TSCNN: A 3D Convolutional Activity Recognition Network Based on RFID RSSI
Weiqing Huang, Yi Liu, Shaoyi Zhu, Siye Wang, Yanfang Zhang
Abstract
Human activity recognition has a wide range of applications, especially for the care of elderly people living alone and the monitoring of abnormal behaviors of key personnel. Although conventional video surveillance technology has made many research advances in this field, this technology destroys people's privacy. Activity recognition technology based on RFID avoids damage to people's privacy, and is being widely studied and applied. This paper uses RFID Received Signal Strength Indicator (RSSI) to identify and classify human behaviors. Predecessors employed CNN and LSTM for human activity identification, but there were still some shortcomings: 1) The 2D convolution loses the temporal information of continuous actions and reduces the classification accuracy. 2) LSTM network has a series of training difficulties. 3) No available public dataset for the current mission. To solve these problems, this paper proposes a convolutional neural network called temporal spatial convolutional neural network (TSCNN). Taking the continuous frame sequence as input, the network is designed using 3D convolution to realize realtime activities recognition. The average classification accuracy of our network is 94.6%, 15.6% higher than the state-of-the- art - Tagfree. Our lowest accuracy is 81.8%, and Tagfree is 35.4%. Besides, the ablation experiment proves the necessity of the design in the TSCNN network. Furthermore, we collect more than 60000 RFID signal data and transform them into corresponding pixel maps to form a new dataset. We present and expose the dataset called RF-men.