School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China.
Sensors (Basel). 2021 Mar 20;21(6):2181. doi: 10.3390/s21062181.
Currently, there are various works presented in the literature regarding the activity recognition based on WiFi. We observe that existing public data sets do not have enough data. In this work, we present a data augmentation method called window slicing. By slicing the original data, we get multiple samples for one raw datum. As a result, the size of the data set can be increased. On the basis of the experiments performed on a public data set and our collected data set, we observe that the proposed method assists in improving the results. It is notable that, on the public data set, the activity recognition accuracy improves from 88.13% to 97.12%. Similarly, the recognition accuracy is also improved for the data set collected in this work. Although the proposed method is simple, it effectively enhances the recognition accuracy. It is a general channel state information (CSI) data augmentation method. In addition, the proposed method demonstrates good interpretability.
目前，文献中有许多基于 WiFi 的活动识别工作。我们观察到现有的公共数据集没有足够的数据。在这项工作中，我们提出了一种称为窗口切片的数据增强方法。通过对原始数据进行切片，我们可以为一个原始数据得到多个样本。因此，可以增加数据集的大小。在对公共数据集和我们收集的数据集进行实验的基础上，我们观察到所提出的方法有助于提高结果。值得注意的是，在公共数据集上，活动识别准确率从 88.13%提高到 97.12%。同样，在这项工作中收集的数据集中，识别准确率也得到了提高。虽然所提出的方法很简单，但它有效地提高了识别精度。它是一种通用的信道状态信息 (CSI) 数据增强方法。此外，该方法具有良好的可解释性。
