layers in this method, and each layer contains a classifier. For training each layers classifier, it runs the feature selection algorithm first, and the classifier is trained based on the selected features. Then, the trained classifier is used to classify the training set, and each samples classification confidence is calculated. Finally, samples whose confidence is lower than the hierarchical threshold are selected as the next layers training set. This process continues until each activitys sample number is less than the predefined pruning threshold. When an unlabeled sample comes, the first layer is used to classify this sample. If the classification confidence is higher than the hierarchical threshold, the recognition is over. Otherwise, the next layer will repeat this process until all the layers are traversed. The experiment collects activity data, and simulates the activity recognition. The simulation show that compared with the current methods, this method may improve the recognition rate from 85.2% to 89.2%.
- Activity recognition /
- Accelerometer /
- Hierarchical classification /
- Feature selection

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参考文献(17)

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