- Thanks for (clears throat) thanks for intro.
So, hello, everyone.
My name is Jun Gong.
I'm a fourth year PhD student
for our XDiscovery Lab at Dartmouth College.
Today, I'm going to talk about Indutivo.
Contact-based, object driven interactions
with inductive sensing.
This work was in collaboration with Xin,
Teddy, Josh, and my advisor, Xing-Dong Yang.
Before I talk about motivation and ready work,
first of all, I just want to quickly show you
what is Indutivo and how Indutivo works.
So, Indutivo is a contact-based inductive sensing technology
for contextual interactions.
If you use this,
the sensing coils can recognize conductive objects
and their individual movements
when you place this object against the sensor.
In this work, we chose smart-watch platform
to demonstrate it's uniques sensing capabilities.
So here's a short demo showing what we can
do with Indutivo.
Users can tap the utensil to trigger a fitness app
and a hinge to record calories.
Remove it to confirm the number.
I will talk more details about implementation and
applications later in this talk but before that
let's get back to the motivation.
Small wearables like Smart watches or IOT devices like
Amazon Echo or Nest Thermostat
are generally difficult to import
due to their small form factors
and lack of effective implementarities.
Just like smart watches as example, as you can see here
sometimes it is hard for us to find app we want
or to control it precisely.
To solve this, we propose to use contextual interactions
based on object recognition and manipulation
to enrich the implementrarities of these devices.
Our work was inspired by
many previous work that has been done
to enrich the interactions based on object recognition
and manipulation.
So next, I'm going to show you several examples.
EM sense recognize electrical objects
while electromagnetic signals
enable robust un touch object recognition.
So I decided instrument objects
was the passive IDs task
to classified object motion events
and why can it recognize objects that can generate
mechanical vibrations by boosting the simpering rate of
existing xerometer inside the smart watch.
And the rate I care to use is multichannel radar signals
to recognize electrical or non electrical objects,
and our approach is different, in that,
it cannot only recognize the conductive objects, but
it is also capable of sensing
the object lateral movements, precisely.
So, uh, next, let me explain how Indutivo works.
Our sensing system consists of,
an LC-tank including a inductor, a say the sensing coil
and a competitor.
So, the LC-tank is connected to an inductor sensor,
that can track the inductance wiring's.
When conductive object comes within the proximity of our
sensing coil, a circulating current induces
an electricmagn-electromagnetic field coupling
between the sensing coil and the conductive object.
This coupling will induce another circulating current,
called, Eddy current, on the surface of the
conductive object.
In turn, the induced Eddy current, will generate
it's own electromagnetic field, which opposes
the original field generated by the inductor.
Objects in different recessivity, size and shape,
will have different coupling effects,
which can be shown in the form of a slight shift
of the inductors of the LC-tank.
And this is what we use to distinguish
different conductive objects.
So with the existing principle in mind,
the key to the success of using Inductive sensing
is the design of the sensing coil,
because it effects the sensitivity, sensing range,
recognition, and tracking accuracy.
So, the first thing we need to consider,
is the coil shape.
In principle, the coil can be made into any shape,
but the most common ones are circle, square, hexagon,
and octagon.
The circular coil has the best coiling factor and the
lowest of serious resistance.
Allowing the largest sensing distance.
However, the trade-off is the sensing value,
is not that of linear proportional
to the distance between the target and the sensing coil.
So, in this work, in order to maximize,
the sensing distance,
to support a hinge movement,
that I have already showed you in the demo in the beginning,
we chose to use circular coil.
And second, we also need to consider the coil size
and arrangement.
Our goal is to fit the sensing coils around
the side of a smart watch, usually a rectangular region
of ten by 40 millimeters.
Larger coils provides longer sensing distance,
however, an array of small, but clustered coils can
better sense the size, shape, and lateral movements
of the object because we will have more data channels.
So, in order to balance the sensing distance
and the number of data channels we have,
we decided to use an array of five coils
in our final design.
So, last but not least, coil inductance is also
an important factor in our design process.
In principle, coils with lower inductance
can increase the sensitivity of the system.
Due to time limit,
I won't go into the details about how we calculate
inductance and how it help us to decide
the number of turns and the layer of the sensing coils.
So if you are interested,
please refer to our paper for more details.
And, here's our prototype.
We place the carefully design of sensing coil array on the
side of a smart watch.
The coils are connected to an induction sensing trip
from Texas Instruments.
Which can capture the inductance wiring's of these coils,
we use a two inch TFT display
to provide a visual feedback to our users.
So, last I'm going to discuss how to use this
Inductivo prototype to perform real time object recognition.
So first, for we need to collect inductance reference
footprint of each object to build our database.
The reference for point is a curved representation
of one-dimensional continuous inductance values
across the object contact si- area.
So, for each object we scan it by hand
to obtain the inductance reference for ping.
Just like this image.
So, the real time object recognition was implemented
by comparing the sensing data
with this pre collected database.
The closest match is our results.
We classified the object
that can be detected into four categories
First, for small conductive object who's contact
area is smaller than the sensing coil, like dime.
So, next I'm going to show you, a video demo
the detected item name
will be shown in the middle of the display.
And the bars below, will show the raw values
of each sensing coil.
Dime, adapter, binder clip,
USB cable,
candy box
and a key chain.
And the last category, is large conductive object,
who's contact area is larger than the sensor
some of them are metallic, where others are electronic
devices with building metallic components.
Like knife, scissors,
metal credit card,
kindle,
Nexus phone,
iPhone front, and back.
So for some of the conductive object,
we also try to attach a strip of copper tape
to create a unique reference for print
so that the same object can be used for different purposes.
Knife with copper tape,
scissors with copper tape,
and a candy box with the copper tape.
So to enable non conductive object sensing we
instrumenting using the copper tape with different
design patterns so that this can be distinguished
by the sensor.
As you can see here,
the system can successfully identify different books,
with different copper tape patterns.
To validate the system accuracy and robotics
we hire ten participant user study.
Each participant was asked to tap 23 objects
against the sensor five times.
The objects were assigned randomly to five locations;
living room, kitchen, computer desk, parking space
outside the building and inside the rolling car,
so that we can test if the system is robust
on the different environmental settings.
And it will use a reference database
collected by one volunteer, one week ago,
before the study to validate the data.
Know that this work here was not recorded again
in the study, and it turns out that
our system can retrieve an overall accuracy of 95.8%
which is worth promising
considering no per user calibration,
no user training and the considerable time separation
between the experiment and
when the reference data was collected.
Besides, the object recognition, Inductivo also
supports precise one dimensional object manipulation.
Including, sliding, hinging, and rotation.
After, the object's typed and predicted,
it's sliding movement can be detected
by sensing the shift of the position of it's
corresponding reference for print over the sensors.
In a former demo video, the purple curve
shows the reference for point
and the bars on the bottom shows the Raw sensor values.
The predicted location is shown on the right side
of the smart watch.
So, here's a demo, the dime is detected,
and tracked.
To measure the sliding accuracy,
we randomly pick one object from each category
including dime, credit card, instrument in knife,
and instrument in books read.
We asked one participant to wear the device
and slide the object within 40 millimeters
for three times.
The participants stop at two millimeters
and recorded predicted distance and object choose.
So, here are the results of four tested objects,
the x-axis shows different objects
while the y-axis shows the average error distance.
Across all objects the average error distance
is less than one millimeters.
And among these four objects,
metal credit card has the lowest accuracy.
It might because it's unbalanced material distribution.
Unlike sliding, for sensing hinging
we need to collect another database
of the four point references by hinging different objects.
The hinging angle can be estimated
by comparing the raw sense of values
with the hinging reference footprints in the database.
So, here's a demo video, tracking the hinging
angle of an instrumented knife.
So, using a simil-, using a similar evaluation
like sliding, here are the results.
The x-axis also shows different objects,
while the y-axis shows average error degree
across these three objects, the average error degree
is about 1.64 degrees.
Most of our errors, comes from the angle
away from the sensor.
Instrumenting knife, has the best performance
since the copper tape can be sensed
even when it is far away from the sensor.
Besides the sliding and hinging,
Inductivo also supports rotation.
To read a bowl rotation,
we place a strip of copper tape
around the barrel of a bottle cap.
The rest of the copper tape, gradually increase
to allow the sensor to pick up the caps orientation
based on strength of the signal.
Here, we support eight rotational directions.
And a similar evaluation, shows the system can retrieve
an average classification accuracy of 93 percent.
And here's a demo.
To demonstrate, the sensing capabilities
enabled by Inductivo we build four demo applications.
As you can see here,
a bottle cap can be used to launch your aircraft game
and use it as a rotating controller.
In your copy room, while waiting for a document to print
Tapping a binder clip can launch a Brick Breaker game
and use it as a sliding controller.
So tapping a dime on a sensor different locations can
trigger the corresponding actions like; play, or pause.
This helps to avoid the finger occluding the screen,
or forcing food from a hand.
While having a meal, this guy want to record the calories
in a fitness app, but his finger is dirty.
A utensil can be used to quickly launch the fitness app
and record the calories with hinging.
And that's for the demo applications.
So, next I will discuss the future work.
So, for the future work we believe inducting sensing
has great potential beyond the smart watches.
Especially for those small and implemented devices.
For example, inductive sensing can also
be added on smart rings supporting contextual input
by simply touching on different objects.
And with inductive sensing
user can also quickly switch between different modes
of digital clock by using different objects
and precisely adjust the corresponding values like time,
by manipulating the object.
For the IOT devices, like amazon echo,
different objects can quickly trigger different functions
and our own apps.
So, with that, I would like to conclude our work
with three take-home messages.
So, first of all, we propose a contact-based
inductive sensing technology for contextual interactions.
And second, our technol- our technique,
can recognize conductive object
as well as their individual movements
including sliding, hinging, and rotation.
Third, our approach can achieve 95.8 percent classification
accuracy with 23 daily objects
and precise one dimensional tracking.
So that's all I want to share about for today
and I'm more than happy to take any questions.
Thank you.
(applause)
- [Host] We have time for uh three or four questions,
so uh, take it away.
- [Dan Ashbrook] Hi, Dan Ashbrook, University of Copenhagen,
I'm curious what the limitations are on coil size.
Could you engineer it and have twice as many coils?
Could you have them in two dimensions.
- Uh, yeah sure, we can.
So basically this is what I have discussed in the uh, uh
coil design section, so basically we can add as many
as coils on the, on the, on the regular, yeah, yeah,
on the rectangular region of the smart watch,
so basically right now the limitation is the
space of the smart watch is only
about ten millimeters by 40 millimeters region,
so we need to consider the sensing distance,
the balance between the sensing distance
and the data channels we have.
If we add more uh coils into that,
we can have more data channels, but if we only have
but, but, the coils will be too small
and the sensing distance will be uh will be
decreased, so basically what in order to support
those uh hinge movement, because we need to our largest
sensing distance, so we,
we kind of pick a balanced design
So, right now we only use on array of the sensing coil.
And, we can definitely try more different combinations,
but we need to consider the, it depends on our design
uh, to, to design requirements.
- [Dan Ashbrook] Right, so you could theoretically
have one larger coil to sense far away,
and a bunch of smaller coils to get higher resolution.
- Yeah, yeah exactly.
- [Dan] Alright, cool, thanks.
- [Host] Any more questions?
- [Audience Member] So I have a question, um, have you,
did you encounter any um difficulties with ya know, um,
like for example the hand occluding on the uh,
inductive sensors,
so is there any like false triggers happening there?
- Uh, So basically, uh right now the inductive sensor
is only sensitive to the uh, to the uh,
conductive object, like uh, mostly the metallic objects
so basically, the when you flex the hand, this won't
trigger anything.
- [Audience Member] No effect
- Yeah
- [Audience Member] Great.
- [Host] Other questions?
Okay we got one more.
- [Clemon] Hi, clemon, University of Copenhagen
Uh, I was wondering how is it with our consumption?
would you have any estimates, how does it compare
to some of the related work you've planted?
- Uh so, so we are, we did measure the uh
power consumption of the system, I think.
I don't remember the number exactly,
is about 100 milli watts, but this is only because
we just use the off shelf, off the shelf the uh
inductive sensing trip.
So we did not do any optimizations on the power consumption
or any stuff.
So this sensing technique, can be definitely, uh
uh, optimized for the power consumptions. Yeah.
- [Host] Let's thank our speaker one more time.
(applause)