Introduction
Human behavior recognition is the core technology that enables a wide variety of human-machine systems and applications, e.g., health care, smart homes, and fitness tracking. Traditional approaches mainly use cameras, radars, or wearable sensors. However, all these approaches have certain disadvantages. For example, camera based approaches have the limitations of requiring line of sight with enough lighting and breaching human privacy potentially. Low cost radar based solutions have limited operation range of just tens of centimeters. Wearable sensor based approaches are inconvenient sometimes because of the sensors that users have to wear. Recently, Wi-Fi Channel State Information (CSI) based human behavior recognition approaches are attracting increasing attentions. The rational is that different human behaviors introduce different multi-path distortions in Wi-Fi CSI. Compared with traditional approaches, the key advantages of Wi-Fi CSI based approaches are that they do not require lighting, provide better coverage as they can operate through walls, preserve user privacy, and do not require users to carry any devices as they rely on the Wi-Fi signals reflected by humans. As a result, the recognition of quite a number of behaviors that are difficult based on traditional approaches have now become possible, e.g., fine-grained movements (e.g., gesture and lip language), keystrokes, drawings, gait patterns, vital signals (e.g., breathing rate and heart rate), etc. However, Wi-Fi CSI based behavior recognition still faces a number of challenges: How to build the CSI-behavior model and algorithm that are robust for different humans? How to overcome the impact of noise and ensure the performance of CSI-enabled systems? How to simultaneously recognize the behavior of multiple users? How the CSI-enabled system can adapt and evolve according to the environment change?

This feature topic issue of IEEE Communications Magazine provides the opportunity for researchers and product developers to review and discuss the state-of-the-art and trends of CSI based behavior recognition techniques and systems.

In the light of the above, the main purpose of this special issue is threefold:
（1）To promote unprecedented approaches and techniques in signal processing, feature extraction, data mining and model construction for behavior recognition based on Wi-Fi CSI;
（2）To identify open issues which remain a challenge towards the convergence of computation theories and technologies for behavior recognition based on Wi-Fi CSI;
（3）To exploit novel application areas and demonstrate the benefits of Wi-Fi CSI in contrast with more traditional sensing approaches.

Topics may include (but are not limited to):
- Behavior Recognition Model/Theory based on Wi-Fi CSI
- Behavior Recognition Algorithms based on Wi-Fi CSI
- Wi-Fi CSI Signal Processing for Behavior Recognition
- Wi-Fi CSI Data Mining for Behavior Recognition
- Novel Behavior Recognition Applications/Systems Supported by Wi-Fi CSI
- Evaluation Metrics and Empirical Studies of Wi-Fi CSI enabled Systems
- Quality-enhanced and adaptive sensing models with Wi-Fi CSI

Submissions
Papers must be tailored to the problems of Wi-Fi CSI-enabled behavior recognition and explicitly consider the above issues. The Guest Editors reserve the right to reject papers they deem to be out of scope of this FT. Only originally unpublished contributions and invited articles will be considered for this FT.

Articles should be tutorial in nature, with the intended audience being all members of the communications technology community. They should be written in a style comprehensible to readers outside the specialty of the article. Mathematical equations should not be used (in justified cases up to three simple equations are allowed). Articles should not exceed 4500 words. Figures and tables should be limited to a combined total of six. The number of references is recommended to not exceed 15. In some rare cases, more mathematical equations, figures, and tables may be allowed if well-justified. In general, however, mathematics should be avoided; instead, references to papers containing the relevant mathematics should be provided. Complete guidelines for preparation of the manuscript are posted at http://www.comsoc.org/commag/paper-submission-guidelines. Please submit a pdf (preferred) or MS WORD-formatted paper via Manuscript Central (http://mc.manuscriptcentral.com/commag-ieee). Register or log in, and go to Author Center. Follow the instructions there. Select "October 2017 / Behavior Recognition Based on Wi-Fi CSI" as the Feature Topic category for your submission.

Guest Editors
Bin Guo (Corresponding Guest Editor)
Northwestern Polytechnical University, China
guobin.keio@gmail.com

Jennifer Chen
Stevens Institute of Technology, USA
yingying.chen@stevens.edu

Nic Lane
Bell Labs & University College London
niclane@acm.org

Chonggang Wang
InterDigital Communications, USA
drchongwang@gmail.com

Zhiwen Yu
Northwestern Polytechnical University, China
zhiweny@gmail.com

Important Dates
Manuscript Submission: February 1, 2017
Decision Notification: June 1, 2017
Final Manuscript Due Date: July 15, 2017
Publication Date: October 2017