Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition

TL;DR

This paper introduces a novel C3D network with attention mechanism for WiFi-based human activity recognition, enabling simultaneous feature extraction and recognition, leading to improved accuracy over existing methods.

Contribution

The paper presents a new deep learning approach using C3D networks that jointly extract features and recognize actions from CSI signals, simplifying the process and enhancing performance.

Findings

Achieved superior recognition accuracy compared to benchmark methods.

Enabled simultaneous feature extraction and recognition with C3D network.

Validated effectiveness through experimental results.

Abstract

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.