RF Sensing for Human Motion Recognition Driven Ambient Intelligence
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 760
Special Issue Editors
Interests: radar signal processing and physics-aware machine learning with application to RF micro-Doppler signature analysis; human activity recognition; gait analysis; assisted living and human–computer interaction
Interests: passive sensing using wireless-based digital communications; classifying people’s actions based on their radar micro-Doppler signatures, and development of techniques for joint communincation and sensing; indoor mapping; high-throughput data processing
Special Issue Information
Dear Colleagues,
The monitoring of human activities using RF sensors has become a topic of great interest as an enabling technology for ambient intelligence applications ranging from security, remote health monitoring, and smart homes to gesture recognition and human–computer interaction. Both passive and active RF sensing offer the advantages of non-contact perception even when there is no light or in through-the-wall scenarios, while also protecting privacy. While the advent of low-cost, RF transceiver chipsets has made RF sensors more ubiquitous, deep learning has driven rapid advancements in recognition accuracy. Nevertheless, there are still significant challenges to be solved to achieve pervasive, effective, and robust human motion recognition with RF sensors.
We invite reviews and original research articles for this forthcoming Special Issue that involve signal processing and machine learning techniques for human-motion-enabled ambient intelligence with RF sensors. These include but are not limited to:
- Novel RF human motion sensing applications;
- Passive wi-fi radar;
- Millimeter wave radar;
- Multi-static radar;
- Cognitive radar for motion classification;
- Deep learning techniques tailored to RF datasets;
- Physics-aware machine learning;
- Open-set and sequential human motion recognition;
- Leveraging additional sensors to aid RF-based learning;
- Translation of research to real-world scenarios.
Dr. Sevgi Zubeyde Gurbuz
Dr. Kevin Chetty
Guest Editors
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Keywords
- Human motion recognition
- Micro-Doppler
- Deep learning
- Machine learning
- Passive wi-fi
- Millimeter wave radar
- RF sensing
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