Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (6): 1122-1132.DOI: 10.3778/j.issn.1673-9418.2005021

• Artificial Intelligence • Previous Articles     Next Articles

Efficient Human Behavior Recognition Method of CSI Based on Multi-antenna Judgment

TAO Zhiyong, GUO Jing, LIU Ying   

  1. School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China
  • Online:2021-06-01 Published:2021-06-03



  1. 辽宁工程技术大学 电子与信息工程学院,辽宁 葫芦岛 125105


Human motion and behavior analysis has become a new research field in pervasive computing. Aiming at the problems of high cost and low accuracy of current behavior identification methods, an efficient method MADR (multi-antenna joint decision efficient behavior recognition system) of CSI (channel state information) based on multi-antenna joint judgment is proposed. The method is divided into three steps: data processing, feature extraction, action and behavior classification. Firstly, to solve the problem that the original signals are susceptible to interference from environment and equipment, this method focuses on the data processing process. Hampel and low-pass filter are used to remove outliers and high-frequency noise, and principal component analysis is further used to remove in-band noise, so as to obtain smooth and stable data. Secondly, the invalid signals of the first principal component containing time-frequency domain details are eliminated by using the method based on sliding variance, and the feature vectors that effectively represent the behaviors and actions are obtained. Finally, in order to make full use of the CSI features of multiple antennas, a number of DTW-based FKNN (fast K nearest neighbor) classifiers are constructed to jointly judge behaviors at the level of neighboring samples. Experimental results show that the accuracy of the method is 95.33% and 92.67% respectively in the conference room and the laboratory, and the system training time is greatly reduced compared with the KNN (K nearest neighbor) classifier.

Key words: WiFi channel state information, multi-antenna joint decision, behavior recognition, fast K nearest neighbor (FKNN)



关键词: WiFi信道状态信息, 多天线联合判决, 行为识别, 快速K近邻(FKNN)