Hello everyone. I'm Shichao.
Today I will present our work BodyCompass: Monitoring Sleep Posture with Wireless Signals
This is a joint work with my colleagues:
Yuzhe, Hao, Rahul and Dina from MIT
As you may or may not know, many people die in their sleep
and many of those deaths are linked to sleep posture
about 35,000 people die in sleep each year worldwide
due to Sudden Unexpected Death in Epilepsy.
It can happen to any Epilepsy patient
and it sadly happened to Disney star Cameron last year
roughly three-quarters of those deaths are related to prone positions
possibly because it is more likely to cause suffocation
and because of this prone sleep is also an important risk factor
for sudden infant death syndrome as well.
Sleep postures also indicate Parkinson's progression
Parkinson patients have problem turning in their bed
and more the disease progresses
the harder it is for them to change postures
some sleep postures particularly the supine position
can also worsen sleep apnea
Thus doctors are interested in monitoring sleep posture
because this will allow them to manage patients better.
So the question is that, how can we allow doctors to monitor sleep postures?
Many researchers has proposed solutions for this task.
Unfortunately however, existing solutions have some major drawbacks
The first type of solutions using infrared camera to monitor the user when they are sleeping
Yet as you can imagine,
such methods are highly privacy intrusive.
An alternative type of solution put sensors
either on body or under the bed sheet
those methods address the privacy issue.
However, they make sleep uncomfortable
as they require the user to wear sensors or change the feeling of the bed.
How can we detect posture without invading privacy or affecting sleep comfort?
The solution is to use wireless signals.
We don’t need any cameras or any other sensors
Our device BodyCompass just like a Wi-Fi box.
We put it on the wall and it will automatically detect sleep posture of the user
by analyzing the wireless signals in the environment.
So, how does it work?
It turns out that our sleep posture affect the wireless reflection.
Let me explain it with this example.
There is a person sleeping on the bed with our device on the wall.
Our device emits low-power wireless signals
and it will be reflected back by the human body and the environment
since the chest area is much larger than the side
and the person is sleeping upwards
The majority of the reflected power comes from the ceiling.
And if the user is sleeping facing right
since now the chest is facing to the device.
The power of the direct reflection will be much larger
as shown on the right.
This gives us the clue to infer the sleep posture from wireless reflections.
However there is a challenge
wireless signals will be reflected by all the objects in the scene
For example we have a fan in the room
and it will reflect wireless signals as well
When those reflections get combined together
things will be messy
and it is nearly impossible to infer the posture.
So what can we do?
Our idea comes from the communication system
When we have multiple sources of signals
we separate them by giving each of them a different modulation
And in our scenario, we do have a modulation naturally.
The person breathes while the fan does not
Therefore only signals reflected by the person will be modulated by his breathing signals signals
And this enables us to filter out all the environmental reflections
With those insights, we build our system as follows
First we collect reflected signals from the environment
then separate them based on which signal is modulated by the user’s breathing signal
Next we feed those filtered signals into a deep neural network.
and finally the model will predict the body angle.
Body angle is defined as following
0 as supine, 90 to the right, 180 for prone and -90 for left
Finally let’s talk about evaluations.
We install our device into each of our subject’s own bedrooms
Just like the picture shown on the right.
In total, we have 26 subjects, in 26 different homes,
and we have collected 224 nights to evaluate the performance of our system.
And let me show the accuracy for each of our subject.
Each bar represents accuracy for that particular subject
and we can see that our system achieves a good accuracy across all the subjects
And on average, our system’s accuracy is 87%
Next, I want to show our system’s ability to track frequency of posture shift
which is moving from one posture to another.
This could be used for tracking Parkinson’s disease progression, as we mentioned earlier.
We plot the frequency of posture shifts for all 26 subjects
ordered in ascending order
As you can see from the figure
BodyCompass is capable of tracking the frequency of posture shifts accurately,
with a relative error of only 10%.
In conclusion,
in this talk, we present BodyCompass
the first wireless-based system that provides accurate sleep posture monitoring in the wild
Thanks for listening.