Robust Indoor Human Activity Recognition Using Wireless Signals

Sensors

Yi Wang¹  Xinli Jiang²  Rongyu Cao²  Xiyang Wang² 

[1] School of Software, Dalian University of Technology, Dalian 116620, China;

关键词: wireless sensing;    channel state information;    action recognition;   

DOI  :  10.3390/s150717195

来源: mdpi

【 摘 要 】

Wireless signals–based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions’ CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.