Hi, I'm Shiwali Mohan.
I'm a senior member of research staff at the Palto Alto Research Center
and today I'll be talking about our efforts on designing
an interactive AI coaching system that can help people in
developing behaviors like walking regularly.
This was work done in collaboration with Anusha Venkatakrishan
and Andrea Hartzler.
It appeared in the Transactions of Interactive Intelligent
Systems in 2020.
I am very grateful that we got this opportunity to present
this work at ACM IUI this year.
So the main motivation behind our work is figuring out how
we can use technology to support people in developing healthier
lifestyles and living a healthier life.
Unhealthy behaviors such as not exercising regularly or not.
Eating healthy foods are associated with several chronic
illnesses like diabetes and cardiovascular diseases
NSF and NIH back in 2014 designed this program called Smart and Connected
Health, in which they wanted to explore how technology can
be used to support people in developing healthy behaviors.
One of the most impactful ways to produce behavior change is
human to human coaching.
But this is extremely expensive, and not everyone can access
a nutrition coach or an exercise coach who could help them
figure out what are the right things for them to do so that
they can live a healthier lifestyle.
So our motivation was to use advances in mobile technology
and advances in AI and machine learning to then develop an
artificial intelligent coach that lives on your mobile phone.
And it supports you in living a healthier lifestyle.
And it's very exciting from an AI perspective because it
requires not just computational algorithmic parts, but
it also requires us to understand what is it that leads humans
to build better behaviors and how we can incorporate reasoning
about the models of human behavior into AI systems and
design an effective tool.
So the paper, it talks about two main contributions.
The first one is more computational.
How do we formulate this problem computationally?
And we contextualize our research in supporting overweight sedentary
individuals in regular exercise.
And we wanted people to exercise at what American Heart Association
would recommend 30 minutes of brisk walking at least five
times a week.
And our approach is based on our learnings from human to
human coaching scenarios.
The way our AI algorithm reasons is aligned with how an expert
exercise coach would reason about their trainee and the
way we have designed our models,
they adapt online as you record what you're doing every day
into your mobile phone app.
It actually changes, it's you know personal to you.
It really takes into account where your capabilities are,
and it's also temporal.
So as you grow in the program as you grow in your capability,
it changes your exercise schedule.
And we leveraged this theory called the goal setting theory
from cognitive behavioral psychology.
And this gives us some design guidelines on how we can think
about specifying goals for each individual trainee.
So they have two design guidelines, the first one is that
you have to set goals that are specific and proximal.
So, for example, instead of saying that, "hey, you have to walk more
this month", you actually have to be more specific.
You have to say, "hey, why don't you go for a walk tomorrow
for 20 minutes and walk at brisk speed?"
And because this is more specific, people can actually estimate
how much effort it would take to pursue that goal and they're
unlikely to postpone it.
Then having these more specific goals.
That AI system helps you define the builds then capability
of exercising regularly.
The second design recommendation that we follow is that you
have to set up goals that are difficult but attainable instead
of basically, if the goals are too easy, then people get
bored with them and they don't continue on with the program.
But if they're too hard, then people are likely to fail at
them and then not find enough motivation to pursue it.
So you have to walk this fine balance.
And our computational formulation is a proposal of how you
can find this fine balance.
And when we're trying to coach somebody.
So just very quickly, how does the system function?
On the right side, you are seeing the AI coach's beliefs about
what the trainee can or cannot do.
And we are using this quantity called exercise volume.
So this is how much the trainee can exercise per week, right.
So as we grow, we should see some increase in the trainee's
capability. So first, we designed an interface that asks
the trainee a few different questions, which helps us baseline
the training.
So how much exercise they can do even before they started on the
program? And all of this is just through asking them some
questions that baselines their behavior.
We already know from American Heart Association, we wanted
to get the trainee to be exercising at this level.
And now the goal of the AI coach is to slowly push the trainee
towards this goal.
Our paper goes into detail about how we can do that.
And basically what happens is based on certain heuristics,
the AI system will propose a set of goals for the trainee
to try in the first week.
So given these set three different proposals, training can
pick one of them, and then that helps affect the slope of
this function.
Like, how much can the trainee
grow in the first week, and then we make an assumption
that, well, whatever the trainee grows in the first week,
they are going to be growing in their areobic capability by
the same amount every week.
So we fit this uniform step staircase function.
And that's the first hypothesis that the agent is going to
be working with. The coach, then just uses some heuristic
planning/scheduling algorithms to then schedule the next
seven days of goals for the training.
So here we are saying that the coach wants the trainee to walk
on day two and day four.
And then the coach would also say, you have to walk for
10 minutes in that session.
The trainee can either say that they were successful in
doing that exercises or not.
If they were successful in doing the exercise, we ask them
how hard this exercise was for you.
And if people did not do this exercise, we asked them, why
didn't you do it?
Right.
So we gather more information about the trainee's performance,
and that really helps us revise this model.
this belief that the agent has.
And by revising these beliefs, we can slowly personalize
that training program to each trainee.
So if the trainee says that they were successful in exercise,
nothing really happens.
We just keep on moving along the same program.
If the trainee fails at doing an exercise that was assigned
for them, we try to increase the number of opportunities
they have to perform the exercise.
We really want them to get to the volume
they said they would get in that specific week.
But if the continuously fail, then that information suggests that,
Hey, maybe the goal was too hard for them.
And we want to revise and make the goal a little bit easier
to make this ramp a little bit easier.
And if the trainees were successful in doing all their exercises
that were recommended to them, and they said that those exercises
were easy, then we make this ramp a little bit harder.
So based on what the trainee is telling us, the agent either
make the exercise schedule easier or makes it harder.
And it's through these adaptations, this regression and progression
that the agent can really personalize the training schedule
for each individual training.
Right.
So the second contribution that our work that our paper talks
about is proposing this task-centric empirical evaluation.
And this evaluation is very interesting from the perspective
of evaluating AI systems in general.
Usually AI systems are designed to have these short-term
transactional behaviors and that's what they are measured
on. Right.
So a typical machine learning system would be measured on
its prediction accuracy.
But none of those evaluations actually help us tell if what
we have built is actually useful for the trainee or not.
And it actually produces behavior change.
So our paper talks about this four-pronged approach of evaluating
an interactive AI system, and it's actually task-focused.
So it's not just us asking trainees that, "hey, did you think
that the exercise that was proposed to you was good for you
or not?"
We actually measure how good those exercises were for them
and actually try to get to this more quantitative measure
of the efficacy of the health coach.
We have three different questions that we wanted to ask with
our AI evaluation.
First was that the AI system has long-term productive behavior.
The second one was that we designed these new interfaces.
So we wanted to know if a trainee could use this interface
to provide right information to the AI system, and then that it
actually matters.
Right.
The way we went about evaluating this was we first just tried to
see how if we simulated different trainees into the system
what their exercise trajectory is going to be.
And we find that the different trainees with different exercise
experience and different capability growth.
the coach actually does provide different personalized exercise
schedule. So that's good.
That means that the coach actually can adapt over a long
time. Then we showed these simulations to different experts, and
we try to see if experts would agree with these recommendations.
And our data tells us that, yes, experts tended to agree
with what the coach provides, and that gives us confidence
that, yes, we have not just invented any arbitrary algorithm,
but this algorithm, its reasoning is aligned with how an expert,
a physical therapist would think about coaching someone.
The third evaluation that we did was with, it was about the interface
and trying to see if this interface actually make sense to
people. And it seems like it does.
We have qualitative data that tells us that people thought it
gave people a whole lot of control over what kind of exercises
they had to do.
It helped them take responsibility.
It gave them a whole lot of choice and our collaborative
goal setting strategy actually helped people commit to a
specific goal that they could then pursue.
And then finally, we deployed this agent.
We conducted this ecological observational study with 21
participants, people who were managing diabetes.
So these are the relevant target population.
And these people installed this coach on their mobile phone
and then worked with it for six weeks and I am very excited to report
that people actually did manage to increase their exercise
volume. There were some concerns with people being over optimistic
with them during their self assessment.
But because we had designed this adaptive system that would
revise its hypothesis about the person based on what it was
observing about them, we were able to recover from those
errors to some extent.
And in general, people developed some capacity for regular
exercise. All of this is exciting, and I'm personally very
excited about this effort because this was not just a lab study
right.
We actually had people take this and just live their regular
lives. And we just observed how their behavior changed over
for the next six weeks.
And the results are very promising.
Thank you to my colleagues at Xerox PARC, Kaiser Permanente
and Amazon.
Just brief summary of this research.
We propose a novel computational algorithm for interactive
coaching. The main part of that algorithm is this parameterized
model that's aligned with how experts reason and it adapts
online as it observes more information about the training.
And then finally, the most exciting thing about this research
is the ecological observational study that's not done
in the lab, but with actual people living their real life.
And if you're interested about what else we are doing in health
coaching. We have another paper that's coming out this year,
and this one is more focused on the work that I presented
today is mostly focused on skill building or capability building
in this other work, we talk about other dimensions of health
coaching. Thank you.