- Thanks very much.
This is a collaboration between
Boston University and TU Delft, and it's titled
Thermal-Comfort Design of Personalized Casts.
Traditional orthopedic casts are typically
made from plaster or fiberglass
together with a soft inner cotton layer.
They often require orthopedic technicians
to assist with applying the cast material.
These casts are bulky, they're not water-resistant,
and they have poor breathability,
so the skin can become irritated,
and in worst case scenarios,
cutaneous complications can occur.
Traditional casts can also make
daily tasks very difficult, like taking a shower.
In our work, we introduce 3D-printable plastic casts
which are waterproof, ventilated, and lightweight,
making them more practical for daily use.
To guide our design, we introduce
a novel design objective of thermal-comfort.
We study the thermal sensitivity distributions
of different people, and we customize the cast design
for patients' thermal-comfort.
Our pipeline is illustrated here in this graphic.
The input of the system is a scanned 3D model,
and the thermal images taken by an infrared camera.
We first generate the temperature distribution
by mapping the thermal images onto the 3D model
that it acquired from the scan.
Then the thermal-comfort sensitivity is computed
to govern the pattern generation where
the human thermal-comfort is maximized.
Considering factors of structural strength,
we introduce a hollowed Voronoi tessellation pattern
that's employed for designing a web-like pattern.
After optimizing the pattern for air exposure,
a structural enhancement scheme is performed
to convert the surface into a non-uniform
thickened solid model that ensures
mechanical stiffness on the cast.
Finally, we 3D print the final result
to create the cast.
Our work is inspired by a number of startups
and designers that have worked on
developing personalized 3D-printed casts as shown here.
For example, in the top image,
Evil designed a web-like casting cortex
where the mesh is denser at the region
of bone fracture to provide support.
Karasahin created a similar cast
equipped with a low-intensity pulsed
ultrasound bone simulator system.
Researchers have also worked on predicting
the mechanical performance of 3D-printed casts
by using finite element analysis.
However, these existing approaches involve
heavy manual editing of 3D models, which is time-consuming
and requires experienced CAD engineers.
While these related methods design 3D-printed casts
with customized shapes and ensure mechanical stiffness,
we think about the addition of
thermal sensitivity variation at different regions
of a human body, and maximize that thermal-comfort.
Thermal-comfort as a sensation describes
how people feel about the thermal state of the body.
Researchers have found that the distribution
of thermal sensitivity changes from person to person.
As an example here, we're showing two different individuals
in two different environments.
You can see that, as an indication
that the temperature distribution
is unique in each of those.
This thermal comfort can be improved
by customizing the air exposure of skin
in regions with high thermal sensitivity.
With this in mind, our work proposes
a new computational approach to design a cast
customized in terms of the shape,
the mechanical stiffness, and the thermal comfort.
The question is, how do we actually get
this thermal state of the human body?
We start by 3D scanning the fractured region
of the human body.
Once we have that 3D-scanned shape,
we capture thermal images using an infrared camera,
and these are two-dimensional images
which are then mapped onto the 3D model
to generate those temperature distributions in 3D.
Thermal-comfort is usually evaluated in a perceptual manner,
so a widely-used method is to collect ratings from subjects
about their satisfaction under
different environmental temperatures.
For example, I'm showing a summary of
Zhang et al.'s work where they give a numerical scale
from very uncomfortable to very comfortable.
Then they developed a mathematical model
to predict people's thermal-comfort responses
to temperature changes by a set of experiments.
This experiment, generally, the subjects had
their local skin temperature changed and measured
by a measurable amount.
Then they were surveyed repeatedly
for their comfort level,
and a mathematical model and its coefficients
were derived by regression of the skin temperature
and the thermal-comfort vote
that was obtained in the experiments.
Here, you have that measure C of x,
which is a shifted and scaled sigmoid,
as a function of the temperature change,
and also a body coefficient.
That coefficient would change depending on
what part of the body.
In our application to obtain that
thermal-comfort distribution,
two temperature distributions are measured
on the human body.
One is on bare skin, so that's the initial condition,
and that's when the human body feels neutral.
The other is on fully-covered skin.
Here, we wrap a plastic film around the local body part
for 30 minutes, and then, under the same
environmental temperature, the thermal images are captured
immediately after uncovering that plastic film.
Using these two temperature distributions,
the initial conditions and then after
having the wrapped body part, we can compute
that thermal-comfort sensitivity.
Our optimization algorithm is formulated
as minimizing a total energy that is composed
of a pattern-controlled term,
that's E-p, and a thermal term, E-t.
The design of our pattern is based on the concept
of honeycomb cell structures, and we use these
because honeycombs are distinct in having
good mechanical properties as well as
being pretty lightweight.
We utilize a centroidal Voronoi tessellation
to generation the uniform honeycomb-like cell distribution,
so that's for the pattern control term.
Then we perform a hollowing in each of the Voronoi cells
to construct the pattern of the cast.
In addition to the pattern control,
then we have a thermal term, and that's added
to maximize the thermal comfort.
Our system generates patterns
with more air exposure at thermally sensitive regions.
Here, in this image, the red is showing
a higher thermal sensitivity,
so you want to have an adaptive cell distribution
that allows more exposure in those regions.
This thermal term measures performance
as the amount of skin that is covered
in the sensitive regions.
We integrate the sensitivity metric
over the solid parts of the cast,
which are the Voronoi cell edges.
The goal is to have less coverage of skin
where the sensitivity is higher.
We formulate the problem as an optimization,
which changes that distribution
and size of the Voronoi cells.
Lastly, we provide a contrast coefficient
to weight the pattern control term and the thermal term,
and that's given by lambda.
Users can adjust this parameter to control
the pattern distribution according to their preference.
In this example, going from left to right,
the lambda term is increasing,
and that puts more weight on the thermal-comfort term.
After the pattern generation is complete,
the next stage of the pipeline
is the structural enhancement.
Casts require, obviously, a certain stiffness
to support the fractured bones.
A goal of the structural enhancement is that
this thickening process should not change
the pattern that we just spent
all this time optimizing for thermal-comfort.
We introduce a bi-directional strategy to accomplish this.
While the thermal-comfort is optimized
in the surface of the model so that's in plane,
for stiffening, we extrude the model
on the direction orthogonal to the surface.
In this way, both the criteria on thermal comfort
and mechanical stiffness can be satisfied
without interfering with each other.
We perform finite element analysis
and three-point bending tests
to calculate the stiffness distribution.
We used PLA material properties.
To perform the structural test,
we fix the two extreme sides as the boundary conditions.
Then we apply a force at mid-span on the cast.
To enhance the stiffness while preserving the material,
we use the finite element analysis results
to iteratively determine
a non-uniformly distributed thickness.
First, we begin with a uniform thickened cast.
We obtain that stress distribution,
and that's shown here with the color coding.
Then we thicken the regions with the large stress
until a maximal stress on the solid model
is less than a critical threshold.
We're starting here with
a uniform thickness of four millimeters.
Then, when we take, say, one iterative step,
we have a varying thickness now from four
to five millimeters, and you can see
that that stress distribution has lowered the maximum.
Take another step, and we have now
a variation in thickness of four to six millimeters,
and you can see lower stress overall,
and the maximum is now below
what our threshold is set to.
This progressive structural enhancement
reduces the maximal stress on the cast.
Now, for comparison, I'm showing, on the bottom row,
this is a stress distribution for a cast
with a regular uniform thickness.
For a fair comparison, it has the same total weight,
meaning the same total material consumption,
as the optimized cast with non-uniform thickness.
As you can see, in comparing those two
stress distributions, the max is definitely higher
in the non-optimized version.
We tested our casts on different body parts
of four individuals, and are showing here
some typical daily use scenarios.
Our experimental results were encouraging.
We observed improved thermal properties
in our experiments, and we also had
positive user feedback, so I'll just
review a bit of that.
To verify that our casts are thermally comfortably,
we compared the skin temperature
after wearing the cast in contrast
to a uniform pattern.
For instance, we have this wrist cast.
The experiment was done in an office room,
so the room temperature was raised
while wearing the cast, and the participant
wore each cast version for 30 minutes.
Then we took these thermal images
immediately after removing the cast.
Again, to conduct a fair comparison,
the uniform cast has the same area
of air exposure compared to the optimized version.
Here, the uniformly sparse casts were generated
using the traditional centroidal Voronoid tessellation.
You can see, on the left is the initial condition,
on the right is after wearing the cast for 30 minutes.
The skin temperature is shown to be lower
for our optimized version.
This experiment was taken in a different environment.
For this arm cast, the user was in a sauna.
Here is the thermal results.
Again, you can see the lower temperatures
for the optimized version if you're looking
at the final result.
Here, we had another experiment for a neck cast.
We observed, again, a lower temperature distribution,
and that's due to the ventilation given
by our optimized casts.
This was true throughout all of our experiments.
In this fourth and last experiment,
we tested a leg cast, and this was, again,
in an office environment.
Here, you see the lower temperature,
looking at that right side on the final conditions
after the raised temperature for 30 minutes.
Again, this optimized ventilation given
by our optimized cast gave a better result
for the final temperature.
Just another look at how we did
the quantitative comparison between
the temperature differences on the skin.
We sample the thermal images,
and compare the histograms of temperature distribution.
Here, the red bars represent the uniform cast.
The yellow bars are our optimized cast,
and the orange is just the overlap.
You can see quite clearly with this visualization
that the optimized casts are consistently
a lower temperature distribution
compared to the uniform.
We conducted mechanical tests as well
on both the uniform and optimized cast.
In this case, when I say uniform,
I mean in the thickness.
We have a uniform thickness cast
versus the variable thickness cast.
Ours was between four and six millimeters,
and the uniform was at 4.4 millimeters uniformly.
This was chosen so that the total material
would be the same.
You can see, in this force displacement curve,
our optimized version is much steeper,
which just means that you need
a higher force to get the same displacement,
so we had better mechanical stiffness for our version.
We also collected feedback on the experience
from the participants, so we asked all the participants
that were shown wearing the casts in those four examples.
All the participants were asked
to rate their experience on different aspects
while wearing first a fiberglass cast,
then the uniformly sparse cast,
and then our optimized version.
The responses were set on a 10-point scale
regarding five different qualities,
the weight, the appearance, thermal-comfort,
tactility, and facility.
Facility was an overall assessment
of how usable they thought the cast would be.
Just going through each of those,
for the thermal-comfort, which is being highlighted here,
this was our main goal, and we were happy to see
that the optimized cast did, in all cases,
get the highest rating.
That's the blue bar, in this case.
This one is showing the lightweight
and feasibility for daily usage, the facility.
In this case, all participants agreed
that the 3D-printed casts were superior,
but you can see that it didn't really matter
whether it was optimized or non-optimized.
Just the 3D-printed casts at all
were better than the fiberglass.
3D-printed casts in general received
a lower score on tactility and appearance.
These scores could potentially be boosted
by adopting a more skin-friendly material.
With fiberglass casts, typically,
you don't just have a single material.
You have a cotton layer that's on the inside
to give a softness to the skin,
and that was not true for the 3D-printed casts.
If we added multi-materials, potentially,
we could improve on that.
Then we conducted an interview
with an orthopedic surgeon, and the doctor agreed
that our cast showed great promise on thermal-comfort
together with making a custom fit
based on 3D-scanning.
There were three challenges that were
foreseen by the doctor.
First, the main concern was that
a patient cannot always attain a particular pose
for 3D scanning, so if you're injured,
and in pain, you won't necessarily be able
to have the flexibility to hold your arm
in a useful way just to get the geometry acquisition.
Second, the current printing materials,
as mentioned, have this unsatisfactory tactile comfort,
which could be improved by using a softer material.
Furthermore, he noted that some post-processing
would probably be necessary.
For example, there can be ridges on the edges
just from a rough surface.
That's a review of the work on thermal casts.
I'll just acknowledge some of our funding sources,
and thank you for your attention.
(audience claps)
- We have time for any questions.
I have a quick question.
When you're doing the initial scan,
the person was in a rest state.
Would you imagine a different result
if they were in a more active state?
Would their profile be different?
For example, if you wanted a cast
for when a person was jogging,
that's a more intensive activity.
Do you have different hot spots?
- Right, yeah, that's a really good point.
In order to get the sensitivity in the first place,
we sort of assumed that the initial conditions were neutral,
but certainly, that could be the case.
If you already have the injury,
then that can affect the blood circulation,
so that would definitely be a challenge
to make it in practice.
- Okay, great.
Okay.
Let's thank Emily once again.
(audience claps)