Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs
Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs
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DOI：
10.1155/2014/503291
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发表时间：
2014-01-01
影响因子：
2.3
通讯作者：
Laine, Teemu H.
中科院分区：
文献类型：
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作者：
Khan, Adil Mehmood;Tufail, Ali;Laine, Teemu H.
虽然人类活动识别(HAR)在过去十年中已经得到了广泛的研究，但智能手机上的HAR还是一个相对较新的领域。智能手机配备了各种传感器。融合这些传感器的数据可以使应用程序识别大量的活动。然而，实现这一目标是具有挑战性的。首先，这些设备资源不足，这限制了可以利用的传感器数量。其次，为了获得最佳性能，需要有效的特征提取、特征选择和分类方法。根据这些需求，本工作实现了一个基于智能手机的HAR方案。仅从3个智能手机传感器中提取了时域特征，并采用非线性判别方法对15个活动进行了高精度识别。这种方法不仅为每个活动从每个传感器中选择最相关的特征，而且还考虑了在不同位置携带手机所产生的差异。评估在脱机和在线设置中执行。我们的比较结果表明，该系统的性能优于以往一些基于手机的HAR系统。
Although human activity recognition (HAR) has been studied extensively in the past decade, HAR on smartphones is a relatively new area. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. Realizing this goal is challenging, however. Firstly, these devices are low on resources, which limits the number of sensors that can be utilized. Secondly, to achieve optimum performance efficient feature extraction, feature selection and classification methods are required. This work implements a smartphone-based HAR scheme in accordance with these requirements. Time domain features are extracted from only three smartphone sensors, and a nonlinear discriminatory approach is employed to recognize 15 activities with a high accuracy. This approach not only selects the most relevant features from each sensor for each activity but it also takes into account the differences resulting from carrying a phone at different positions. Evaluations are performed in both offline and online settings. Our comparison results show that the proposed system outperforms some previous mobile phone-based HAR systems.