>> Mary Czerwinski: Anne is the co-principal investigator and director of the
BodyTrack project which she started around 2010, and she's here to talk to you
about today. She's from Carnegie Mellon University, the Create Lab, correct?
>> Anne Wright: Correct.
>> Mary Czerwinski: Welcome, Anne.
>> Anne Wright: Thank you. Okay. And we have a little extra introductory treat
here from Gordon.
>>: I met Anne at the Quantified Self Annual Meeting at Stanford last month and
through a doctor, Jack Abramson, who has a project with a number of trackees
who are using BodyTrack to capture health data to understand cause and effect.
I'm a trackee collecting symptoms and body characteristics for someone who's
had two heart attacks, two bypasses and two pacemakers with the goal of
extending my own end-of-life.
Anne also seeks synergistic efforts such as Quantified Self and friendly doctor.
Perhaps there could be something synergistic with MSR. So here's Anne, who
may be using my recent data, that includes sleep, lack of exercise and pain when
I do exercise so now all there is left is to analyze.
>> Anne Wright: Okay. Now, to tell you a bit of my own story. Let me get my
thing going here. So as she said, I'm a robotics systems engineering.
I worked at NASA Ames Research Center as a lead systems engineer for
prototype Mars rovers for many years. This is Marsacad and Canine [phonetic].
And that was really great job. I really enjoyed it. Then I got too sick to do it
anymore.
And this is a real disappointment. And it was all the sort of vague stuff that
messes with your ability to do what you care about but comes up negative on all
the tests.
And after pushing things as hard as I could and realizing that I'm not going to get
really a useful answer out of the standard medical system except for what it's not,
but nothing sort of useful about how to run my life better to be able to suffer less,
I said, well, okay, I know how to debug one off systems I'm a one-off system,
what can I do here.
This was before the quantitative self movement started and actually several
years before I actually found out about it. But did a lot of the same sort of things.
Found trackers that I could -- like the heart rate straps and started taking
mixtures of food and recording what I was eating and things like that.
And I did eventually come up with an answer that allowed me to feel much, much
better most of the time. But in the process I was really, really frustrated with the
tools.
And in the end, the answer I found -- actually the most useful thing was taking
the pictures of the food. Because it did turn out to be a food issue.
Luckily, when I kind of hit the bottom of the barrel, the doctor sent me to Iarvada
[phonetic], it's about imbalance, Iarvada is thousand years Indian science, go
figure out what they say about body imbalance. And for paying attention to what
I was eating -- when I was at home cooking the way they said, I would get better.
Then I would go out to dinner and just about anything I went out and ate, no
matter how hard I tried, it would be a problem.
And I would pay attention to the few that succeeded and many that failed and
tried to find a pattern. It seemed to be an issue with night shades: Tomatoes,
potatoes, eggplants, peppers, stuff in that family. Turned out to have neurotoxins
in them. Apparently some people have problems with them. Obscure knowledge
that no one is ever going to tell you about, but there's a lot of people who have
figured it out experimentally. They write really, really crappy books.
Maybe some day somebody who can write well will write such a book. But in the
process, basically I said, well, this goes against everything that I thought about
the way the world worked. Everything I've heard is vegetables are good for you,
these are vegetables therefore they're good for you.
Some how my experience is different. Are there other examples of people who
have had these similar sort of experiences where they, through paying attention
and experimenting, have figured out that there's something about the way they
react to the world that is significantly enough different than the way they expect
based on what they've been told that they really do a whole lot better if they
come up with sort of custom strategies, their own user's manual.
There's examples in all sorts of ways. And people sort of know about the typical
peanut and pollen allergies people are learning about celiac disease but it's the
tip of the iceberg. Weird stuff.
The weirdest was my boss at NASA Ames is allergic to talc. He had a problem
where he would itch uncontrollably whenever he was in a warm room for like ten
years. Turns out he was dosing himself with talc in his supplements.
We figured that out, figured out it's in rice and all these other things. Talc an
unlisted ingredient. Now he's fine.
And he may be unique in the universe, but he figured it out. So how do we take
the examples of people like me, like him and like lots of others, who have gone
through this process, and figure out how to make that opportunity available to
more people who are kind of suffering in these kind of obscure ways that may be
amenable to this sort of approach.
And in going through and looking at what people were doing, it really looks like a
feedback loop where you observe, capture, reflect and adjust.
So you pay attention to what's going on. You capture it. It may just be in your
memory or on paper or may be electronically. You reflect on what was going on
in the context around various experiences when you either did well or did poorly
and see if there's anything you can learn.
And then you adjust your approach based on what you've learned. And so we
looked at that. He said, okay, what can we do here? And how can we
contribute? How can we make this less of a burdensome process?
And what we realized is there's sort of this capture and reflect piece in the middle
is really the piece that we can help with.
So this was around 2009 that I started saying, okay, the tools suck. We need
better tools, what can we do? And this is conveniently enough when self-tracking
devices started coming out.
So these are the Zeo sleep monitor, Body Media armband, the witting scale
[phonetic] and the activity monitor. And they all came out within a year or so of
each other around that time. And then there's also active tracking devices
because there's things like you can -- these guys can tell you certain kinds of
things can't tell you other kinds of things. They can't tell you whether or not
you're in pain. They can't tell you what your mood is.
And so we started looking around for people we could partner with who had a
good handle on this and the best we found was some folks doing an app called
Mymee that allows you to do observation capture. And I'll talk about that more
later. And then things taking pictures of things, this is powerful especially in
dealing with food.
There's lots of other contextual data sources that may or may not be directly
related to whatever symptom or experience that is of concern to you, but that
allows you to reflect more deeply because it gives you more information about
what the context was at the time.
You can never get all of the data into the computer. But what our hope is that
you get enough data into the computer that the person can look at that point in
time and sort of figure out where they were, what was going on in a way that can
sort of access that internal memories of that in ways that just sort of looking at
the data on the calendar you can't.
And so we started building Web tools, started a project called BodyTrack.
Started building Web tools. In the last year we merged with another open source
project called Fluxtream that was doing similar sorts of things.
And the idea is that basically we find data sources that contribute in useful ways
to this process for people. And we figured out how to aggregate all the data,
timestamp data, put them together in the same system.
And here's an example from the first version. And in all these cases you really
want to kind of understand both your personal context and also kind of
relationships between things.
And we built a little environmental base station that talks to an air particulate
monitor. And so the green and the black are air particulate readings, small and
large, and then we've got humidity, light and photos. And we just wanted to sort
of poke around and see what goes on with air particulates. It's not something
that you can see but it's potentially interesting. My husband has asthma, so he's
very interested to look into this.
And just what jumps out at you is there's these spikes with these slow decays.
And so these decays are like 12-hour decays on these spikes. What are they?
So when we start actually going and poking around and exploring, you can see
that each of these really big spikes has something in common, which is these are
all places where I was cooking a meal using a particular cast iron grill pan. Now
the meal's using the crock pot, using the skillet, those didn't have these spikes
but this one particular pan did.
And that's something we didn't know. And when we realized that, we stopped
using that pan. Started using the cast iron skillet instead. And we don't have that
happening to that degree.
It's also interesting in that the folks that we've worked with this, they all see the
same thing you see these caused by personal activity. A lot of times it's
interesting for things when you can tell when you're cooking when it's not
ridiculous like that. You can tell when you're cooking versus going out.
That can tell you, for instance, how well you're doing. You cook more at home
and you're doing well, if you're not. A lot of interesting information to be gathered
from stuff like this, and having an interface where you can just go and explore it
and see what there is and understand the shape is really important.
This is an example with Body Media and Zeo data. So this is Body Media
activity. So number of the -- the multiplier on basal metabolic rate. These spikes
are going out and walking and running, and this is whether or not you're lying
down.
And then this, the Zeo -- orange is awake. The greens are deep and light sleep
and the gray is -- I'm sorry. Deep and REM sleep and the gray is light sleep.
You can sort of -- this is kind of an example of using the exploration for just kind
of remembering what things were like. So here I took a nap. Here it was a pretty
good night of sleep. Pretty active that day. Here I'm up. What's going on in the
middle of the night there?
Ha, I got up and read e-mail, walk into the computer and walking back, no big
deal. Oh man, that's a mess, what's going on here. Looks like I'm going for a
run here in the middle of the night. Turns out that's waking up and being very,
very sick.
And it turns out that I assume because of GSR being very, very sick looks like
you're running around. And so this is now useful, because this sort of thing
happens to me sometimes. It's happened to me sometimes since high school.
Now it's easy to find them because I know what they look like.
Here's an example using the newest system after combining with Fluxtream and
here we have some additional sort of interactivity. It's easier to add channels,
select from all the available ones. You can modify the channel height. You can
modify the channel settings.
You can go through and explore. And this is an example for my own data so that
the main issue I still struggle with is happy versus unhappy guts. And four is
happy guts. And the higher readings are unhappy.
So this is kind of an example of starting from some sort of symptom, and then
trying to understand what the context is. So here this looks really bad, what was
going on then?
And so in the new system we actually have additional views like a map view
where I can see, well, this is right after I got back from a trip to Boston and I went
out to dinner with some friends from out of town. What did I eat?
Okay. Pineapple, I almost never eat pineapple. That was desperation getting
back in the middle of the night and that was all I could find at the Wal-Mart. And
here's some red wine with the Brookses. And I was dabbling with red wine just
then and in Belgium I generally don't do that. Maybe it's not a good idea. I think
the red wine experiment is done.
And what we found sort of going through this and building the tools and trying to
figure out how this is going to contribute is that the tools are important but alone
they're just not enough. And sort of the typical story that you hear from this is
they're not enough because people are stupid.
We don't think that. We think very, very much differently than that. What we
think is that passing on culture is complex. And really what we're talking about
here are cultural practices and developing and passing on cultural practices.
And just like we don't expect somebody to learn how to cook by going to the
store, buying a knife and a pan and reading the user's manual. That's kind of
wherefore some reason that's where we're generally at in the tone of the
discussions about self-tracking right now.
And we know with things like cooking and fishing and stuff that that's not how it
works. You have somebody who knows what cooking is like or somebody who
knows what fishing is like and you hang out with them for a while, right?
And so we think that that was sort of -- that's sort of a big missing piece. Also,
revising personal narratives is an interactive process. And the best person I think
I've seen who explains this is Rachel Naomi Raymond in Kitchen Table Wisdom,
she talks about the process of interacting with other people, telling them our
stories, we revise stories and come up with new ones.
It's really this sort of process -- you can't do very well in your head. And having
the tools helps, but the real thing that we're trying to support is this sort of
process of self-narrative editing and it works much better with the second human.
It doesn't have to be a trained human just has to be a human who puts up with
you and cares about you. So we came up with this, me and Dr. Abramson, came
up with this idea of quant coach, someone who knows the shape of the space,
has kind of done it before, knows what the tools do, who works with somebody
who has these sort of issues and doesn't know how to use the tools yet, to kind
of do skill development and help translate.
And having somebody who kind of knows what things do to help select and
configure and use the tools just makes a huge, huge difference. Because when
you're by yourself, a lot of these sort of speed bumps you hit along the way
where you don't know what to expect, somebody who does know what to expect,
can just help you over them it's no big deal.
People can really get stuck and not be able to get back to it. And reflecting on
the data together and looking at specific incidents I think is really, really useful.
When you go and you talk to your doctor, you're really not usually able to go to
the level of detail of looking at specific incidents. What you're talking about is
your current edit on your story. What is it that you currently believe?
But it doesn't really give you the ability to see the context and to sort of challenge
your current set of stories and maybe to come up with some that fit the data
better.
Give you a couple of examples here. So one of the -- we've done a number of
pilots with this. I've done two -- my lab director's done one and the Mymee folks
in New York are kind of doing an ongoing pilot.
Marcy was one of the participants in the first pilot we did in August in California in
preparation for the Medicine 2.0 conference. And her biggest issue was sleep
disruption. So the orange is awake.
>>: Then she went to Burning Man.
>> Anne Wright: Then she went to Burning Man. Orange is awake. This is
August. And she had a Zeo sense about May and pretty much every night
looked like this. I'm not kidding you. Just waking up constantly and
understandably concerned about that.
And her number one theory as to what was going on is her and her partner were
waking each other up. Which leads to some relationship stress and finger
pointing and it wasn't so good.
Actually worked with both of them. And doing the quant coaching thing, and it
turns out when they actually looked at the data synchronized together, they saw
that that's not what was happening. She had some other ideas about food and
stuff, caffeine that just didn't really pan out either.
Then she went to Burning Man. And they had a ritual where you take your
burdens. You symbolically put them on a pile and burn them. Here we have
perfect uninterrupted nights of sleep. She ended up having perfect uninterrupted
nights of sleep. When she came back to talk to me she was like, oh, I think I
figured it out.
The new story based on this sort of surprise, serendipitous event, is that maybe it
had something to do with some low level vigilance response that she wasn't
really aware of or able to access, that going through this ritual had accessed for
her.
That wasn't on her initial list at all. But it's working well for her. Mike has sleep
paralysis. So sleep paralysis is where sort of the opposite of sleep walking. You
become aware but your body is still paralyzed.
And it's pretty disturbing. It's been happening all his life. There's really nothing
the doctors can do about it. There's no drugs. No treatments. And so he didn't
really have any stories, any theories going other than sucks to be me.
And so we started sent him over for Zeo and Mymee and he started tracking.
And within the first week, actually, he had the first sleep paralysis episode.
Yeah?
>>: Monitor your sleep states.
>> Anne Wright: It has an EEG. It's a little band EEG, wireless band and they
do classification on the base unit.
And so actually got e-mail at three something a.m. from Mike saying I had a
sleep paralysis episode. It was the first time I was happy about it.
So that was kind of funny. And sort of points out how changing your role can
actually change your experience, even when you're still having the thing happen
to you.
And so then it happened a second time before we met. And we looked at this
together. And from this he came up with an idea, which is an idea he hadn't
thought of before, which is these are the nights you went to sleep the night the
latest, when his schedule was sort of most disrupted.
He said maybe it has something to do with circadian rhythm disruption, which he
hadn't thought of before. So he started working on how to reliably get to bed at
1:00 a.m. And that's something we've been working on since. And his
experiences since have actually reinforced this idea of circadian rhythm
disruption. When he goes to Haiti, getting up with the sunrise, it doesn't happen
to him. When he flies to Italy, is in a hotel and many hours different, it happens.
And there's something he can do about it. And he's actively working on it. He's
getting better. Not 100 percent. He's a Ph.D. student.
Alan is an engineer in the Bay Area, works for Fitbit. He's been a type two
diabetic for 22 years. He had two stint operations. His HBA1C was very high.
He felt like he was doing everything he was supposed to do and it just wasn't
working.
He begged his doctor for a continuous glucose monitor. And so the green line
here is dex com 7, continuous glucose monitor, sent to BodyTrack via upload
API. And in the beginning it's out of range a lot.
And he actually, in paying attention to what happened when he would eat
something and you'd kind of watch the trending data and go out for a walk, watch
the trending data.
He ended up coming up with strategies that were not the strategies his doctor
told him that worked for him. And that as time got on, he got better and better at
keeping in between the lines.
And at this point basically he's got normal HBA1Cs and his weight's way down.
>>: Glucose monitor work because you have to sample blood, right.
>> Anne Wright: Interstitial fluid actually. There's a slight delay but it measures
the interstitial fluid. You have a little needle in your tummy that wirelessly
transmits to a little pod and it shows you a little graph and gives you the trending
information in real time, and you have to do finger sticks about like four times a
day to keep it in calibration.
But he said he's much happier doing the finger sticks keeping it in calibration and
getting the data from it than he was -- he felt like doing the finger sticks alone at
the wasn't getting the kind of insights he needed.
I'll give you an example here. The green line is the deck com data and this is the
Fitbit data. One thing he noticed if he got out of bounds went for one to two mile
walk, over hours it would come down but with transience.
So depending on when you stick your finger, you're going to get very, very
different answers here and it's going to give you a very different idea of what the
effect of this is. But when you have the continuous data and can see it over the
long run, you can see these trends.
And that really helped him to change his idea of how to act and how to
self-regulate. So talking a little more detail about the process rather than just
kind of the, what people found, so this is Gordon. Gordon's been going for a
couple of weeks now.
He talked to Dr. Abramson and then talked to me before going down to Australia
where he now is. And so when he talked to Dr. Abramson, he set him up with a
Mymee install. Those are all custom. And those who have been doing quant
coaching with Mymee, one of the first things we do as part of the first meeting is
talk to the person about what they want to capture, what they think is relevant
both in terms of kind of symptom or kind of experiential kinds of things they want
to capture and also contextual things they might think is relevant that they want
to capture and they can change over time.
This is what he has initially. So angina and shortness of breath are sort of his
main sort of symptoms that he wants to track, and that are a cause of concern to
him.
And the exercise. So swimming, rowing, walking. He wants to be able to see
what effect that has or doesn't have on that. And then HRV and food are things
that he thinks are potentially relevant context.
HRV is heart rate variability. It's a measure of sympathetic versus
parasympathetic activation. Fight or flight activation calm. So what happens
when he experiences one of these things, when he experiences the angina pain,
is that you hit the button, and then you can put in a value, which optional value,
optional comments, optional photo. And then all of those get wrapped up and
automatically pushed to the cloud server and then we can pull them from there.
Also suggested to him getting a body -- new Body Media armband, and he's got
a heart rate strap he's been using with a SweetWater HRV app which does not
have an API. So that's why he's doing it on Mymee. And then he's got
BodyTrack account.
The top two here come from the Body Media armband, the same ones I showed
you before activity -- this one is sleep rather than lying down. But basically lying
down with some noise.
And then HRV, he's recording from the SweetWater and these are observations
from the various Mymee buttons. And he's taking pictures of his food so he can
go through and see at various points in time what he's eating.
And so now that he's got the data he can go in and he can reflect on it. This is
sort of an example of going through and doing that. Where here is a particular
point in time where he had some angina pain. Had some shortness of breath.
And this is interesting in that he wasn't really doing much when this happened.
So what's up with that?
Interestingly, the HRV was incredibly low. So which means that sympathetic
nervous system activation. So maybe that has something to do with it, I don't
know. It's an interesting data point.
And then here he's actually quite active, and the pain's gone. Little shortness of
breath. So it's not as simple as the more active you are, the more it hurts. Okay.
So we've got more we need to learn and he knows that.
So let's go and look at more data later on. Here's a time when he had some pain
after one of these sort of big activity spikes: Says walked to pool in 90-degree
heat, no past exercise, question mark. No pain on long walk to flight. I think
that's supposed to be shortness of breath.
And so here sort of his thinking about what's going on has evolved a bit. This is
walking to the pool after he hadn't exercised for a couple of days. Now he's like,
okay, he's seen examples where he exercises and he's okay. And so maybe it's
because he wasn't regular enough.
So now he's got that story. And the consequence of that story he's probably
going to try harder to be more regular about it. This is the sort of thing that
happens.
You don't know when you're early in the process like this where you're going to
end up. But at each step along the way he's got something positive he can do
when this happens rather than just, no, it sucks to be me.
And he can kind of go through and evolve these stories to fit what he sees as he
goes along.
So one of the questions that keeps cropping up is how can we incorporate
medical data. And medical data in quotes. And what I mean by medical data is
that there's sort of this big dividing line between everything I've been showing
you, which kind of falls in nonmedical bin, which you can do whatever the hell
you want with, and the medical data over there, which essentially means that it
originates within the medical system. It's the kind of stuff that the insurance
company perks up about.
And there's all sorts of data over here that's potentially useful and there's the
question of how to leap the gap and which direction should the gap be left.
There's an interesting example of someone who like me is kind of involved in the
quantitative self movement named Larry Smarr doing frequent lab testing and
has had some interesting experiences and some interesting results.
If you're interested in this he's got good presentations online. He also presented
at the QS Conference. So here he's been looking at CRP which is an
inflammation marker and lactoferrin, a specific marker, and by tracking that over
time -- and he didn't really have an agenda when he started. It was just like, oh,
lab tests.
But he noticed that these things would spike at various times and tried to
understand it. And eventually he and his doctors realized that actually these are
bowel inflammation episodes. And having that tracking data and being able to
see how did it change over time, what's the lead-up to it.
Before they realized anything was going wrong, it started going up. And you see
interesting articles about things like this, about it's like well six months before
getting diagnosed, the inflammation markers were going up, that sort of thing.
So you can imagine that this kind of data could be really interesting for people
going through this sort of process to be able to combine it with all of the other
experiential data and what am I eating and how am I exercising and stuff.
So here's for me the current situation, which is very non-explorable. I've got
half-inch stack of pieces of paper of just lab tests. These are not even sort of
stuff. It's just the lab tests.
And being able to combine this with the rest of the tracking data is -- in that form
is not so doable. So how about importing into HealthVault. Yeah, HealthVault. I
have lots of lab test results in here now.
This is looking up. Well, it's digital now. It's secure. But it's still not explorable.
If you look at the lab tests, what you get is the name of it and the date. And then
you actually have to do a drop-down in order to see what the value is.
And if you do this, this very juicy looking little export thing up here, the CSV
doesn't have values in it either.
So you know it's a step in the right direction, but it's not yet as far as we need to
go. And one of the main things that we need is that we need to be able to
support comparison over time. And actually in my just sort of individual poking
around, the paper still won. The HealthVault was very useful to be able to do the
search on the name of the test and be able to see the dates but actually going
through my date-ordered pieces of paper and folding them up and putting them
next to each other was still much more usable to be able to see the trend than
looking at it online in that form.
But there's an interesting example of a place where one of the API clients has
made a specialized little tool that only for these particular fields HDL total
cholesterol, et cetera, but you can see the trending data over time. And so this is
cool. This is definitely in the right direction.
So can we make this more interactive and combine it more fluidly with other stuff.
And my current hope for that and part of the reason I jumped at it when Gordon
said you should go talk to the guys at Microsoft Research is the idea actually
HealthVault acting as a bridge between these worlds and being able to, you
know, if we just made this sort of API client piece, being able to incorporate that
kind of data together with the other stuff in BodyTrack.
And probably lab test results would be the first thing that would be potentially
useful. And being able to sort of play with how to deal with that rather than trying
to deal with it directly.
So I think the HealthVault is really playing a unique role in the ecosystem right
now in that it is the only thing I've been able to find that you can just haul off and
make an account on that is working on bridging this gap.
And so I think that if we can sort of combine forces and take some of the
explorability tools that we've been working on in the usage models for that and
being able to also then leverage the HealthVault data, I think that will be
awesome.
So hopefully while I'm here I'll be able to kind of find out who are the right people
to talk to as part of that vision, as well as other folks in other departments.
So question?
[applause]
>>: For folks like yourself who are trying to solve the mystery, can you be a little
more explicit about what the practice is like today of doing hypothesis formation
and then hypothesis testing?
>> Anne Wright: You mean in the standard sort of medical model or in the quant
coaching model that we've been working with?
>>: For the quant folks, so given the nature of the demos you showed us, it
looked like hypothesis formation is almost sort of like a visualization task. You're
just sort of browsing your data and ->> Anne Wright: I think it's a task that the visualization is part of the loop. But I
think that the question of how do I -- what story might better fit, what hypothesis
might better fit what I'm looking at is something that naturally comes out of the
process of going and looking at specific incidents. And talking about them with
somebody.
What was going on.
Well, I got up in the morning. I went for a run. I went to the museum, blah, blah,
blah. And you look at a few of those. And it's like you know I've noticed that
whatever it is that you've noticed, maybe that's relevant. Let me go look at other
incidents.
That kind of exploratory process is the best thing that I've been able to find for
being able to do this sort of hypothesis generation and testing.
And one of the really important things about that is because it comes from the
individual's musings, one, you have access to a source of data that nobody else
has access to, which is your own sort of personal memories that can be evoked
through that process.
And, two, it makes it so that the person whose life it is is empowered and has
agency. And I think that that's really important. A lot of people are working on
tools that basically do hypothesis generation in some machine learning kind of
sense, and then feed it to the individual.
But there can be sort of a gap between that and the things that they are
interested in thinking about, interested in exploring, and that might actually be
something that will resonate with them.
And so I think that by having the person themselves be the generator of that, I
think there's something important about that. And in not being able to sort of
further kind of disempowered by the process.
Yes?
>>: On the other side of the fence, more objective, less objective, what devices,
hardware, do you think that you're missing now that would be really helpful?
>> Anne Wright: Good question. So the continuous HRV, I think, is sort of the
next frontier that we all have hopes for. And we actually have a bead on that
right now, which is that the polar HR-7, which I have a picture of here, here we
go. So this little guy here is bluetooth smart and it gives you R to R intervals
which look pretty dang clean.
So the first step in the process of doing heart rate variability is to have really,
really dangling unfiltered R to R intervals. And this little guy now does this.
So my husband just in the last week has gotten an up loader working for that that
works on -- so right now it's iPhone 4-S. And 5 and iPad 3 are the sort of portable
things that we know have a usable developable bluetooth smart stack. The
androids aren't there yet, unfortunately. The ones that have the hardware still
don't have the software.
>>: [inaudible]?
>> Anne Wright: Well, depends on your usage. As part of a bio feedback thing
it's important to get real time. For our purposes, real time isn't as important as
having the accurate time stamps. You want to really, really know when that
happened in a very precise sort of fashion so you can combine it with other
things.
>>: [inaudible] poll a month worth of data like you're talking about with less power
and [inaudible].
>> Anne Wright: As long as it has a real time clock that it can sync up with so it
doesn't have too much drift. If you look at the bay station, for instance, that's
what we do with that.
But the person who did the base station looked into it, and he said that the
bluetooth smart reception devices aren't there yet. You have to build your own
stack. So it's not quite there. But hopefully it will get there.
>>: For managing stress, what are the trade-offs between using HRV and GSR?
>> Anne Wright: So nobody really knows. I mean, there are pluses and minuses
of either one. Both kind of in the time domain, how quickly they respond. And in
terms of false parts of the signal.
And really you want both. BodyMedia armband natively gets GSR. They will not
give it to you via the commercial thing. You can get at it with the research band,
but the usability of the research band is unfortunately complete disaster.
I mean, I have one. It's such a pain in the butt to use that I don't. So there's
something called the Q Sensor that does continuous GSR. For $2,000 a pop.
But I'm just not that motivated to deal with it given that five people I know would
be willing to buy one and it's not going to have a bigger impact. This thing has a
much more appropriate price point.
If there were a GSR sensor that was sort of comparably priced and usable,
please let me know about it. I haven't seen it.
>>: So you've developed an API with BodyTrack that's kind of geared toward the
real time data collection and is that kind of extensible to new devices?
>> Anne Wright: Yes, it's actually geared towards aggregating data with other
data sources. So you have the timestamp and you have the device name and
the channel name, and it's a sort of a patch JSON sort of format with double
floating point Unix time timestamps in it. And it's pretty simple to use it. And
that's what we use for like, for instance, the dex com 7 data is just being -- the
Perl script that is just called the HTP post.
And so the new system has that as well. And so hopefully as these things
become available, we do our own little things, but hopefully the ability for this will
expand into the environment. And there's already several people who know how
to do this and have been doing it for their own little experiments. But hopefully it
will grow.
>>: Other questions?
>> Mary Czerwinski: All right. Let's thank our speaker again. [applause]