Hello, everyone!
I am Youwei Zeng from Peking University.
And today I will talk about: MultiSense: Enabling Multi-person Respiration Sensing with Commodity WiFi.
This is a joint work with my colleagues Dan Wu, Jie Xiong, Jinyi Liu, Zhaopeng Liu and
Daqing Zhang.
Accurate monitoring of human respiration plays a crucial role in a wide range of applications
in health care, such as sleep apnea detection and sleep stage recognition.
The principle behind these applications is that the rich information extracted from human
respiration can provide useful insights about the physiological state of an individual.
Recently, we have seen a lot of different approaches for respiration monitoring, such
as pulse oximeter, camera and CW radar.
Compared with these approaches, WiFi is more appealing because it's less intrusive; it
can work in dark; it's ubiquitous.
While most of existing WiFi-based approaches are dedicated to single-person scenarios,
we have seen efforts made to simultaneously monitor the respiration of multiple persons.
However, existing multi-person respiration sensing approaches mainly rely on spectral
analysis of the CSI amplitude to obtain respiration rate information, leading to multiple limitations:
First, spectral analysis fails to resolve two similar respiration rates.
Second, spectral analysis can only obtain the average respiration rate.
Third, they fail to sense the respiration when a target is located at the ''blind spots''.
How to robustly track respiration in the presence of multiple targets still faces lots of technical
challenges.
In this work, we aim at accurately and robustly monitoring multi-person respiration.
Let me show you how the respiration of multiple persons affects the WiFi CSI first.
In a typical multi-person scenario, WiFi signals propagate from transmitter to receiver through
multiple paths, including LoS (Line of Sight), reflection paths from static objects, and
reflection path from multiple targets.
Therefore, the WiFi CSI can de expressed as below.
As the CSI is a linear mixture of respiration signals, we can apply ICA method to separate
the respiration of each person.
That sounds really good.
However, the CSI obtained from commodity WiFi contains the amplitude impulse noise and the
time-varying phase offset.
Thus, we cannot directly apply ICA to separate multi-person respiration signals with the
raw CSI from commodity WiFi.
One method was proposed in the state-of-the-art that employs the ratio of CSI readings between
two antennas to cancel out the noise.
Unfortunately, such division operation distorts the linearity of the mixture so that the ICA
method cannot be directly applied, either.
So how to cancel out the noise in amplitude and phase without distorting the linearity
of mixture?
Interestingly, we find that If the denominator does not contain any respiration signal, the
linearity can be kept.
To create such a special denominator, we employ a weighted sum of CSI readings from M antennas
to null out the respiration signals.
In this way, we make ICA work for WiFi-based multi-person respiration monitoring.
Now, I' d like to show the evaluation results by video.
Here is the GUI of MultiSense: the left is the respiration pattern, the top right is
the video captured by a camera and the bottom right is the human status.
In this scenario, two subjects are lying on the bed.
The subject moves.
At last, I will summarize our work.
First, we propose a novel method to cancel out the noise in the original CSI amplitude
and phase without distorting the linear mixture of WiFi CSI.
Second, we design and implement MultiSense on commodity WiFi devices.
For the first time, MultiSense is able to extract respiration patterns with only a pair
of transceivers even in the presence of four persons, with the mean absolute respiration
rate error of 0.73 bpm.
Thanks for your attention.
Any questions?