Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition
This work addresses the challenge of complex CSI signal interpretation for human activity recognition, offering a simplified algorithm for domain-specific applications, though it appears incremental in method.
The paper tackles the problem of WiFi-based human activity recognition using CSI signals by proposing a C3D network that simultaneously handles feature extraction and recognition, achieving the best recognition performance compared to benchmark approaches.
Human actions recognition has attracted more and more people's attention. Many technology have been developed to express human action's features, such as image, skeleton-based, and channel state information(CSI). Among them, on account of CSI's easy to be equipped and undemanding for light, and it has gained more and more attention in some special scene. However, the relationship between CSI signal and human actions is very complex, and some preliminary work must be done to make CSI features easy to understand for computer. Nowadays, many work departed CSI-based features' action dealing into two parts. One part is for features extraction and dimension reduce, and the other part is for time series problems. Some of them even omitted one of the two part work. Therefore, the accuracies of current recognition systems are far from satisfactory. In this paper, we propose a new deep learning based approach, i.e. C3D network and C3D network with attention mechanism, for human actions recognition using CSI signals. This kind of network can make feature extraction from spatial convolution and temporal convolution simultaneously, and through this network the two part of CSI-based human actions recognition mentioned above can be realized at the same time. The entire algorithm structure is simplified. The experimental results show that our proposed C3D network is able to achieve the best recognition performance for all activities when compared with some benchmark approaches.