Beamsteering for training-free Recognition of Multiple Humans Performing Distinct Activities

Beamsteering for training-free Recognition of Multiple Humans Performing Distinct Activities. Palipana, S., Malm, N., & Sigg, S. In 18th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom) , 2020.
doi abstract bibtex

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 multi-antenna installations, for instance, found in upcoming 5G instrumentations, 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-steering 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 22.4m2 area with an accuracy that significantly exceeds the related work). 2) We further demonstrate that simultaneously conducted activities and gestures can be extracted from the spatial streams through blind source separation.

@InProceedings{Sameera_2020_Beamsteering,
  author    = {Sameera Palipana and Nicolas Malm and Stephan Sigg},
  booktitle = {18th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom) },
  title     = {Beamsteering for training-free Recognition of Multiple Humans Performing Distinct Activities},
  year      = {2020},
  doi = {10.1109/PerCom45495.2020.9127374},
  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 multi-antenna installations, for instance, found in upcoming 5G instrumentations, 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-steering 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 22.4m2 area with an accuracy that significantly exceeds the related work). 2) We further demonstrate that simultaneously conducted activities and gestures can be extracted from the spatial streams through blind source separation.},
  %url_Paper = {http://ambientintelligence.aalto.fi/paper/findling_closed_eye_eog.pdf},
  project = {radiosense},
  group = {ambience}
  }

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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. 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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 multi-antenna installations, for instance, found in upcoming 5G instrumentations, 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-steering 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. 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