Wireless Sensing for Human Activity: A Survey

By Jian Liu, Hongbo Liu, Yingying Chen, Yan Wang, Chen Wang. IEEE Communications Surveys and Tutorials, 2020

397 citations recorded by the source corpus.

With the advancement of wireless technologies and sensing methodologies, many studies have shown the success of re-using wireless signals (e.g., WiFi) to sense human activities and thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, gesture recognition to vital signs monitoring and user identification involving even finer-grained motion sensing. These applications arguably can brace various domains for smart home and office environments, including safety protection, well-being monitoring/management, smart healthcare and smart-appliance interaction. The movements of the human body impact the wireless signal propagation (e.g., reflection, diffraction and scattering), which provide great opportunities to capture human motions by analyzing the received wireless signals. Researchers take the advantage of the existing wireless links among mobile/smart devices (e.g., laptops, smartphones, smart thermostats, smart refrigerators and virtual assistance systems) by either extracting the ready-to-use signal measurements or adopting frequency modulated signals to detect the frequency shift. Due to the low-cost and non-intrusive sensing nature, wireless-based human activity sensing has drawn considerable attention and become a prominent research field over the past decade. In this paper, we survey the existing wireless sensing systems in terms of their basic principles, techniques and system structures. Particularly, we describe how the wireless signals could be utilized to facilitate an array of applications including intrusion detection, room occupancy monitoring, daily activity recognition, gesture recognition, vital signs monitoring, user identification and indoor localization. The future research directions and limitations of using wireless signals for human activity sensing are also discussed.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b42590f5f9bf7802d453dbe89544f27cc3f1b058

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In Computer Vision and Pattern Recognition

  1. Shan-zhi Chen

In Electrical and Electronic Engineering

  1. Kai Wu
  2. Senior Member Ieee Xiaojing Huang
  3. S. M. I. J. Andrew Zhang
  4. F. I. Y. Jay Guo
  5. Ieee Shanzhi Chen Fellow
  6. Fellow Ieee Jinhong Yuan
  7. Md. Lushanur Rahman
  8. Shan-zhi Chen

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