Small-Sample Recognition of Location and Activity Algorithm Based on Residual Networks With Multiattention Layers

With the wide deployment of wireless networks, channel state information (CSI)-based joint recognition of location and activity boasts a wide range of application prospects in the fields of smart homes, remote health monitoring, and security alarm. However, the existing algorithms for joint recognition of location and activity can achieve high recognition accuracy only when trained on a large number of samples. To address this problem, this study proposes a novel, small-sample joint “location and activity” recognition algorithm based on residual networks with multiattention layers, referred to as the SSJR algorithm. In the algorithm, a small number of training samples are expanded via subtraction between CSI amplitude data of multiple links and horizontal flipping of images. Furthermore, a peak-seeking algorithm is proposed to decompose CSI data into location and activity recognition components, both of which are input into the constructed residual networks with multiattention layers for joint recognition. The algorithm can not only expand number of samples but also accurately extract and enhance the features of each category of samples. Experimental results indicate that the SSJR algorithm achieves an average recognition accuracy of 96.83% with only five training samples per category, demonstrating both its practicality and feasibility for human-computer interaction applications.