Hello, everyone.
I'm Chuyu from Nanjing University.
It is very honor to present our work called
DropMonitor mm-level sensing for RFID-based infusion drip rate monitoring.
This is a joint work with
my colleagues and students.
Next,
I will introduce our work
with the following six parts.
First, it's motivation.
As we all know, intravenous therapy is one of the most commonly use to clinical treatment methods in the hospitals.
So, the drip rate is a very important indicator of infusion monitoring.
Improper drip rate may lead to significant problems, or even affect the life of the patients.
Neither fast nor slow dip rates can can cause health problems, which are very dangerous for eld people and infants
Therefore, it is really necessary to satisfy the following three requirements
accurate, real time, and parallel.
However, existing drip rate monitoring methods usually have these drawbacks.
So we want to ask,
can we design a battery-free, lightweight, and easy to deploy solution using RFID?
Existing RFID-based solutions either sense the liquid or
use contact-based solutions to sense the vibrations.
They can not monitor the drip rate.
But DropMonitor can do it.
This is a gif for DropMonitor.
We deploy two tags besides a drip chamber and propose a novel model to distill the drip rate.
We can see,
although the raw signals of the two tags are very noisy,
our methods can still efficiently extract the correct drip rate.
So how to achieve this goal?
As we can see,
we deploy two tags beside the drip chamber for sensing.
The signal received by the tags are the summation of the free-space signal, reflected signal
When the droplet falls,
the liquid level insider the drip chamber is vibrating,
leading to the change of the reflected signals.
Then we can extract the drip rate from the signal changes.
Based on the principle,
we can provide a battery-free, light weight and easy to deploy way for drip monitoring.
To implement the system,
we need address two main challenges.
The first Challenge is
how to perform the mm-level sensing.
Particularly, both the falling stage and the vibration stage are mm-level.
We need to firstly investigate
which factor can indicate the droplet falling.
To answer the question,
we design two isolation experiments and find
the vibration of liquid surface can represent the falling of droplet.
We place the tag just above the liquid surface,
such that the vibration can be efficiently enlargered, according to our empirical study.
Then we can infer the drip rate from periodic patterns.
For the drip rate inference,
We build a signal model in the IQ plane
We can divide the signal received by the tag into the static part and the dynamic part.
We focus on the dynamic part.
When the liquid level changes,
the dynamic part of the signal changes and rotates in the IQ plane
Based on understanding,
the phase is changed due to the vibration of the liquid level.
which means we can use the phase to sense the vibrations.
However,
in a real sensing scenario,
there are lots of multi-path interference,
especially from the human activities, which are very common in the hospitals.
The second Challenge is
how to mitigate the multi-path interference.
We also conducted contract experiments in a real environment,
and find the multi-path interference totally drown out the phase mutation caused by the falling droplets
To solve the problem,
we deploy two tags besides the drip chamber for sensing.
One is the sensing tag,
And the other is the reference tag.
Based on our understanding,
we find the sensing tag can sense
both the interference of the human activities and the drip rate signal.
While the reference tag only senses interference of the human activities.
we can compute the difference of these two phase signals from the tag-pair to extract the drip rate.
After resolving the two challenges,
we next go over the system design.
We have three modules,
signal pre-processing, interference cancellation, and drip rate extraction.
For the interference cancellation,
we find each tag is read at different time stamps,
which can need to large error
when computing the phase difference.
To solve the problem,
we resample the signal phase
into the same sampling time stamp,
and then calculate phase difference.
As we can see,
after the resampling,
the phase change due to human activities are removed.
And then we leverage the frequency features to extract the drip rate.
We use auto-correlation to enhance the periodicity.
Then we detect the peaks in the correlation sequence and
leverage the cluster analysis to estimate the drip rate.
Next, we quickly go over the evaluation.
We implement the system with ImpinJ reader and Laird antenna.
and evaluate our system with different parameters.
We find DropMonitor can achieve the relative error below 2% for drip rate estimation
Moreover,
drop monitor can work well when some bodies are walking beside the drip chamber
Last but not least,
DropMonitor can simultaneously monitor over 10 users,
which guarantee the parallel usage in the hospital.
Based on the above system,
several hospitals have used our system for several weeks,
and achieve very stable and reliable performance.
We want to say that
the deployment of infusion clip is crucial,
and the liquid level in the drip chamber matters.
Finally, it's a conclusion.
In this paper,
we present a battery-free lightweight and easy to deploy solution to monitor the drip rate.
We also design a tag-pair solution to mitigate the multi-path interference.
and implement the system in real applications.
That's all for my presentation.
Thank you.