CSI-Based Human Continuous Activity Recognition Using GMM–HMM

Abstract

Recently, device-free human activity recognition has become a research hotspot, and great progress has been made in ubiquitous computing. Among the different kinds of implementations, activity recognition based on WiFi channel state information (CSI) has attracted enormous attention for its superiority compared with conventional approaches. In this article, a device-free human continuous activity recognition system based on WiFi CSI is proposed. First, the CSI phase difference expansion matrix is constructed as a more obvious activity recognition feature, and a method based on threshold combined with labeling is used to achieve continuous activity segmentation. Then, the Gaussian mixture model–hidden Markov model (GMM–HMM) is used to model the CSI feature data of each activity, which is originally used for human 3-D skeleton-based activity recognition. The approach is of great value not only for its high accuracy compared with other classification approaches, such as long short-term memory (LSTM) and convolutional neural network (CNN), but also for its tremendous advantage that a pretty short CSI time series could be used to identify human activities, thus saving computer memory, reducing system calculation time greatly, and improving the error tolerance rate of the segmentation. Experiments on measured activity datasets and methods comparison demonstrate the effectiveness and superiority of the proposed system. The factors affecting system performance, such as the length of the CSI time series, have been discussed in this article.