Device-free human micro-activity recognition method using WiFi signals

ABSTRACT

Human activity tracking plays a vital role in human–computer interaction. Traditional human activity recognition (HAR) methods adopt special devices, such as cameras and sensors, to track both macro- and micro-activities. Recently, wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment. This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information (CSI) of wireless signals. Different from existed CSI-based micro-activity recognition methods, the proposed method extracts both amplitude and phase information from CSI, thereby providing more information and increasing detection accuracy. The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity. We applied a machine learning algorithm to recognize the proposed micro-activities. The proposed method has been evaluated in both line of sight (LOS) and none line of sight (NLOS) scenarios, and the empirical results demonstrate the effectiveness of the proposed method with several users.

KEYWORDS:

• Human activity recognition
• channel state information
• WiFi
• device-free
• micro-activity recognition
• machine learning

Acknowledgments

We would like to thank the committee chair of the 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) and the reviewers for their insightful comments and suggestions that improved the quality of this manuscript. We would like to thank them for recommending this article. We also thank the editorial staff and reviewers of GSIS for their consideration and valuable comments.

Additional information

Notes on contributors

Mohammed A. A. Al-qaness

Mohammed A. A. Al-qaness received the B.S., M.S., and Ph.D. degrees in information and communication engineering from the Wuhan University of Technology in 2010, 2014, and 2017, respectively. He is currently a Lecturer at the School of Computer Science, Wuhan University, Wuhan, China. He has published over ten papers in wireless and mobile computing and indoor human tracking and activity recognition. His current research interests include wireless sensing, mobile computing, machine learning, and image classification.