Abstract
Recognition of the context of humans plays an important role in pervasive applications such as intrusion detection, human density estimation for heating, ventilation and air-conditioning in smart buildings, as well as safety guarantee for workers during human-robot interaction. Radio vision is able to provide these sensing capabilities with low privacy intrusion. A common challenge though, for current radio sensing solutions is to distinguish simultaneous movement from multiple subjects. We present an approach that exploits antenna installations, for instance, found in upcoming 5G technology, to detect and extract activities from spatially scattered human targets in an ad-hoc manner in arbitrary environments and without prior training of the multi-subject detection. We perform receiver-side beamforming and beam-sweeping over different azimuth angles to detect human presence in those regions separately. We characterize the resultant fluctuations in the spatial streams due to human influence using a case study and make the traces publicly available. We demonstrate the potential of this approach through two applications: 1) By feeding the similarities of the resulting spatial streams into a clustering algorithm, we count the humans in a given area without prior training. (up to 6 people in a 22.4 m2 area with an accuracy that significantly exceeds the related work). 2) We demonstrate that simultaneously conducted activities and gestures can be extracted from the spatial streams through blind source separation.
Original language | English |
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Title of host publication | 18th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2020 |
Publisher | IEEE |
Number of pages | 10 |
ISBN (Electronic) | 9781728146577 |
DOIs | |
Publication status | Published - Mar 2020 |
MoE publication type | A4 Conference publication |
Event | IEEE International Conference on Pervasive Computing and Communications - Austin, United States Duration: 23 Mar 2020 → 27 Mar 2020 Conference number: 18 |
Conference
Conference | IEEE International Conference on Pervasive Computing and Communications |
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Abbreviated title | PerCom |
Country/Territory | United States |
City | Austin |
Period | 23/03/2020 → 27/03/2020 |
Keywords
- Beamsteering
- Multi-subject recognition
- Radio sensing
- Training-free crowd counting
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1X4 radio channel data at 3.42 GHz for device-free human sensing
Palipana, S. (Creator), Malm, N. (Creator) & Sigg, S. (Creator), Zenodo, 1 Jul 2019
Dataset