Capturing the human figure through a wall
Abstract
We present RF-Capture, a system that captures the human figure -- i.e., a coarse skeleton -- through a wall. RF-Capture tracks the 3D positions of a person's limbs and body parts even when the person is fully occluded from its sensor, and does so without placing any markers on the subject's body. In designing RF-Capture, we built on recent advances in wireless research, which have shown that certain radio frequency (RF) signals can traverse walls and reflect off the human body, allowing for the detection of human motion through walls. In contrast to these past systems which abstract the entire human body as a single point and find the overall location of that point through walls, we show how we can reconstruct various human body parts and stitch them together to capture the human figure. We built a prototype of RF-Capture and tested it on 15 subjects. Our results show that the system can capture a representative human figure through walls and use it to distinguish between various users.
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New applications such as tracking and identification of humans (through walls) as well as gesture and emotion recognition could be achieved using ML on radio signals.
“New applications beyond the traditional scope of communications, such as tracking and identification of humans (through walls) [65] as well as gesture and emotion recognition [66], could be achieved using ML on radio signals.”
From An Introduction to Deep Learning for the Physical Layer (2017) · cites this paper as [65]
Cites
An iterative technique for the rectification of observed distributions
Lucy, L. B. (1974) · The Astronomical Journal · doi:10.1086/111605 · Cited by 3,866 · cited 1× · see citing works
“5, and the deconvolution is done using the Lucy-Richardson method [Lucy 1974].”
MethodThe space of human body shapes: reconstruction and parameterization from range scans
Allen, B., Curless, B., & Popović, Z. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882311 · Cited by 913 · see citing works
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An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980 · see citing works
Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi
Zheng, Y., Zhang, Y., Qian, K., Zhang, G., Liu, Y., Wu, C., & Yang, Z. (2019) · Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3307334.3326081 · Cited by 557 · see citing works
Wireless Sensing for Human Activity: A Survey
Liu, J., Liu, H., Chen, Y., Wang, Y., & Wang, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2934489 · Cited by 414 · see citing works
Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network
Zhang, Z., Yang, K., Qian, J., & Zhang, L. (2019) · Sensors · doi:10.3390/s19143170 · Cited by 165 · see citing works
XGest: Enabling Cross-Label Gesture Recognition with RF Signals
Zhang, Y., Yang, Z., Zhang, G., Wu, C., & Zhang, L. (2021) · ACM Transactions on Sensor Networks · doi:10.1145/3458750 · Cited by 29 · see citing works
- Authors
- Adib, F., Hsu, C.-Y., Mao, H., Katabi, D., & Durand, F.
- Affiliations
- Massachusetts Institute of Technology
- Venue
- ACM Transactions on Graphics
- Published
- 2015-11-02
- Volume
- 34
- Issue
- 6
- Pages
- 1-13
- ISSN
- 0730-0301
- 1557-7368
- Cited by
- 402
- References
- 2
- Language
- en
- Funders
- National Science Foundation
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