College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China.
Gansu Province Internet of Things Engineering Research Center, Lanzhou 730070, China.
Sensors (Basel). 2022 Jan 5;22(1):402. doi: 10.3390/s22010402.
Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually carries a large amount of human-related information, the motion-recognition model trained for a specific person usually does not work well in predicting another person's motion. To deal with the difference, we propose a personnel-independent action-recognition model called WiPg, which is built by convolutional neural network (CNN) and generative adversarial network (GAN). According to CSI data of 14 yoga movements of 10 experimenters with different body types, model training and testing were carried out, and the recognition results, independent of bod type, were obtained. The experimental results show that the average correct rate of WiPg can reach 92.7% for recognition of the 14 yoga poses, and WiPg realizes "cross-personnel" movement recognition with excellent recognition performance.
目前，运动识别的应用范围很广。最近，通过分析 Wi-Fi 数据包中的信道状态信息 (CSI) 来进行运动识别的方法越来越受到学者们的青睐。由于在人类活动的无线信号环境中采集到的 CSI 通常携带大量与人相关的信息，因此针对特定人员训练的运动识别模型通常无法很好地预测其他人的运动。为了解决这种差异，我们提出了一种人员无关的动作识别模型，称为 WiPg，它是由卷积神经网络 (CNN) 和生成对抗网络 (GAN) 构建的。根据 10 名不同体型实验者的 14 种瑜伽动作的 CSI 数据进行了模型训练和测试，得到了与体型无关的识别结果。实验结果表明，WiPg 对 14 种瑜伽姿势的识别平均准确率可达 92.7%，并且 WiPg 实现了“跨人员”运动识别，具有出色的识别性能。
