Behavior Recognition of the Elderly in Indoor Environment Based on Feature Fusion of Wi-Fi Perception and Videos

Abstract

With the intensifying aging of the population, the phenomenon of the elderly living alone is also increasing. Therefore, using modern internet of things technology to monitor the daily behavior of the elderly in indoors is a meaningful study. Video-based action recognition tasks are easily affected by object occlusion and weak ambient light, resulting in poor recognition performance. Therefore, this paper proposes an indoor human behavior recognition method based on wireless fidelity (Wi-Fi) perception and video feature fusion by utilizing the ability of Wi-Fi signals to carry environmental information during the propagation process. This paper uses the public WiFi-based activity recognition dataset (WIAR) containing Wi-Fi channel state information and essential action videos, and then extracts video feature vectors and Wi-Fi signal feature vectors in the datasets through the two-stream convolutional neural network and standard statistical algorithms, respectively. Then the two sets of feature vectors are fused, and finally, the action classification and recognition are performed by the support vector machine (SVM). The experiments in this paper contrast experiments between the two-stream network model and the methods in this paper under three different environments. And the accuracy of action recognition after adding Wi-Fi signal feature fusion is improved by 10% on average.

Keywords

human behavior recognition / two-stream convolution neural network / channel status information / feature fusion / support vector machine (SVM)