Hello everyone, my name is Chenshu Wu.
I am an assistant research scientist from the University of Maryland.
Today, I’m going to present our work, mSense, towards mobile material sensing with a single
millimeter-wave radio.
Material sensing could be useful for many
applications.
For example, it can facilitate household waste sorting, which are sometimes difficult to
distinguish, and can detect in-home leakage.
It can help robots to adjust their behaviors with the awareness of target materials.
An electric saw can stop itself when detecting human tissues.
Also, it could be used for interactive toys and package check.
For all these applications to be practical, however, the material sensing system should
be mobile!
However, existing low-cost solutions are not
truly mobile.
They usually need at least a pair of transceivers and use signals penetrating the objects.
They require two-sided setup or need to instrument the objects, and therefore, are not flexible
enough for mobile environments.
In this work, we ask the question: Is it possible
to identify the material from only the reflection signals measured by a single off-the-shelf
radio?
If yes, then we can have an easier setup for mobile scenarios.
Different from penetrating signals that indeed interact with the materials inside, the reflection
signals only touch the surface of an object.
So how can we obtain useful information about the materials?
Our key insight here is that, just like different objects reflect the light differently, different
materials also reflect RF signals in distinct ways.
For example, the figure on the right shows the diverse reflection properties of some
common materials.
With this in mind, we present the design and
implementation of mSense, a mobile material sensing system for ubiquitous environments.
Unlike existing system, we explore only the target reflections using a single 60GHz mmWave
radio.
Our device reuses an 802.11ay networking radio
by attaching an extra array to make it a radar.
It operates at 60GHz band and has 32 elements for each array.
It uses a bandwidth of 3.52GHz and offers a range resolution of 4.26 cm, which is much
better than the 2.4/5GHz WiFi.
The device outputs CIR measurement.
However, it is challenging to estimate the material reflectivity precisely from the CIR.
Mainly because the range resolution is not enough for material sensing.
There are also measurement noises and synchronization errors over the CIR time series.
To overcome these challenges, we propose a
pipeline to process the CIR.
First, we perform subsample peak interpolation to improve the range estimation to the sub-centimeter
precision.
We then leverage the direct leakage between the co-located transmitter and receiver to
synchronize all the CIRs.
We also employ noise cancellation to eliminate the hardware distortions and measurement noises
and obtain the components that only relate to the target reflection.
After these, we have the clean CIR and from it we can locate the target and obtain the
distance and CIR amplitude estimation.
We will use these values for material identification.
We then derive a novel material reflection
feature (MRF) that characterizes the material’s reflectivity and accordingly associates the
target’s material type.
Using existing propagation model and reflection model, we define the MRF as a function of
the received amplitude and the target’s distance, which can be calculated from what
we already obtained.
The proposed MRF turns out to be independent of the environments, and the propagation distance, and the object size and thickness and so on,
and thus allows material sensing in mobile contexts with flexible setup.
With that, we can construct a profile for each material and perform best matching to
identify an unknown material.
One point we’d like to make here is the
device orientation.
Minor distortions in the radio orientation alter the signal propagation, but the impacts
on the received amplitude are negligible.
This is an important property since it allows natural hand motions in mobile scenarios.
Now let me show the evaluation results. We first evaluate with five
common types of materials.
The overall recognition accuracy is 93% and retains 90% for the mobile case, which we believe
is promising for everyday application.
We also carry out a case study for 21 daily
objects like tables and chairs, building materials, kitchen utensils, toys.
As seen from the results, most objects of similar materials are correctly recognized
as the same basic types, regardless it is woods, plastics, or metals.
Now let me conclude this work.
We present mSense to demonstrate the feasibility of mobile material sensing based on the reflection
signals with a single mmWave radio We introduce a novel feature that characterizes
the target’s reflectivity and thus identify its material type.
We prototype mSense by reusing a commodity 60GHz networking chipset as radar and validate
the performance in both static and mobile cases with common materials and daily objects.
That’s the end of my talk.
For more information, please refer to our paper.
Thank you for your listening and watching.