  Breathing signal monitoring can provide important clues for human's physical
health problems. Comparing to existing techniques that require wearable devices
and special equipment, a more desirable approach is to provide contact-free and
long-term breathing rate monitoring by exploiting wireless signals. In this
paper, we propose TensorBeat, a system to employ channel state information
(CSI) phase difference data to intelligently estimate breathing rates for
multiple persons with commodity WiFi devices. The main idea is to leverage the
tensor decomposition technique to handle the CSI phase difference data. The
proposed TensorBeat scheme first obtains CSI phase difference data between
pairs of antennas at the WiFi receiver to create CSI tensor data. Then
Canonical Polyadic (CP) decomposition is applied to obtain the desired
breathing signals. A stable signal matching algorithm is developed to find the
decomposed signal pairs, and a peak detection method is applied to estimate the
breathing rates for multiple persons. Our experimental study shows that
TensorBeat can achieve high accuracy under different environments for
multi-person breathing rate monitoring.
