- All righty then.
Hello.
I'm Rundong or Kevin Tian.
Today I'm presenting MyPart,
a Low Cost, Accurate, Personal Air Quality Monitor,
and this was worked on at UC Berkley with my collaborators,
Christine Dierk, Chris Myers, and also Eric Paulos.
All right, so to begin with, some motivation.
The World Health Organization estimates that
over 3.7 million premature deaths occurred in 2012
as the result of air pollution.
The primary pollutant really was particulate matter,
airborne particulate matter
of 10 microns or less in diameter,
which tend to get into your lungs and then cause
cardiovascular or respiratory diseases.
The issue with these is that
they're extremely harmful to human health,
but at the same time, it's not always easily perceptible
what the air particle pollution is like in your area.
All right, so to find out information
about air quality sensing, what can you do?
So one way is kind of the distributed approach.
In the US government, air quality maps
are provided by the US government
to tell you about the air quality in your region.
These maps are typically created using
highly expensive calibrated instruments
located in fixed locations,
and then they're coarsely distributed,
and then the maps are then generated
by interpolating between those stations.
So there's a few issues with that.
One issue is the granulator.
So air particulates can really differ
at a street to street level.
One issue is that these are outdoor sensors
and they don't really say anything about
the indoor quality,
which is where we spend most of our time.
And thirdly, it's not a really personal experience
in terms of the data collection or the device interaction.
Personal air quality sensors do exist,
and I would say that the three most important metrics
for these types of sensors
are cost, so how accessible are they to a wide audience?
Accuracy, how much can you trust the data
that's being given?
And also its portability,
so its ability to function in a variety of environments,
not necessarily just kinda hanging on your wall.
Okay, so to start off
at the kind of high end of the spectrum
are sensors like the MET 1.
They're highly expensive, highly calibrated,
and really intended to be used
in these clean room applications
to gauge how well your HVAC system is working.
They have good portability,
it does have an onboard battery,
but it is kind of this clumsy device
that weighs roughly two pounds.
There are sensors like the MET 1,
or there are sensors like the Dialus,
which is kind of middle of the pack
in terms of cost and accuracy,
but the portability is quite poor.
If you take the sensor out into sunlight,
the sunlight will read into the optical sensor
and it will really mess with the readings a lot.
There's also these low-cost sensors that exist
that are pretty inexpensive.
So $10 to $20, and also rather small.
Typically they're relatively inacurate
and the burden's kind of put on the designer
to then integrate this into some other device.
So what I'll be talking to you guys today
is MyPart, the sensor that we developed ourselves,
lacking the kind of existing solutions
in the current landscape of sensors.
The build materials was under $50.
We found that it's pretty accurate,
which we correlated against the sensor on the far left,
and the portability is also quite good.
The final size is 50 by 40 by 25 millimeters,
and that's roughly the size of kind of a large wristwatch.
Yeah, also we really informed
by this broad range of prior work,
some of that focused on on-body personal sensing,
and also work that's focused on
kind of these DIY toolkits
and my broader participation into high quality sensing.
And also this really wide range of products
that looked at how air quality sensing
can be incorporated within a wide range of form factors,
ranging from wearable T-shirts to carrier pigeons
and even to bicycles.
But the issue with these products
is that none measure particles.
So these other metrics are really important as well,
such as carbon monoxide, ozone, sulfur dioxide,
but the most important for human health
is really the airborne particulate matter.
And there's also been kind of this flurry of activity
within the startup Kickstarter landscape as well,
but some of those same problems exist.
Many use gas sensors,
many repackage existing low cost dust sensors
that weren't really accurate to begin with,
and also they're sold as these one size fits all solutions
and really restricts the customizability,
and I would argue it restricts the level of engagement
people have with this technology.
So kind of the four main metrics that we're trying to hit
when building the sensor is portability,
accuracy, accessibility in terms of the low cost components,
and the fabrication tools required,
and also its adaptability.
How modular is it to be incorporated
into a variety of form factors?
So I believe that when we do hit that sweet spot,
we kinda get, you know, we really,
by broader participation,
by creating a sensor that creates meaningful,
that creates usable, meaningful, and appropriate data
for differing context and communities
that want to use the sensor.
So in the remainder of the talk,
I'll talk about optical sensing principles in general,
and then I'll talk about our particular design
in terms of the mechanics, the electronics,
and the software that enabled this smaller form factor
but yet maintained the accuracy.
I'll talk about the interactions that we designed
with this sensor,
and with the user study.
So air particle detectors,
many of them look like this.
The main idea is that you have
some sort of focused light source
and also a photodiode.
You draw air in through the system
which contains particles,
as the particles intersect the light source,
the light is scattered,
and that light is captured by the photodiode,
and that signal is amplified
and then analyzed to detect
how many particles there are in the air.
So kind of the two issues with these sensors
is one, the size can be driven
by the size of the light source and photo diode,
so the distance between those two elements
and the optical elements in between.
And also because they're optical sensors,
not only does the photo diode
respond to the particles scattering the laser light,
they also respond to ambient light.
So to kind of adjust these issues,
we went through really just many, many generations
of these designs that yeah,
tried out different arrangements of the air channel,
of the laser and sensing,
of the fan and battery.
We kind of started with these initial laser cut prototypes
and then transitioned to these 3D printed prototypes
as we realized how complex the flow geometries needed to be.
And this is, of course, kind of the abridged history
of the design iterations,
and this is the real history for just the bottom row.
Yeah.
Okay, so diving deeper into the mechanical design,
here is an exploded view of the micro device.
For reference, that inlet port at the left
is the same inlet port that you see
at the bottom left of the exploded drawing.
So here I'll point out some of the most important features.
To make it as small as possible,
we've located the...
We've located the laser to be at the diagonal
of our rectangular device.
Now for ambient light rejection,
as you can see, the blue line kind of indicates
the path of air as it enters the system,
and as you can see, both the inlet and outlet
have these bends and turns
that prevent ambient light
from directly entering the system.
And so most of the, oops, sorry, skipped over that.
Most of these parts are 3D printed,
with the exception of the PCB, laser, fan, battery,
and I'll talk about the PCB next.
So in terms of the electronics design,
one of the main points is that we have
a high gain photodiode amplifier,
that's pretty sensitive to noise.
So to kind of deal with that,
we separated our digital and analog circuitries entirely
with separate voltage regulators.
So...
With separate voltage...
With separate voltage regulators.
So communally this actually lets us to turn off
the entire analog portion of our circuitry
when the device is not in use.
So to call out some of the integrated circuits
that we used for this device,
we used the rfduino, which is a micro controller
that has both the computing but also the bluetooth radio.
We have other chips for charging the battery,
for GPI expander,
LED drivers,
and more information can be found in our paper.
Great, so this is a typical signal trace
seen by the analog to digital converter
after it's gone through several stages of
amplification and filtering.
To count particles, we use a very basic scheme
to just pick a count threshold
above the noise floor.
More advanced techniques can certainly be used.
For example, those that consider the pulse width,
those that consider the signal on the frequency spectrum,
and also those that consider the signal
at various points of its amplification.
But we found that with this technique,
we can actually get fairly good results.
So in this particular test,
we wanted to see the performance between our sensor
and the MET 1 in a wide variety of locations,
both indoors and outdoors.
So on the Y axis, we have the counts from the MET 1 sensor
and on the X axis, we have the counts from our sensor,
and as you can see, we had fairly good correlation
between the two.
Okay, talking a little bit about the interaction design.
To initiate a reading with our device,
the user presses a button on the face of the sensor,
and the scattered light array on the face of the sensor
light up to indicate that a reading is being taken.
Once the reading is finished taking,
once the reading is finished,
the number of lights and the intensity of those lights
kind of indicate in an ambient way
the number of particles that device sees.
In addition, we've designed this to work with a mobile app,
so once it's finished sensing
it is also broadcasting that information over bluetooth
to the mobile phone.
And then I'll talk about some of the interactions
with the mobile phone app on the next slide.
So again we have this kind of...
We have this ambient display, again to kind of mirror
the, oops.
This ambient display that shows
the particles floating around in this playful manner.
We can also swipe down, I believe,
to get the particles to stack up into a,
into kind of this spirograph display,
and lastly, we have this historical view
where we can scan through the times
at which we've taken readings.
Okay.
So for our preliminary user study,
the main goal was to gauge the initial reactions
using our device, both in terms of the physical form factor
and as well as the mobile app.
And also we wanted to ask users about
their envisioned usage of this technology.
So the setup for the user study
was in total six participants,
three male, three female,
ranging from 18 to 59 years old,
and the procedure included an introductory interview,
a walkabout, and a follow up interview.
So I'll describe the walkabout next.
During the walkabout section,
we asked the users to go to eight locations
in an urban environment
that ranged from a variety
of indoor to outdoor environments.
And these environments were really chosen
to get them to think about the air quality
in relation to that particular place.
So these included places such as
a bus stop, a library, and also construction site.
So several themes emerged,
and the first one that I'll talk about
is a general lack of knowledge about air quality.
So consistent with previous studies,
most users were not really knowledgeable about air quality,
how it's measured, or how it's reported.
More specifically, there was definitely a mismatch
between the perceived and actual air quality.
So one user said, well,
"I usually think I smell something
when I think about air quality.
It's interesting to see the readings
with no way to detect otherwise,
and that odor makes no difference."
We also asked users to comment on the physical form factor.
So within the context of this 30 to 40 minute walkabout,
most users thought that it was pretty comfortable
and lightweight to wear.
However, we...
So we wanted to...
We did do...
Oh, sorry.
We engineered the device to be as small as possible,
but we did understand going into this user study
that it was still quite large
to be this wrist-worn form factor.
And as expected, we definitely saw some pushback,
or not necessarily pushback,
we did see the users who swung to that in the user study.
However, the idea behind choosing this particular location
was to really provoke users to think about
kind of these other form factors that could be
about on-body sensing or wearables.
So when asked about alternate form factors
that these users could imagine using,
a variety of objects came up
including bicycles, briefcases, phones, and also necklaces.
So one thing that points to is kind of this need
for customizable design.
Not necessarily one size fits all,
especially when people want to use the sensor
in a different variety of contexts.
Great, next I'll talk about engagement
and personal curiosity.
Though not required, many users took additional readings
while they were on this walk.
In particular, one user said,
when asked about where they would use it next,
"I live in the northwest part of the city,
and I'm curious about the factories down there.
The air quality definitely seems
different on different days."
In addition, many users...
Yeah, in addition, many users described
other places that they would take the sensor,
from places that they spent a lot of time at,
for example, their offices or their cars,
and also many users
showed a desire to kind of share the sensor
with other communities, for example,
sharing with a group of mothers who had children,
or sharing with, one particular user said
that she would want to share it with her grandfather,
who lived in India.
So in addition to the size, accuracy, and portability,
one central element of this design
is that it could be kind of
adapted to a variety of form factors.
So here in addition to the watch,
we designed these prototypes that are,
for example, the keychain, the backpack strap,
and also incorporated within a children's toy.
And I think that this is especially relevant
as digital fabrication becomes more commoditized
and personal production becomes more democratized.
So according to one statistic,
in 2016 the number of active or planned maker spaces
has grown to over 1400 in 2016.
And so one theme that we're seeing
is that more and more people will have the tools
and access to the tools that can make this device
completely from scratch.
So we really want to tap into this rich ecosystem,
and all of our design files will be released open source.
And the idea is not only to invite broader participation
into the use of these sensors,
but also into the design, the customization,
and the co-authorship of the sensor
for specific people and contexts.
All right, thank you very much.
(applause)
- [Voiceover] Hi, I'm from Delhi.
I have a question about the sensitivity correlation
that you showed for the correlation between
the high sensitivity, highly sensitive MET
and the other one with the pack?
- Yeah?
- [Voiceover] Was it for PM 2.5 or PM 10,
and how that slope varies when you have
different diameters of that particle emitter?
- Right, absolutely, that's a great question.
Oops, sorry.
Let me find that slide really fast.
Yeah, so this...
So right.
This, the particles that we're seeing
were simply basically choosing a threshold
above the noise floor.
To really get at...
To really calibrate that sensitivity well,
what you would have to do is
get basically a test chamber
and then release particles of exactly known distribution.
- [Voiceover] So this is kind of
an open ambient environment kind of test?
- Right, so this is...
And that was kind of the purpose of this test,
to test it in these ambient environments
that range both from these indoor and outdoor locations
to see if the light leakage was an issue
or to see if different sunlight conditions were an issue.
- [Voiceover] And have you tried any sensitivity analysis
on the sensing part?
Have you tried any sensitivity analysis
for the sensing elements?
Like, how good is the temperature variations and...
- That we haven't examined.
That would definitely be future work, yeah.
- [Voiceover] Thank you.
- Thank you.
- [Voiceover] Hi, I was wondering
if you plan to open source the design,
and what's the battery life?
- Could you repeat the question?
I'm sorry.
- [Voiceover] Yeah, sorry,
how about making it open source?
And what is the battery life?
- Sorry.
- [Voiceover] Battery?
- Oh, the battery life, great.
Yeah, we definitely want to make this device open source.
The current battery in this particular form factor
is 400 milliamp hours.
We haven't done...
Yeah, I think the 400 milliamp hour battery
roughly lasts a few hundred sampling cycles,
and then in between we drop everything,
microcontroller stall, we differentize these
into a low power sleep mode.
But as far as, the other point is that
it is a totally open source design.
The battery compartment was designed specifically
to fit underneath at the very bottom of the device
so that you could imagine having that be
inside a trapdoor that drops into
a bigger battery compartment where you can put in
a much larger battery for these data logging applications.
- [Voiceover] Sorry, so, with the one that you showed,
like, how often do you have to recharge it?
Like every day, every two days, a week?
- I would say probably on the order of every day.
- [Voiceover] Okay.
- Yeah.
- [Voiceover] Thanks very much.
Thank you for presenting.
(applause)
- [Voiceover] I have a question.
Are you guys keeping the data and planning to,
I guess correlate or have that available
based on location so people can see how
are tracking the air quality?
- You mean once we have the data onto like the mobile phone,
what are the different ways that we can use it?
- [Voiceover] Yeah, or what happens to the data, I guess.
- Currently, it's designed live kind of...
It gets offloaded onto the mobile app, where it's saved,
but we can definitely envision other applications
where this, yeah, is broadcast to a wider network of sensors
where people can see the, yeah,
sensors from other people and from other places.
All right, thanks.
(applause)