Wireless Sensing for Human Activity: A Survey
Abstract
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.
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As Wi-Fi can travel through almost any indoor corner, human body movement or gesture changes can lead to small changes in signal propagation, and thus can be used to detect activities.
“As Wi-Fi can travel through almost any indoor corner, human body movement or gesture changes can lead to small changes in signal propagation, and thus can be used to detect activities.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [182]
Wi-Fi signal is one of the key modalities for human activity recognition and gesture recognition.
“Wi-Fi signal is one of the key modalities for human activity recognition and gesture recognition [182].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [182]
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A Survey on Human Behavior Recognition Using Channel State Information
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Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
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- Authors
- Liu, J., Liu, H., Chen, Y., Wang, Y., & Wang, C.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Indiana University – Purdue University Indianapolis (US)
- Binghamton University (US)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2020
- Volume
- 22
- Issue
- 3
- Pages
- 1629-1645
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2968303571
- Cited by
- 414
- References
- 137
- Citations by year
- 2026: 51 · 2025: 85 · 2024: 76 · 2023: 73 · 2022: 61 · 2021: 44 · 2020: 22 · 2019: 2
- FWCI
- 11.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- Funders
- National Science Foundation (CNS-1820624, CNS-1826647, CNS-1815908, CNS-1717356)
- Army Research Office (W911NF-18-1-0221)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- wireless sensing, human activity recognition, WiFi sensing, gesture recognition, vital signs monitoring, user identification, indoor positioning, intrusion detection, room occupancy monitoring, smart healthcare, home automation, frequency shift sensing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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