Hi
everyone.
I'm
Mason.
Today
I'll
be
presenting
a
project
on
designing
a
treadmill
exercise
plan
for
diabetes
patients.
Just
to
give
a
bit
of
a
background
as
to
why
did
this
project.
In
the
spring
of
21,
one
of
my
family
members
was
told
that
he
had
type
two
diabetes.
A
follow-up report
in
the
summer
of
21
showed
that
his
glucose
level
was
higher
than
200
milligram
per
deciliter,
which
is
much
higher
than
the
normal
glucose
range
of
65
to
99
milli
grams
per
deciliter.
A
t
the
same
time,
I
wanted
to
conduct
a
project
to
analyze
exercise
data,
especially
because
diabetes
is
so
common
across
human
race.
We
did
this
project
on
designing
a
treadmill
program
for
my
family
member.
A
fter
following
this
plan
for
a
few
months,
his
glucose
level
went
back
to
the
normal
range
in
the
fall
of
21.
To define
our project
we
want
to
listen
to
the
voice
of
the
customer,
which
is
our
doctor,
who
provides
his
advice
on
what
my
family
member
should
do.
The
doctor
suggests
to
see how
mammals
take
metformin
and
insulin
and
also
exercise
more
intensely
to
burn
sugar.
In
this
project
will
focus
on
the
last
piece
of
advice
because
the
other
three
are
quite
easy
to
follow.
But
we
don't
quite
know
you
know
how
to
exercise
the
most
efficiently
yet.
We
need
to
translate
this
advice
to
what
we
will
do,
which
is
critical
to
quality.
Our goal
is
to
design
a
treadmill
program
specifically
focusing
on
the
legs,
to
strengthen
the
lower
body
muscle,
prevent
injury
and
also
help
cure
diabetes.
In
more
quantitative
terms,
we
want
to
lower
live
glucose
levels
to
below
100
mg
per
deciliter
and
also
reduce
the
resting
heart
rate.
A
s
healthier
individuals,
usually
have
a
lower
heart
rate,
since
it
takes
more
rigorous
exercise
for
them
to
lack
the
same
amount
of
oxygen
and
have
a
higher
heart
rate.
Just
introduce
the
team.
The
project
leader
is
me.
We
will
have
a
52.5
year
old
diabetes
patient
as
the
experimental
subject,
who
will
monitor
his
daily
blood
glucose level.
Our
family
doctor
is
also
in
the
team
who
will
follow
up
with
the
diabetes
patient
every
three
months.
We
will
also
have
two
advisors,
a
Six
Sigma
advisor
to
assist
in
the
DIMAC framework,
and
a
JMP
advisor
who
will
help
in
the
physical
analysis.
To
design
our
treadmill
program,
we
wanted
to
know
how
intense
we
should
exercise.
So
doing
exercise
for
normal
individuals,
it's
recommended
to
reach
50
to
85
%
of
your
maximum
heart
rate
for
the
exercise
to
be
effective.
However,
our
patient
is
at
a
moderate
to
higher
risk
of
having
a
heart
attack.
So
and
that's
because
his
calcium
score
was
131,
which
is
from
the
coronary
artery
heart
attack
risk
assessment,
which
is
at
the
72nd
percentile
for
his
age.
The
family
doctor
advised
to
limit
the
upper
bound
of
the
target
heart
rate
to
just
80
%
because
a
too
vigorous
and
intense
exercise
can
lead
to
heart
attacks.
But
to
accommodate
that
drop
in
the
upper
limit
of
the
target
heart
rate,
we
also
increased
the
lower
limit
from
50
%
to
65
%.
Now
for
the
exercise,
specifically,
we
chose
to
do
brisk
walking,
since
one
leg
will
always
be
on
the
ground
and
[inaudible 00:03:52]
so
brisk
walking
helps
to
protect
the
knee
and
lower
injury
risk.
Also
choosing
brisk
walking
over
running
helps
prevent
heart
attacks,
because
if
we
do
run,
we
may
accidentally
go
over
80
%
of
the
maximum
heart
rate.
So
to
determine
the
upper
and
lower
balance
of
our
target
heart
rate,
we
have
to
first calculate
the
maximum
heart
rate,
which
is
220
minus
the
years.
For
a
52.5
year
old,
the
maximum
heart
rate
would
be
167.5
beats
per
minute.
And
the
upper
limits
for
the
target
heart
rate
would
be
134
beats
per
minute
and
a
lower
limit
would
be 109
beats per minute.
As
you
might
recall,
one
of
our
goals
is
to
reduce
the
resting
heart
rate.
We
want
to
lower
the
heart
resting
heart
because
doing
so
makes
the
heart
muscles
stronger,
and
as
a
result
helps
prevent
heart
attacks.
When
we
strengthen
the
heart
muscles,
the
heart
pumps
more
blood
and
more
oxygen
is
available.
So
now
that
we've
set
our
goals
and
what
we'll
be
levering,
we
can
design
our
treadmill
program.
We'll
be
considering
three
control
variables.
The
first is
walking
uphill,
so
whether
we
want
to
add
incline
or
speed.
The
second
is
HIIT
or
High Intensity Interval Training,
which
involves
a
short
period
of
intense
exercise
followed
by
a
recovery
period.
And
we
need
to
design
this
HIIT
workout
so
that
the
heart
rate
does
not
go
below
65
%,
but
maximum
high
rate,
or
above
80
%
of
the
maximum
heart
rate.
The
third variable
is
frequency,
which
is,
how
many
times
we
will
conduct
this
exercise
every
week,
and
also
how
long
for
each
time.
To
set
up
our
experimental
design,
we
chose
to
alter
two
variables,
incline
and
speed.
An i
ncline
has
two
levels,
zero
or
five
degrees,
and
speed
has
nine
levels,
from
zero
to
3.6
mph.
Now
the
most
vigorous
level
would
be
at
an
incline
of
five
degrees
and
a
speed
of
3.6
mph.
We
don't
want
to
increase
the
speed
cast
3.6
mph
because
that
we
transitioning
to
running,
which
we
do
not
want,
since
we
want
to
focus
on brisk
walking,
and
also
don't
want
to
exceed
the
80
%
the
maximum
heart rate.
I
also
want
to
add
rests
after
each
exercise,
so
that
the
patient
returns
to
resting
heart
rate
before
undergoing
another
treatment.
We
ran
stepwise
regression
on
the
response
for
surface
design.
Our
model
has
a
pretty
higher
R-
square
of
97
%
and
a
p-value
less
than
0.05.
For
the
studentized
residuals,
which
are
the
residuals
that
underground
[inaudible 00:06:39]
,
only
about
one
point
goes
over
the
green
line,
which
is
two
2 cm
to
the
mean
and
the
red
line
represents
three
studentized
deviations.
So
jump
towards
the
most
significant
variables,
which
include
the
two
main
effects,
both
incline
and
speed,
the
interaction
term
between
incline
speed
and
the
quadratic
term
for
speed.
Why
did
the
model
include
the
quadratic
term
for
speed,
but
not
inclined?
Well,
if
we
look
at
the
interaction
profiles
on
the
right,
we
can
see
that
heart
rate
has
a
linear
correlation
with
incline
and
occur
relationship
with
speed.
We
can
explain
the
linear
relationship
between
heart
rate
and
inclined
as
due
to
potential
energy,
which
is
mgh,
mass
X
gravity
X
height.
So
height
is
a
linear
term.
When
the
angle
of
the
incline
is
small
enough,
we
can
use
the
mgh
approximation.
So
the
relationship
is
linear
based
on
physics.
On
the
other
hand,
speed
is
connected
to
kinetic
energy,
which
is
100
squared
or
one
half
X mass
X Velocity squared
.
So
kinetic
energy
has
a
quadratic
speed term.
From
the
bottom
two
propellers,
we
see
that
we
can
reach
the
lower
bound
of
the
target
heart
rate
at
109
beats per minute
at
an
incline
of
zero
degrees
and
a
speed
of
2.9
mph.
In
the
improved
[inaudible 00:08:06]
,
we
won't
need
to
include
easier
settings
than
these
levels,
since
the
heart
rate
will
then
be
too
low.
So
we
don't
want
to
go
under
the
60%.
Also,
the
upper
bound
of
the
target
heart
rate
is
reached
at
an
incline
of
five
degrees
and
a
speed
of
3.5
mph.
So
2.6
mph
is
a
good
maximum
level
for
speed.
We
also
want
to
prevent
injury
risk
in
addition
to
managing
diabetes,
which
is
our
second
objective.
More
than
80
%
of
runs
are
injured
each
year,
and
some
of
the
most
common
ones
include
Achilles
tendonitis,
G
splits
and
hamstringing
injuries.
We
also
wanted
to
avoid
injuries,
so
we
made
sure
that
the
patient
was
using
correct
form
while
brisk
walking
by
keeping
their
head
up
neck
relaxed
and
back
straights.
In
addition
to
posture,
muscle
coordination
is
also
really
important
to
preventing
lower
body
injuries.
So
three
motion
around
mechanics
studies
the
correct
angle
of
joints
relative
to
each
other
in
order
to
lower
injury
risk.
Centers allow
us
to
measure
and
monitor
the
angles
of
joints
relative
to
each
other.
We
can
also
conduct
exercise
on
[inaudible 00:09:19]
,
to
look
at
which
places
on
the
feet
hit
the
ground
[inaudible 00:09:]
,
Based
on
the
acces,
whether
the
runner
is
using
correct
form
or
not.
Just
to
take
a
quick
detour
in,
I
guess,
a
greater
study
of
injury
risk.
The
first thing
that
we
did
to
study
injury
risk,
is
we
conduct
variable
clustering,
which
groups
the
different
sports
together.
You
can
see
that
every
sport
has
different
injury
areas.
For
example,
cluster
one
and
cluster
three
have
different
pattern.
So
one
targets
the
lower
body,
which
makes
sense
as
it
consists
of
basketball,
soccer,
foot
skating,
tennis,
which
I'll
use
the
lower
blood
extensively.
Three
as
more
upper
body
injuries,
as
it
consists
of
golf,
volleyball
,
weightlifting.
You
can
see
that
these
clusters
are
differentiated
quite
well
from
the
principal
component
analysis.
Any,
I
guess,
exercise
plan
that
is
used
for
running,
for
example,
can
be
modified
for
the
other
sports.
It's
an
efficient
way
of
both
designing
access
to
plan
as
well
as
studying
injury
risk.
The
specific
injury
risk
that
we
looked
at
as
a
result
of
running
was
anterior
cruciate
ligament,
because
it
is
a
common
injury
in
a
lot
of
sports
that
used
to
lower
body
muscle,
such
as
basketball,
for
example.
And
ACL
is
located
at
the
center
of
the
knee
joint,
from
the
backside
of
the
thigh
bone
or
the
femur,
to
the
front
the
shinbone
or
the
tibia.
The
image
shows
the
three
othe
important
ligaments
of
the knee,
the
LCL,
the
MCL
and
the
PCL.
These
four
ligaments
are
crucial
to
protecting
the
ACL
from
the
injury,
especially
the
lateral
collateral
ligament,
as
well
as,
the
lateral
and
medial
which
are
pieces
of
cartilage
that
further
cushions
the
ACL.
ACL
injuries
occur
when
the
tibia,
or
the
shinbone
moves
two
foot
forward
and
is
hyper
extended,
so,
in
other
words,
straining
too
much.
That
causes
the
ACL
to
tear.
This
can
be
caused
by
a
variety
of
ways,
such
as
sudden
desolation
or
pivoting
in
places,
or
when
the
foot
is
planted
and
the
body
changes
direction suddenly.
These
movements
are
common,
in basketball, I said,
but
also
football,
soccer,
downhill
skiing,
and
mostly
this sports,
of
course,
use
a
lot
of
running.
So we
want
to
understand
how
ACL
injury
can
be
altered
before
an
act
fatigue,
specifically
in
the
context
of
running,
as
part
of
this
project that
focuses
on
running
and
injury
risk.
To
understand
the
connection
between
fatigue
and
ACL
injury,
we
wanted
to
conduct
an
experiment
to
measure
how
fatigue
and
ACL
injury
risk
are
related.
We
need
to
choose
an
exercise
that
can
compare
before
and
after
fatigue
flexion
and
forces.
And
choosing
the
right
exercise
that
can
accurately
measure
ACL
injury
risk
is
really
important
because,
after
we
consulted
with
the
local
physical
therapist,
we
found
that
the
countermovement jump
can
assess
the
ACL
injury
risk
quite
well
through
force
and
flexion
of
different
body
parts.
Before
I
go
into
what
exactly
is
a
countermovement jump,
let
me
tell
you
why
we
chose
this
exercise
specifically.
The
countermovement jump
is
a
jump.
So it can
assess
how
much
force
your
knee
puts
on
the
ground.
AO
nce
again,
Newton's
third law
comes
into
play
here
physics,
as
the
same
amount
of
force
from
your
knee
to
the
ground
is
experienced
by
the
knee
from
the
ground.
Too
much
force
onto
the
ground
can
increase
ACL
injury
risk
as
your
knee
experiences
too
much
force.
And
this
is
how
it
can
land
awkwardly
and
[inaudible 00:13:33]
the
ACL.
In
addition
to
force,
self
coordination
between
flexion
and
extension
of
hip
sneezes
and
ankles
are
really
important
when
doing
this
exercise.
Both
force
and
joint
flexion
are
connected
as
how
well
the
test
subject
effectively
transitions
from
flexion
extension
during
the
exercise
is
reflected
in
the
amount
of
force
they've
put
on
the
ground.
This
is
why
we
chose
the
countermovement jump,
because
it
enables
us
to
compare
the
before
and
after
fatigue
state
for
both
flexion
and
force,
which
are
the
two
most
important
factors
related
to
ACL
injury.
How
does
the
countermovement jump
work?
There's
five
main
exercise,
as
you
can
see
here,
the
unleaded,
breaking,
propulsive
flights
and
landing.
Five
images
on
the
top,
or
an
example
of
where
the
test
subject
is
at
each
of
these
phases
in
the
exercise.
The
bottom
graph
shows
the
time
versus
force
exerted
on
the
ground.
For
the
graph
on
the
bottom,
I'll
focus
on
the
top
curve.
So
the
darkest
blue
curve.
As
that
is
the
total
force,
whereas
the
two
curves
below
it
are
the
left
and
right
forces.
The
first phase
of
a
countermovement jump
is
the
unrated
phase.
When
the
person
is
standing
upright,
and
is
currently
the
orange
portion
of
the
graph.
Now
the
force
briefly
decreases
before
coming
back
up
as
the
person
continues
bending
their
knees.
When
they
reach
maximum
knee
and
hip
flexion
at
the
bottom
of
their
prejump,
which
is
the
breaking
phase,
they
start
extending
their
body,
which
is
propulsive
fit.
A smooth
transition
from
breaking
to
propulsive
is
reflected
in
a
smooth
curve
over
here.
The
smoother
the
curve,
the
more
the
knee
and
hip
are
coordinated
well.
Now
the
flight
time
is
when
the
force
is
zero,
before
the
ending,
before
ending
the
landing
phase.
As
you
can
see,
the
huge
spike
in
the
amount
of
force
in
the
landing
phase,
that
is
when
the
subject
lands.
The
first major
peak
is
the
soft
landing.
So
it's
the
light
blue
dots.
When
the
person
lands
on
their
toe
first is
a
soft
landing
before
hitting
the
hard
landing,
which
is
when
the
soles
of
the
feet
touch
the
ground.
That's
the
light grey
dot.
Doing
the
soft
landing
period
is
when
hip
and
knee
flexion
can
help
balance
the
force
across
different
body
parts
so
that
the
knee
isn't
the
only
one
experiencing
all
of
the
force.
That
can
help
reduce
ACL injuries.
But
if
the
hard
landing
or
the
second
peak
has
too
much
force,
that's
when
there
can
be
a
greater
risk
of
ACL
injury,
as
that's
when
the
whole
foot
runs
on
the
ground.
In
addition
to
the
general
flexion
and
force
patterns,
we'll
be
looking
to
see
if
there's
any
difference
in
the
soft
and
hard
landings
before
and
after
fatigue.
This
brings
me
to
experimental
design.
We
wanted
to
measure
the
flexion
of
your
different
joints,
such
as
ankle,
hips
and
knees,
to
study
them
in
detail
further,
as
they
reflect
how
fatigued
the
muscles
are.
The
more
the
muscles
are
fatigued,
the
greater
the
ACL injuries
.
To
measure
those
joints,
we
used
several
different
sensors
that
can
measure
all
of
these
joints
together,
and
we
attach
them
to
the
test
subject,
as
seen
on
the
right.
Two
on
the
bilateral
thigh,
two
on
the
bilateral shank
,
and
two
on
the bilateral dorsum.
Four on the front side,
and
one
on
the
pelvis
for
the
backside.
A
fter
calibrating
our
sensors,
the
test
subject
did
ten
runs
of
countermovement jumps.
He jump
ten
times
on
force
weight
to
measure
the
force.
A
fterwards
he
ran,
squatted,
played
basketball
jumps,
did
some
cone
drills
anything
to
get
fatigued
for
an
hour.
We
decided
1
h
would
be
enough
fatigue
because
it
was
pretty
hot
outside,
when
we
did
this
experiment.
After fatigue,
we
put
back
the
sensors
and
he
conducted
the
ten
trials
of
the
counter
movement
jump
once
again.
We
collected
our
data
through
a
biomedical software
called Meloxicam
that
enabled
us
to
simulate
the
different
degrees
and
angles
of
bending
for
several
different
joints,
as
well
as
the
forces
on
the
ground.
When
we
look
into
the
individual
force
profiles,
comparing
before and after fatigue
,
we
can
observe
even
more
differences
in
the
two
behaviors.
The
prejump,
which
is
the
transition
from
the
breaking
simple
to
the
propulsive,
is
a
lot
smoother
for
before
than
after
fatigue.
We
can
see
a
minor
plateau
during
the
after fatigue
which
could
indicate
that
the
different
body
parts
are
not
oriented
as
well
after fatigue.
The
also
for
the
landing
period,
the
heart
landing
and
the
soft
landing,
while
our
contrast
is
quite
huge
for
before
fatigue,
but
the
contrast
isn't
as
large
for
active
fatigue.
The
soft
landing
is
important,
once
again,
because
only
the
toe
touches
the
ground.
So
it
doesn't
increase
ACL
injury
risk
as
much
as
compared
to
the
greater
force
during
the
hard
landing.
The
hard
and
soft
landing
contrast
isn't
as
great
for
active
fatigue,
which
may
increase
the
ACl
injury risk
during
the
[inaudible 00:18:59]
.
This
may
have
been
due
to
the
muscles
not
being
able
to
hold
the
knee
as
stable
during
after
fatigue.
So
the
force
for
the
soft
landing
wasn't
too
much
greater
than
the
hard
landing.
We
want
to
know
if
there
are
any
other
platforms
besides
a
multivariate control
that
we
can
use
to
help
us
find
at
what
time
is
the
difference
between
before
and
after
fatigue the
most.
The
multivariate
SBC
control
chart
helps
us
visualize
the
differences.
The
top
right
corner
is
a
screenshot
of
the
different
trials.
A
ll
of
the
six
variables
we
study
are
considered
in
that
graph.
We
use
the
T-
square
chart
because
it
can
help
us
detect
the
relationship
between
the
six
variables
that
we
chose.
So
hip,
ankle
and
knee
flexion
for
both
the
right
and
left
side.
The
red
line
is
the
T-
squared
on
the
limit.
And
outliers
are
points
that
do
exceed
this
upper
control
limit.
Is
a
good
thing,
because
it
means
tha
there's
more
contrast
between
the
jumping
and
the
lining
behavior.
If
you
look
more
in-depth in
one
of
these
specific,
I
guess,
[inaudible 00:20:13]
spikes,
which
each
represent
one
trial
for
before and after fatigue
You
can
see
that
we
outline
five
main
points
for
one
trial,
before fatigue and after
fatigue,
To
help
visualize
the
differences.
The
biggest
difference
should
be
in
points
two
and
four,
since
two
is
right
before
the
chest
center
leaves
it
there.
So
it
should
have
one
of
the
highest
flexion
because
the
knee
are
bent
the
most
there.
Four,
it
should
also
be
similarly
as
high
as
two
because,
it's
when
the
[inaudible 00:20:45]
the
ground
and
lands
on the ground,
and
the
knees
are
bent the
most.
You
can
see
that
before
fatigue,
points
two
and
four
are
way
above
the
upper
controller
and
quite
different
from
one,
three
and
five.
But
for
after
fatigue,
the
contrast
is
much
less
obvious.
We'll
try
to
understand
why
that
is
and
connect
us
back
to
our
research
on
ACL injury.
Discussed
previously
by
looking
at
the
specific
contributions
of
each
of
these
flexions
and
joints
in
each
of
these
five
points.
But
the
multivariate control chart
for
now
tells
us
specifically
that
points
two and four
are
when
the
before
and
after
fatigue
defer
the
most.
As
I
said,
we
are
going
to
look
into
the
specific
flexion
components.
So
the
top
portion
is
the
before
fatigue,
top
row.
The
bottom
is
the
active
fatigue.
Now
these
three
joints
can
really
detect
the
difference
between
the
before
and
after
fatigue,
because
during
the
countermovement
jump,
the
lower
body
is
fatigue.
So
the
muscle
fatigue
and
the
different
angle
flexion
for
the
different
joints
is
evident
when
we
compare
the
contributions.
If
you
look
at
the
graphs
starting
at
one,
you
can
see
that
ankle
has
the
greatest
contribution,
where
hip
and
knee
are not
so
much
for
after
fatigue.
This
may
be
because
some
of
the
muscles
are
already
fatigue
and
only
some
muscles
contribute
to
the
overall
flexion.
Now
if
we
move
from
one
to
two
for before fatigue
,
we
see
a
very
clear
transition,
permit
even
distribution
across
all
the
different
joints
to
focusing
on
just
ankle
joint flexion
.
But
if
we
two,
the
other
knee
and
hip
components
still
somewhat
flexed
and
haven't
been
able
to
reach
full
extension.
We
have
these
4
bar
still
providing
some
contribution.
You
can
see
that
in
three,
the
atrophy
contrast
between
the
knee
and
hip
and
ankle
is
also
not
as
large
for
after
fatigue.
Again,
there's
not
a
full
extension
of
the
hips
and
knee.
Then
for
the
first distribution,
isn't
as
good
for
after
fatigue,
as
the
knee
and
hips
are
already
bending
at
the
same
time
as
ankle.
So
the
soft
landing,
which
is
at
point 4,
is
in
as
effective.
And
finally,
in
five,
the
ankle
is
still
flexed.
It
seems
that
the
knee
hip
aren't
able
to
support
the
body
now
and
rely
only
on
ankle.
This
may
indicate
the
lower
body
in
general
is
really
fatigued,
and
hip
and
knee
are
mostly
fatigued.
As
we
don't
see
much
contribution
from
them
the
fifth point.
There's
less
flexion
for
these
two
joints,
causing
a
greater
reliance
on
the
ankle,
which
increases
ACL injury
risk.
Now
back
to
our
treadmill
program.
With
information
from
injury
risk,
as
well
as
the
previous
research
on
HIIT,
we
can
set
up
a
HIIT
workout
plan.
We
designed
a
15
minute
workout
with,
the
first 2
minute
for
warm
up,
the
next
12
minutes
for
three
cycles
of
exercises,
consisting
of
2
minute
at
the
lower
bound
of
the
target
heart
rate
at
zero
inclined
and
2.9
speed,
and
2
minute
at
the
upper
bound
of
the
heart
target rate
at
five
inclined
and
3.5
speed.
So
total
of
12
minutes
and
then
one minute
cool
down.
We
chose
relatively
short
time
period
for
each
exercise
so
that
the
patient
can
work
out
for
a
longer
period
of
time
without
getting
tired
too
quickly,
which
may
have
happened
if,
the
exercise
at
the
upper
bound
of
the
target
heart
rate
was
done
for
too
long.
To
prove
that
our
project
is
successful,
we
will
need
to
validate
our
results.
We
want
to
see
if
the
workout
plan
helped
lower
the
diabetes
risk,
which
can
be
seen
through
the
glucose
reading,
and
the
resting
heart
rate.
Heart disease
risk
as
well,
which
can
be
measured
reducing
the
calcium
score.
All
these
values
to
decrease
if
the
treadmill
exercise
helps,
we
may
also
want
to
revise
the
treadmill
settings
every
three
to
six
months,
because
the
resting
heart
rate
may
have
decreased
due
to
stronger
heart
muscles.
In
that
case,
we
may
want
to
increase
incline
and
speed
to
achieve
the
same
target
heart
rate,
since
the
resting
heart rate
is
now
lower
due
to
a
stronger
heart.
So
in
conclusion,
we
utilize
the
DMAIC
approach
and
[inaudible 00:25:36]
methods
to
help
the
patient
with
type
two
diabetes
reduce
their glucose
levels
while
preventing
them
from
getting
a
heart
attack
or
getting
injured.
We
also
designed
an
experimental
plan
to
study
injury
risk,
but
looking
at
joint
flexion
as
well
as
force.
We
used
the
DOE
to designed
as
transplant
and
from
the
model
results,
we
selected
the
settings
at
109
beats
per
minute
and
134
beats
per
minute
to
be
included
in
a
15
minute
High
Intensity
Interval
Training
workout.
So
we're
currently
finishing
the
improvement
control
phases and which
we
hope
to
present
at
a
future
conference.
Yeah,
that's
all
I
have
for
today.
Thanks
for
tuning
in.
