Hi everyone, my name is Dishita Turakhia
and I am PhD student at MIT CSAIL with Prof. Stefanie Mueller,
and I am very excited today to talk about the work my co-authors and I did
on studying if physical tools that adapt their shape
based on a learner’s performance can help in motor skill training.
Currently, there are several shape-changing tools
that use actuation to support the motor task execution
For example, this actuated desk and monitor system that moves up and down,
to change its height and orientation
to support sitting in correct posture
or this actuated knife that protects the finger
by locking the blade it gets too close to it
While these shape changing tools use actuation
to ensure correct motor task execution,
they do not help in mastering it.
In our work, we study if shape-changing tools
can also be used for motor skill training
In particular, we study if shape change,
that adapts the task difficulty
based on learner's performance, can be used for motor skill training.
Take for example in this adaptive basketball stand that we built,
which changes the hoop height
and width based on the learner's performance.
So as the learner gets better at the task,
the tool adapts to increase the difficulty.
And we study if such a tool helps in training of motor skills,
for example, scoring of baskets.
This approach of adapting task difficulty
is already widely used in online learning systems,
for example these tools for learning math
that change the difficulty of the math problem
based on the learner’s skill level,
so that every learner can learn at their own pace.
A key benefit of these tools
is that they make personalized learning
accessible to a broader audience
But what about making the learning of motor skills
personalized and accessible in the same way?
When it comes to motor skills,
we are still limited to in-person coaching,
where it is the trainer,
who adapts the task difficulty for the learner
So we asked ourselves, how can we build a physical version
of personal learning tool
that automatically adapts the task difficulty
for the learner during training?
If we see the related work in tools for learning motor skills,
most systems rely on giving
multimodal feedback on learner’s performance.
For example, this system that gives
haptic feedback to the learner on violin playing,
or systems that give visual and tactile feedback
on body posture for learning snowboarding and archery.
Where our approach differs from this work,
is that we focus on adapting the task difficulty.
And to adapt the task difficulty,
we propose using shape change.
And we were interested in studying if this combination,
of shape change and adapting task difficulty,
is useful in the training of motor skills.
To study this, we first built a shape changing tool,
and deployed an algorithm for adapting the task difficulty
based on the learner’s performance during their training.
We then conducted two user studies
to test the learning gains in training with our auto-adaptive tool
against training with conventional methods
like static or manually adaptive tools.
Let us first start with
the shape-changing tool and the motor skill
As mentioned earlier,
we selected the motor skill of
throwing a basketball in the hoop
and built an adaptive basketball stand
that adjusts its hoop width
and hoop height
as the learner improves in their skill.
For measuring the learner’s performance,
we mounted sensors on the tool.
For example, to detect when a ball goes inside the basket,
we mounted a switch sensor
And to detect when the ball hits the board,
we mounted a piezo sensor
To trigger the shape change during adaptation of task difficulty,
we used actuators.
For example, we used a stepper motor to adapt the hoop height
And a servo motor to adapt the hoop width
The tool works in 3 conditions
static, where the difficulty remains constant during the training,
manually adaptive – where the task difficulty
can be adjusted by the learner anytime during the training
and auto-adaptive – where the task difficulty
is adapted by our algorithm based on the learner’s performance.
Now that we have a shape-changing tool
designed and built,
let us move to deploying an adaptive algorithm
that decides when to adapt the task difficulty
And before we delve deeper,
let us familiarize ourselves with two important terms,
often used in the field of motor skill learning
nominal task difficulty and functional task difficulty.
Nominal task difficulty level is basically
the universal difficulty levels
for example – beginner, intermediate or expert.
And these difficulty levels are invariant to the learner’s skill level.
In our example, low nominal level would be the easiest setting
with lowest hoop height and widest hoop width.
And high nominal difficulty level would be hardest setting
with the smallest hoop width and the lowest hoop height.
However, functional task difficulty
is subjective to the learner’s skill level.
For example, a certain task might be
more difficult for a novice or beginner level learner
and hence functional task difficulty
experienced by the learner would be
much higher compared to an expert
for whom the same level might be at
a low functional task difficulty.
And our goal is to take learners
from a beginner level to an expert level
ie. low nominal to high nominal difficulty,
by maintaining the functional task difficulty optimal,
i.e. neither too easy nor too difficult
And we do this because studies have shown
that learning with optimal functional task difficulty,
or what we call at optimal challenge point
leads to higher learning gains.
This is because at this point,
the potential learning benefit is the highest
So we deployed an algorithm,
that uses sensor data,
calculates the score,
computes learner’s performance,
figures out the training difficulty level,
and actuates the motors to adapt the tool
to higher or lower difficulty level.
Please refer to the paper for more details on the algorithm.
Now that we have the shape-changing tool ready
and algorithm to adapt the task difficulty deployed,
let us move on to user studies
In our first user study,
we tested for auto-adaptive v/s static condition.
We recruited 12 participants and trained them on both conditions.
In auto-adaptive, our algorithm adapted the tool
and in static condition, there was no adaptation,
but half training was in easiest setting
and the other half in the hardest setting.
The training involved scoring baskets.
Every scored basket was counted as 1 point
and a board hit was counted as ½ a point.
We conducted the study in 5 stages.
Before any training, we first did a pre-training test,
where the participants attempted 20 throws in the hardest setting.
Then they trained on one of the two conditions.
After that, we tested them on the hardest setting again
to see if there was any learning gain
after their training.
We then trained them on the next condition
followed by a post-training test in the same way.
The order of the training was randomized
to compensate for the order effect.
By comparing the learning gains,
we can analyze which training condition was useful.
And turns out that auto-adaptive condition
is significantly more useful!
The average learning gain after training
on the auto-adaptive condition was almost 4 points,
i.e. 20% higher in comparison to training on static condition.
Not only the results, but even the
feedback from the participants was promising.
The likert scale showing their preferences for training,
had adaptive training rated higher.
And the feedback suggests that
the adaptive tool helped them
focus better and feel motivated.
For example, p7 said
that seeing the changes in the tool
was like having a personal trainer motivate them.
We then moved onto our second study,
where we compared learning gains
of training on auto-adaptive condition v/s manually condition.
Like last time, we recruited 12 new participants
and conducted the study in the same 5 stages.
For manual setup, the participants used a keyboard
to adjust the hoop height and width
and thus decide the training level to train on.
We were interested in studying
if the learners are given freedom to choose
their training difficulty level, would they score better?
And it turns out people are not very great
at assessing their own skill levels and
deciding what difficulty to train on.
We again got a significantly higher learning gain
post auto-adaptive training,
this time more than 5 points,
ie. 25% increase in score after auto-adaptive training.
We noticed that the participants often
either under-challenged or over-challenged themselves
by either setting the difficulty level too easy,
thus losing the potential to learn,
or setting the difficulty too high,
again missing the opportunity to learn optimally.
Take a look at the score chart of a participant for example.
It shows their score over 80 attempts
during manual training where they over challenged themself.
The blue line here shows their score
and the red dotted line shows when they adapted tool harder.
As you can see, they set the difficulty too high early on,
and then struggled to score consistently for the rest of training.
And the same participant had a
much consistent score during auto-adaptive training
and the difficulty adaptation was regularly spaced.
And just like this participant,
a couple others manually chose to
train on only the hardest setting.
This is because their post-training performance
was being tested on the hardest setting.
However, in spite of that,
their scores were much better
after training on auto-adaptive condition.
Thus, it turns out that accessing one’s own skill level
and optimal training level is very hard.
It is no wonder that when asked
which training would they prefer in future,
most preferred auto-adaptive training
One participant mentioned that
they even trusted it more than themselves.
And other found it easier
as they didn’t have to think if they set the tool
too easy or too hard.
Of course, more longitudinal studies need to be conducted
to gather data on long-term learning gains.
Similarly, studies with a higher number of participants
also need to be done
And studies with other adaptive training tools
also need to be done
But the results of our studies are significant because
with such adaptive learning tools,
we can now make personalized training
accessible to learners who might not have access to trainers.
This also opens up avenues for future work,
especially in design several shape-adapting tools for motor skill learning,
For example in sports, an adaptive bike
Or in rehabilitation (of motor skills), an adaptive wobble board
Or even adaptive wearables
Thus, in conclusion we showed that
physical tools that adapt their shape based on learner’s performance
and adapt the task difficulty during training
lead to higher learning gains,
and thus helps in motor skill training
And with this, I look forward to your feedback
and am happy to take questions. Thank you!