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Fatig
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[
1
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[
2
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R
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cy
,
u
s
er
ac
c
ep
tan
ce
,
an
d
ac
co
u
n
tab
ilit
y
k
ey
is
s
u
es
with
in
th
e
h
u
m
an
f
a
cto
r
s
d
is
cip
lin
e
[
3
]
.
I
n
co
n
tr
ast,
g
lass
-
b
o
x
m
o
d
els
s
u
ch
as
d
ec
is
io
n
tr
ee
(
DT
)
a
n
d
r
u
le
-
b
ased
s
y
s
tem
s
o
f
f
er
i
n
ter
p
r
etab
ilit
y
an
d
alig
n
b
etter
with
th
e
p
r
in
cip
les
o
f
ex
p
lain
ab
le
AI
,
t
h
o
u
g
h
o
f
ten
at
th
e
co
s
t o
f
r
ed
u
ce
d
p
r
ed
i
ctiv
e
p
er
f
o
r
m
an
ce
.
Prio
r
r
esear
ch
in
f
atig
u
e
d
etec
tio
n
h
as
lar
g
ely
f
o
cu
s
ed
o
n
ca
r
d
r
iv
er
s
,
with
em
p
h
asis
o
n
f
ac
ial
r
ec
o
g
n
itio
n
,
ey
e
tr
ac
k
in
g
,
h
ea
r
t
r
ate
(
HR
)
v
ar
ia
b
ilit
y
,
an
d
v
eh
icu
lar
b
e
h
av
io
r
as
in
d
icato
r
s
o
f
d
r
o
wsi
n
ess
b
u
t
f
o
r
two
wh
ee
led
r
id
er
s
it’s
v
er
y
d
if
f
ic
u
lt
to
u
s
e
f
ac
ial
r
ec
o
g
n
itio
n
an
d
ey
e
t
r
ac
k
in
g
b
ased
s
en
s
o
r
s
[
4
]
.
Stu
d
ies
em
p
lo
y
in
g
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
lear
n
in
g
tech
n
iq
u
es
h
av
e
d
em
o
n
s
tr
ated
s
u
cc
ess
in
o
f
f
lin
e
d
etec
tio
n
s
ettin
g
s
,
y
et
th
eir
r
ea
l
-
wo
r
ld
ap
p
licatio
n
r
em
ain
s
lim
ited
b
y
th
e
lack
o
f
r
ea
l
-
tim
e
a
d
ap
tab
ilit
y
an
d
th
e
co
g
n
itiv
e
l
o
ad
im
p
o
s
ed
o
n
th
e
u
s
er
.
Mo
r
eo
v
er
,
f
ew
s
tu
d
ie
s
d
ir
ec
tly
c
o
m
p
ar
e
th
e
u
s
ab
ili
ty
,
in
ter
p
r
etab
ilit
y
,
an
d
co
n
tex
t
u
al
ap
p
r
o
p
r
iaten
e
s
s
o
f
d
if
f
er
en
t
AI
m
o
d
el
ty
p
es
in
f
atig
u
e
d
etec
tio
n
s
y
s
tem
s
.
I
n
th
e
r
ea
lm
o
f
m
o
to
r
cy
clin
g
,
wh
e
r
e
r
i
d
er
s
m
ay
lack
i
n
-
v
e
h
icle
d
ash
b
o
ar
d
s
o
r
p
ass
iv
e
m
o
n
ito
r
in
g
in
f
r
ast
r
u
ctu
r
e,
a
h
u
m
an
-
ce
n
ter
ed
a
p
p
r
o
ac
h
to
f
atig
u
e
d
etec
tio
n
is
ess
en
tial
to
en
s
u
r
e
s
y
s
tem
ef
f
ec
tiv
en
ess
with
o
u
t
co
m
p
r
o
m
is
in
g
u
s
er
ex
p
er
ien
ce
o
r
au
t
o
n
o
m
y
[
5
]
.
T
h
is
s
tu
d
y
s
ee
k
s
to
f
ill
th
is
g
a
p
b
y
c
o
n
d
u
ctin
g
a
co
m
p
a
r
ativ
e
an
aly
s
is
o
f
b
lack
-
b
o
x
an
d
g
l
ass
-
b
o
x
AI
ap
p
r
o
ac
h
es
f
o
r
r
ea
l
-
tim
e
r
i
d
er
f
atig
u
e
d
etec
tio
n
th
r
o
u
g
h
a
h
u
m
an
f
ac
t
o
r
’
s
len
s
.
W
e
co
lle
ct
m
u
ltimo
d
al
d
ata
in
clu
d
in
g
p
h
y
s
io
lo
g
ical
s
ig
n
al
s
,
b
eh
av
io
r
al
i
n
d
icato
r
s
,
a
n
d
e
n
v
ir
o
n
m
en
tal
co
n
tex
t
u
s
in
g
w
ea
r
ab
le
s
en
s
o
r
s
an
d
r
id
er
telem
etr
y
.
T
h
e
m
o
d
els
ar
e
ev
alu
ated
n
o
t
o
n
ly
o
n
tr
a
d
i
tio
n
al
m
etr
ics
s
u
ch
as
ac
cu
r
ac
y
an
d
laten
cy
b
u
t
also
o
n
h
u
m
an
ce
n
tr
ic
cr
ite
r
ia
s
u
ch
as
in
ter
p
r
etab
ilit
y
,
in
tr
u
s
iv
en
ess
,
an
d
co
g
n
itiv
e
co
m
p
atib
ilit
y
.
B
y
f
o
r
eg
r
o
u
n
d
in
g
t
h
e
h
u
m
an
o
p
er
ato
r
in
b
o
th
s
y
s
tem
d
esig
n
an
d
e
v
alu
atio
n
,
th
is
wo
r
k
co
n
tr
ib
u
tes
to
th
e
d
ev
elo
p
m
e
n
t
o
f
AI
-
p
o
wer
ed
f
atig
u
e
d
etec
tio
n
to
o
ls
th
at
ar
e
b
o
th
ef
f
ec
tiv
e
an
d
alig
n
ed
with
h
u
m
an
ca
p
ab
ilit
ies,
lim
itatio
n
s
,
an
d
ex
p
ec
tatio
n
s
.
Ultim
ately
,
th
e
f
in
d
in
g
s
aim
to
in
f
o
r
m
t
h
e
d
esig
n
o
f
in
tellig
en
t
r
id
er
s
u
p
p
o
r
t
s
y
s
tem
s
th
at
p
r
io
r
itize
s
af
ety
,
u
s
ab
ilit
y
,
an
d
tr
u
s
t.
I
n
d
o
in
g
s
o
,
th
is
s
tu
d
y
b
r
id
g
es
a
cr
itical
g
ap
b
etwe
en
AI
s
y
s
tem
d
ev
elo
p
m
en
t
an
d
h
u
m
an
f
ac
to
r
s
en
g
in
ee
r
in
g
,
em
p
h
asizin
g
th
e
im
p
o
r
tan
ce
o
f
tr
an
s
p
ar
en
cy
,
c
o
n
tex
t
u
al
r
elev
an
ce
,
an
d
u
s
er
-
ce
n
ter
ed
d
esig
n
in
d
ep
lo
y
in
g
r
ea
l
-
tim
e
f
atig
u
e
d
et
ec
tio
n
tech
n
o
l
o
g
ies
f
o
r
m
o
t
o
r
cy
cle
r
id
e
r
s
.
B
y
in
teg
r
atin
g
ex
p
lain
a
b
le
an
d
m
u
ltimo
d
al
f
atig
u
e
in
d
icato
r
s
in
to
s
y
s
tem
d
esig
n
,
th
e
p
r
o
p
o
s
e
d
ap
p
r
o
ac
h
s
u
p
p
o
r
ts
m
o
r
e
r
eliab
le
d
ec
is
io
n
m
ak
in
g
an
d
en
h
an
ce
s
r
id
er
ac
ce
p
tan
c
e
o
f
AI
-
ass
is
ted
s
af
ety
tech
n
o
lo
g
ies
in
r
ea
l
-
wo
r
ld
r
id
in
g
en
v
ir
o
n
m
en
ts
.
2.
M
E
T
H
O
D
2
.
1
.
P
r
o
po
s
ed
a
rc
hite
ct
ure
T
h
is
s
tu
d
y
a
im
s
t
o
d
e
v
e
lo
p
an
d
r
i
g
o
r
o
u
s
ly
e
v
al
u
a
te
a
s
e
n
s
o
r
-
b
ase
d
f
at
ig
u
e
d
ete
cti
o
n
s
y
s
te
m
f
o
r
m
o
to
r
c
y
cle
r
i
d
e
r
s
b
y
e
m
p
l
o
y
in
g
tw
o
d
is
ti
n
c
t
ty
p
es
o
f
AI
m
o
d
els:
b
la
ck
-
b
o
x
a
n
d
g
lass
-
b
o
x
a
p
p
r
o
a
c
h
es
[
6
]
.
T
h
e
p
r
o
p
o
s
e
d
s
y
s
te
m
in
te
g
r
ates
r
e
al
-
tim
e
p
h
y
s
io
lo
g
i
ca
l
d
at
a
s
u
c
h
as
HR
a
n
d
s
w
ea
ts
m
ea
s
u
r
e
m
e
n
t
[
7
]
.
T
h
e
o
v
e
r
al
l
ar
ch
ite
ct
u
r
e
o
f
t
h
is
a
p
p
r
o
a
ch
is
il
lu
s
tr
at
ed
i
n
Fi
g
u
r
e
1
,
w
h
ic
h
p
r
es
e
n
ts
t
h
e
p
r
o
p
o
s
e
d
a
r
c
h
it
ec
t
u
r
e
f
o
r
d
a
ta
ac
q
u
is
iti
o
n
,
p
r
o
ce
s
s
in
g
,
a
n
d
m
o
d
el
-
b
ase
d
f
ati
g
u
e
d
et
ec
ti
o
n
.
B
y
c
o
m
p
ar
in
g
t
h
e
p
er
f
o
r
m
a
n
c
e,
i
n
t
er
p
r
e
ta
b
ili
ty
,
an
d
r
el
ia
b
il
it
y
o
f
b
o
t
h
AI
m
o
d
el
t
y
p
es
,
t
h
e
s
t
u
d
y
s
ee
k
s
t
o
p
r
o
v
i
d
e
c
o
m
p
r
e
h
e
n
s
i
v
e
i
n
s
i
g
h
ts
i
n
t
o
e
f
f
e
cti
v
e
f
ati
g
u
e
d
et
ec
ti
o
n
m
ec
h
an
is
m
s
,
u
l
tim
at
ely
e
n
h
a
n
ci
n
g
r
i
d
e
r
s
af
et
y
an
d
r
e
d
u
ci
n
g
ac
ci
d
e
n
t
r
is
k
s
o
n
t
h
e
r
o
a
d
.
2
.
1
.
1
.
Ste
p 1
:
d
a
t
a
a
cquis
it
io
n a
nd
pre
-
pro
ce
s
s
i
ng
T
h
e
s
y
s
tem
’
s
in
itial c
o
m
p
o
n
e
n
t is d
ata,
wh
ich
in
v
o
lv
es two
m
ain
p
r
o
ce
s
s
es:
i)
Ph
y
s
io
lo
g
ical
d
ata
wer
e
co
llec
ted
u
s
in
g
wea
r
ab
le
s
en
s
o
r
s
,
in
clu
d
in
g
:
‒
HR
:
m
ea
s
u
r
ed
in
b
ea
ts
p
e
r
m
i
n
u
te,
HR
r
ef
lects
p
h
y
s
ical
an
d
m
en
tal
s
tates.
Ab
n
o
r
m
al
HR
p
atter
n
s
m
ay
s
ig
n
al
d
r
i
v
er
f
atig
u
e,
m
ak
in
g
it a
cr
u
cial
in
d
icato
r
in
r
ea
l
-
tim
e
f
atig
u
e
d
etec
tio
n
s
y
s
tem
s
.
‒
S
w
e
a
t
m
e
as
u
r
e
m
e
n
t
:
a
n
o
n
-
i
n
v
a
s
i
v
e
m
et
h
o
d
t
o
m
o
n
i
t
o
r
f
a
tig
u
e
o
r
s
t
r
es
s
b
y
a
n
al
y
z
i
n
g
s
we
a
t
r
a
t
e
o
r
s
k
i
n
c
o
n
d
u
c
t
i
v
i
t
y
,
t
y
p
i
c
a
l
l
y
u
s
in
g
g
a
l
v
a
n
i
c
s
k
i
n
r
es
p
o
n
s
e
/
el
e
c
tr
o
d
e
r
m
a
l
a
c
t
i
v
it
y
(
G
SR
/
E
D
A
)
s
e
n
s
o
r
s
.
ii)
Data
p
r
e
-
p
r
o
ce
s
s
in
g
:
R
aw
s
en
s
o
r
d
ata
u
n
d
er
g
o
n
o
r
m
aliza
tio
n
,
s
ig
n
al
f
ilter
in
g
,
f
e
atu
r
e
ex
tr
ac
tio
n
,
a
n
d
tim
e
s
e
g
m
en
tatio
n
t
o
tr
an
s
f
o
r
m
it in
to
s
tr
u
ctu
r
ed
i
n
p
u
ts
s
u
itab
le
f
o
r
m
ac
h
i
n
e
lear
n
i
n
g
m
o
d
els.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
R
ea
l
-
time
d
etec
tio
n
o
f rid
er fa
tig
u
e:
a
c
o
mp
a
r
a
tive
s
tu
d
y
o
f
b
la
ck
-
b
o
x
a
n
d
g
la
s
s
-
b
o
x
…
(
C
yn
th
ia
Ha
ya
t)
1411
Fig
u
r
e
1
.
Pro
p
o
s
ed
a
r
c
h
itectu
r
e
2
.
1
.
2
.
Ste
p 2
:
g
l
a
s
s
-
bo
x
v
s
bla
ck
-
bo
x
mo
dels
T
h
e
m
o
d
elin
g
co
m
p
o
n
e
n
t is ca
teg
o
r
ized
b
ased
o
n
i
n
ter
p
r
eta
b
ilit
y
[
6
]
,
[
8
]
:
i)
Glass
-
b
o
x
m
o
d
els
(
h
ig
h
tr
an
s
p
ar
en
cy
)
:
‒
L
o
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
:
a
s
im
p
le
lin
ea
r
m
o
d
el
o
f
f
er
in
g
clea
r
in
s
ig
h
t in
to
f
ea
t
u
r
e
co
n
tr
ib
u
tio
n
s
[
9
]
.
‒
R
u
le
-
b
ased
class
if
ier
(
R
B
C
)
:
u
s
es h
u
m
an
-
r
ea
d
ab
le
r
u
les f
o
r
d
ec
is
io
n
m
ak
in
g
[
1
0
]
.
‒
DT
:
p
r
o
v
id
es in
tu
itiv
e
d
ec
is
io
n
p
ath
s
v
ia
tr
ee
s
tr
u
ct
u
r
es
[
1
1
]
.
ii)
B
lack
-
b
o
x
m
o
d
els
(
h
ig
h
p
er
f
o
r
m
an
ce
an
d
lo
w
in
ter
p
r
etab
ilit
y
)
:
‒
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
:
c
ap
tu
r
es
s
p
atial
an
d
tem
p
o
r
al
f
ea
t
u
r
es
f
r
o
m
p
h
y
s
io
lo
g
ical
s
ig
n
als
[
1
2
]
.
‒
Set
co
v
er
in
g
m
ac
h
in
e
(
SC
M
)
c
lass
if
ier
:
m
ar
g
in
al
o
r
co
m
b
i
n
ed
class
if
ier
s
with
co
m
p
lex
d
ec
is
io
n
b
o
u
n
d
ar
ies
[
1
3
]
.
‒
R
an
d
o
m
f
o
r
est
(
R
F):
a
n
en
s
em
b
le
o
f
DT
d
eliv
er
in
g
h
ig
h
a
cc
u
r
ac
y
b
u
t
lim
ited
tr
an
s
p
ar
e
n
cy
[
1
4
]
.
T
h
is
co
m
p
ar
is
o
n
aim
s
to
ev
a
lu
ate
th
e
tr
a
d
e
-
o
f
f
b
etwe
en
m
o
d
el
ac
cu
r
ac
y
an
d
in
ter
p
r
et
ab
ilit
y
in
r
ea
l
-
tim
e
f
atig
u
e
d
etec
tio
n
[
1
5
]
,
[
1
6
]
.
2
.
1
.
3
.
Ste
p 3
:
XAI
e
x
pla
iner
l
a
y
er
SH
AP
a
nd
L
I
M
E
T
o
in
ter
p
r
et
b
lack
-
b
o
x
m
o
d
el
s
,
th
is
lay
er
in
tr
o
d
u
ce
s
ex
p
lain
ab
le
AI
tech
n
iq
u
es
[
1
7
]
,
[
1
8
]
.
T
h
ese
tech
n
iq
u
es
ar
e:
i)
Sh
ap
ley
ad
d
itiv
e
ex
p
lan
atio
n
(
SHAP)
q
u
an
tifie
s
ea
ch
f
ea
tu
r
e
’
s
co
n
tr
ib
u
tio
n
to
p
r
ed
ictio
n
s
u
s
in
g
g
am
e
th
eo
r
y
an
d
ii)
l
o
ca
l
in
ter
p
r
eta
b
le
m
o
d
el
-
ag
n
o
s
tic
ex
p
lan
atio
n
s
(
L
I
ME
)
b
u
ild
s
in
ter
p
r
etab
l
e
s
u
r
r
o
g
ate
m
o
d
els
ar
o
u
n
d
in
d
iv
id
u
al
p
r
e
d
ictio
n
s
.
T
h
ese
m
et
h
o
d
s
en
ab
le
t
h
e
s
y
s
tem
to
ex
p
lain
wh
y
a
d
r
i
v
er
is
class
if
ied
as "f
atig
u
ed
"
o
r
"n
o
t f
atig
u
ed
"
,
e
v
en
wh
e
n
u
s
in
g
c
o
m
p
lex
m
o
d
els.
2
.
1
.
4
.
Ste
p 4
:
e
x
pla
na
t
io
n
l
a
y
er
T
h
is
lay
er
tr
an
s
lates
ex
p
lain
er
o
u
t
p
u
ts
in
to
d
o
m
ain
-
s
p
ec
i
f
ic
in
f
o
r
m
atio
n
ac
ce
s
s
ib
le
to
u
s
er
s
an
d
ex
p
er
ts
[
1
9
]
,
[
2
0
]
.
C
o
n
tr
ib
u
tio
n
s
f
r
o
m
f
ea
tu
r
es
lik
e
HR
,
ey
e
b
lin
k
r
ate,
an
d
h
ea
d
n
o
d
d
in
g
ar
e
q
u
an
tifie
d
an
d
v
is
u
alize
d
.
R
e
-
p
r
o
ce
s
s
in
g
is
a
p
p
lied
to
en
s
u
r
e
t
h
e
p
r
esen
te
d
in
f
o
r
m
atio
n
is
clea
r
,
r
elev
an
t
,
an
d
i
n
ter
p
r
eta
b
le
b
y
n
o
n
-
tech
n
ical
s
tak
eh
o
ld
er
s
.
2
.
1
.
5
.
Ste
p 5
:
d
ec
is
io
n
m
a
k
ing
T
h
e
f
in
al
s
tag
e
in
teg
r
ates
cl
ass
if
icatio
n
r
esu
lts
an
d
th
eir
in
ter
p
r
etatio
n
s
f
o
r
in
f
o
r
m
ed
d
ec
is
io
n
-
m
ak
in
g
:
i)
r
esu
lt
i
n
ter
p
r
etati
o
n
:
a
s
s
ess
e
s
wh
eth
er
th
e
d
r
iv
er
s
h
o
ws
ea
r
ly
s
ig
n
s
o
f
f
atig
u
e
an
d
ii)
m
o
d
el
s
elec
tio
n
:
b
ased
o
n
ev
alu
atio
n
r
esu
lts
,
u
s
er
s
ca
n
ch
o
o
s
e
m
o
d
els
p
r
io
r
itizin
g
eith
er
ac
cu
r
ac
y
(
b
lack
-
b
o
x
)
o
r
tr
an
s
p
ar
en
cy
(
g
lass
-
b
o
x
)
.
T
h
i
s
ar
ch
itectu
r
e
o
u
tlin
es
a
co
m
p
r
eh
en
s
iv
e
f
r
am
ew
o
r
k
f
o
r
ev
alu
atin
g
r
ea
l
-
tim
e
d
r
iv
er
f
atig
u
e
d
etec
tio
n
m
o
d
els.
I
t
in
teg
r
ates
in
ter
p
r
eta
b
le
an
d
h
ig
h
-
p
e
r
f
o
r
m
an
ce
e
x
p
lain
ab
le
a
r
tific
ial
in
tellig
en
ce
(
XAI
)
tech
n
i
q
u
es,
s
u
ch
as
SHAP
an
d
L
I
ME
,
to
p
r
o
v
id
e
tr
an
s
p
ar
e
n
t
in
s
ig
h
ts
in
to
m
o
d
el
d
ec
is
io
n
-
m
ak
in
g
.
B
y
co
m
b
in
in
g
p
r
e
d
ic
tiv
e
ac
cu
r
ac
y
with
in
te
r
p
r
etab
ilit
y
,
th
e
s
y
s
tem
en
s
u
r
es
th
at
d
ec
is
io
n
s
r
eg
ar
d
i
n
g
d
r
iv
er
f
atig
u
e
ar
e
n
o
t
o
n
ly
r
eliab
le
b
u
t
also
ex
p
lain
ab
le,
wh
i
ch
is
an
ess
en
tial
r
eq
u
ir
em
en
t
f
o
r
d
ep
lo
y
m
en
t
i
n
s
af
ety
-
cr
itical
ap
p
licatio
n
s
.
Fu
r
th
er
m
o
r
e,
th
is
f
r
am
ew
o
r
k
s
u
p
p
o
r
ts
co
n
tin
u
o
u
s
m
o
n
ito
r
i
n
g
an
d
ev
alu
atio
n
,
en
ab
lin
g
th
e
id
en
tific
atio
n
o
f
p
o
ten
tial
f
ailu
r
e
p
o
in
ts
an
d
th
e
im
p
r
o
v
em
e
n
t
o
f
m
o
d
el
r
o
b
u
s
tn
ess
in
r
ea
l
-
wo
r
ld
co
n
d
itio
n
s
[
2
1
]
,
[
2
2
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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2
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2
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P
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s
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lo
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ica
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t
a
co
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rt
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T
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HR
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atig
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Par
ticip
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itio
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V
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clu
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th
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ap
p
r
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tain
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d
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ar
ticip
an
ts
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r
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r
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ts
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o
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ic
n
er
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s
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ile
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r
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T
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2
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r
esp
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T
ab
le
1
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Sam
p
le
f
it d
ata
H
R
[
b
p
m]
H
R
V
[
ms]
RR
-
i
n
t
e
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v
a
l
[
ms]
G
S
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1
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t
T
ab
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2
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Sam
p
le
f
atig
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e
d
ata
H
R
[
b
p
m]
H
R
V
[
ms]
RR
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v
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l
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S
R
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3
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a
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2
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6
5
8
5
1
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4
1
5
F
a
t
i
g
u
e
T
h
e
r
aw
HR
V
an
d
GSR
s
ig
n
al
s
ar
e
s
u
b
jecte
d
to
a
s
er
ies o
f
p
r
ep
r
o
ce
s
s
in
g
p
r
o
ce
d
u
r
es to
en
h
an
ce
d
ata
q
u
ality
,
i
n
clu
d
in
g
n
o
is
e
f
ilter
i
n
g
,
a
r
tef
ac
t
c
o
r
r
ec
tio
n
,
a
n
d
s
ig
n
al
n
o
r
m
aliza
tio
n
[
2
3
]
.
Fro
m
th
e
HR
V
d
ata,
b
o
th
tim
e
-
d
o
m
ain
f
ea
tu
r
es
s
u
ch
as
r
o
o
t
m
ea
n
s
q
u
ar
e
o
f
s
u
cc
ess
iv
e
d
if
f
er
en
ce
s
(
R
MSSD
)
an
d
s
tan
d
ar
d
d
ev
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n
o
f
n
o
r
m
al
-
to
-
n
o
r
m
al
in
ter
v
als
(
SDNN)
an
d
f
r
e
q
u
en
c
y
-
d
o
m
a
in
m
etr
ics
n
o
tab
ly
t
h
e
lo
w
f
r
eq
u
e
n
cy
t
o
h
ig
h
f
r
eq
u
e
n
cy
(
L
F/HF
)
r
atio
ar
e
e
x
tr
ac
ted
,
as
th
ese
p
a
r
am
eter
s
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e
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lis
h
ed
in
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icato
r
s
o
f
f
atig
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elate
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o
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ic
m
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latio
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r
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SR
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e
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als
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r
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to
d
e
r
iv
e
k
ey
f
ea
tu
r
es
s
u
c
h
a
s
s
k
in
co
n
d
u
ctan
ce
lev
el
(
SC
L
)
an
d
s
k
in
co
n
d
u
c
tan
ce
r
esp
o
n
s
e
(
SC
R
)
,
wh
ich
ar
e
k
n
o
wn
to
r
e
f
lect
v
ar
iati
o
n
s
in
s
y
m
p
ath
etic
n
er
v
o
u
s
ac
tiv
ity
lin
k
ed
t
o
em
o
tio
n
al
ar
o
u
s
al
an
d
f
atig
u
e
s
tate
s
[
2
4
]
–
[
2
6
]
.
Fo
llo
win
g
th
e
ev
alu
atio
n
,
a
c
o
m
p
ar
ativ
e
an
aly
s
is
was
co
n
d
u
cted
to
ex
p
l
o
r
e
th
e
tr
ad
e
-
o
f
f
s
b
etwe
en
b
lack
-
b
o
x
m
o
d
els an
d
g
lass
-
b
o
x
m
o
d
els.
T
h
e
an
al
y
s
is
was s
tr
u
ctu
r
ed
a
r
o
u
n
d
th
e
f
o
llo
win
g
d
im
en
s
io
n
s
:
i)
Acc
u
r
ac
y
:
b
lack
-
b
o
x
m
o
d
els
ty
p
ically
ac
h
iev
ed
h
i
g
h
er
p
r
e
d
ictiv
e
p
er
f
o
r
m
an
ce
d
u
e
to
th
eir
ab
ilit
y
to
ca
p
tu
r
e
co
m
p
lex
,
n
o
n
-
lin
ea
r
p
atter
n
s
in
p
h
y
s
io
lo
g
ical
s
ig
n
als.
Me
tr
ics
s
h
o
wed
th
at
m
o
d
els
lik
e
C
N
N
an
d
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
y
ield
e
d
s
u
p
e
r
io
r
F1
-
s
co
r
es
an
d
r
ed
u
ce
d
m
is
class
if
icatio
n
r
ates
co
m
p
ar
ed
to
th
eir
i
n
ter
p
r
etab
l
e
co
u
n
ter
p
ar
ts
[
2
7
]
,
[
2
8
]
.
ii)
I
n
ter
p
r
etab
ilit
y
:
g
lass
-
b
o
x
m
o
d
els
d
em
o
n
s
tr
ated
clea
r
ad
v
an
tag
es
in
tr
an
s
p
ar
en
cy
.
DT
a
n
d
LR
p
r
o
v
id
e
d
u
n
d
er
s
tan
d
a
b
le
d
ec
is
io
n
p
ath
s
an
d
f
ea
tu
r
e
weig
h
tin
g
s
,
m
ak
in
g
t
h
em
p
r
ef
er
a
b
le
in
s
e
ttin
g
s
wh
er
e
ex
p
lain
ab
ilit
y
an
d
u
s
er
tr
u
s
t
ar
e
cr
itical,
s
u
ch
as
f
o
r
r
eg
u
lato
r
y
c
o
m
p
lian
ce
o
r
d
r
i
v
er
f
ee
d
b
ac
k
s
y
s
tem
s
[
2
9
]
–
[
3
1
]
.
T
h
is
co
m
p
ar
ativ
e
f
r
am
ewo
r
k
p
r
o
v
i
d
es
a
n
u
a
n
ce
d
u
n
d
er
s
ta
n
d
in
g
o
f
th
e
ac
cu
r
ac
y
–
tr
an
s
p
ar
en
cy
tr
a
d
e
-
o
f
f
in
s
en
s
o
r
-
b
ased
f
atig
u
e
d
etec
tio
n
s
y
s
tem
s
.
I
t
s
u
p
p
o
r
ts
in
f
o
r
m
ed
m
o
d
el
s
elec
tio
n
tailo
r
ed
t
o
s
p
ec
if
ic
u
s
e
-
ca
s
e
r
eq
u
ir
em
e
n
ts
—
wh
eth
er
p
r
io
r
it
izin
g
p
r
ed
ictiv
e
p
r
ec
is
io
n
o
r
in
ter
p
r
etab
ilit
y
f
o
r
u
s
er
tr
u
s
t
[
3
2
]
–
[
3
4
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
I
nte
rpre
t
a
t
io
n
o
f
f
e
a
t
ur
e
co
ntr
ibu
t
io
ns
us
ing
SH
AP
s
um
m
a
ry
plo
t
a
nd
L
I
M
E
T
o
in
v
esti
g
ate
th
e
c
o
n
tr
ib
u
tio
n
s
o
f
p
h
y
s
io
lo
g
ical
f
ea
t
u
r
es
to
s
tr
ess
an
d
f
atig
u
e
p
r
ed
ictio
n
,
we
u
s
ed
SHAP
an
d
L
I
ME
tech
n
iq
u
es,
as
s
h
o
wn
in
Fig
u
r
es
2
a
n
d
3
.
Key
f
ea
tu
r
es
in
clu
d
e
HR
,
HR
V,
R
R
-
in
ter
v
al,
an
d
GSR
,
wh
ich
r
ef
lect
a
u
to
n
o
m
ic
n
e
r
v
o
u
s
s
y
s
tem
ac
tiv
ity
.
L
I
ME
v
is
u
aliza
tio
n
s
s
h
o
w
th
at
h
ig
h
e
r
HR
V,
lo
wer
HR
,
an
d
lo
n
g
er
R
R
-
in
ter
v
als
r
ed
u
ce
f
atig
u
e
p
r
o
b
a
b
i
lity
,
wh
ile
elev
ated
GSR
an
d
ce
r
tain
HR
v
alu
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
R
ea
l
-
time
d
etec
tio
n
o
f rid
er fa
tig
u
e:
a
c
o
mp
a
r
a
tive
s
tu
d
y
o
f
b
la
ck
-
b
o
x
a
n
d
g
la
s
s
-
b
o
x
…
(
C
yn
th
ia
Ha
ya
t)
1413
in
cr
ea
s
e
it.
T
h
ese
ex
p
lan
atio
n
s
en
h
an
ce
m
o
d
el
tr
an
s
p
ar
en
cy
an
d
p
r
o
v
id
e
ac
tio
n
ab
le
in
s
ig
h
ts
f
o
r
d
esig
n
in
g
p
er
s
o
n
alize
d
f
atig
u
e
m
o
n
ito
r
in
g
s
y
s
tem
s
.
Fig
u
r
e
2
.
SHAP su
m
m
ar
y
p
lo
t
Fig
u
r
e
3
.
L
I
ME
ex
p
la
n
atio
n
3
.
2
.
E
v
a
lua
t
i
o
n a
nd
co
m
pa
r
is
o
n o
f
m
o
dels
T
h
is
s
ec
tio
n
p
r
esen
ts
a
co
m
p
r
eh
en
s
iv
e
e
v
alu
atio
n
o
f
th
e
i
m
p
lem
en
ted
m
o
d
els
with
r
es
p
ec
t
to
two
cr
itical
asp
ec
ts
:
d
etec
tio
n
ac
cu
r
ac
y
an
d
in
ter
p
r
etab
ilit
y
.
Fu
r
th
er
m
o
r
e
,
a
co
m
p
ar
ativ
e
a
n
a
ly
s
is
i
s
co
n
d
u
cted
b
etwe
en
b
lack
-
b
o
x
an
d
g
las
s
-
b
o
x
m
o
d
els
to
h
ig
h
lig
h
t
th
e
tr
ad
e
-
o
f
f
s
in
v
o
lv
ed
in
m
o
d
el
s
elec
tio
n
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
ea
c
h
m
ac
h
i
n
e
lear
n
in
g
m
o
d
el
was
ass
ess
ed
u
s
in
g
s
tan
d
a
r
d
e
v
alu
atio
n
m
etr
ics,
in
clu
d
in
g
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
th
e
ar
ea
u
n
d
er
th
e
r
ec
eiv
e
r
o
p
er
atin
g
c
h
ar
ac
ter
is
tic
(
R
OC
)
cu
r
v
e
ar
ea
u
n
d
e
r
th
e
r
ec
eiv
er
o
p
e
r
atin
g
c
h
ar
a
cter
is
tic
(
AURO
C
)
.
T
h
e
r
esu
lts
f
o
r
th
e
tr
ain
in
g
d
ataset
ar
e
s
u
m
m
ar
ized
in
T
ab
le
3
,
wh
ile
th
e
p
er
f
o
r
m
an
ce
m
etr
ics
o
b
tain
ed
f
r
o
m
th
e
test
in
g
d
ataset
ar
e
p
r
esen
ted
in
T
ab
le
4
.
T
h
ese
tab
les
p
r
o
v
id
e
a
co
m
p
ar
ativ
e
o
v
er
v
iew
o
f
h
o
w
b
lack
-
b
o
x
an
d
g
lass
-
b
o
x
m
o
d
els
p
er
f
o
r
m
in
ter
m
s
o
f
p
r
ed
ictiv
e
ca
p
a
b
ilit
y
,
allo
win
g
f
u
r
th
er
an
aly
s
is
o
f
th
e
tr
a
d
e
-
o
f
f
s
b
etwe
en
ac
cu
r
ac
y
an
d
in
ter
p
r
etab
ilit
y
.
T
ab
le
3
.
Per
f
o
r
m
an
ce
v
alu
e
f
o
r
tr
ain
d
ataset
M
e
t
h
o
d
s
No
M
o
d
e
l
A
c
c
u
r
a
c
y
(
%)
P
r
e
c
i
s
i
o
n
(
%)
R
e
c
a
l
l
(
%)
F1
-
sc
o
r
e
(
%)
A
U
R
O
C
-
mea
n
I
n
t
e
r
p
r
e
t
a
b
i
l
i
t
y
G
l
a
ss
b
o
x
-
m
o
d
e
l
1
DT
8
3
.
3
8
0
.
4
8
4
.
2
8
2
.
2
0
.
8
8
H
i
g
h
2
LR
8
1
.
1
7
8
.
2
8
0
.
5
7
9
.
3
0
.
8
5
H
i
g
h
3
R
B
C
7
5
.
6
7
4
.
8
7
6
.
2
7
5
.
5
0
.
8
1
V
e
r
y
h
i
g
h
B
l
a
c
k
box
-
mo
d
e
l
1
C
N
N
+
LST
M
8
9
.
4
8
8
.
7
9
0
.
1
8
9
.
4
0
.
9
4
Lo
w
2
S
V
M
c
l
a
ss
i
f
i
e
r
9
2
.
5
9
1
.
3
9
3
.
7
9
2
.
5
0
.
9
6
Lo
w
3
RF
9
3
.
2
9
2
.
5
9
4
.
2
9
3
.
2
0
.
9
5
Lo
w
T
ab
le
4
.
Per
f
o
r
m
an
ce
v
alu
e
f
o
r
test
in
g
d
ataset
M
e
t
h
o
d
s
No
M
o
d
e
l
A
c
c
u
r
a
c
y
(
%)
P
r
e
c
i
s
i
o
n
(
%)
R
e
c
a
l
l
(
%)
F1
-
sc
o
r
e
(
%)
A
U
R
O
C
-
mea
n
I
n
t
e
r
p
r
e
t
a
b
i
l
i
t
y
G
l
a
ss
b
o
x
-
m
o
d
e
l
1
DT
8
5
.
1
0
8
0
.
4
0
8
4
.
3
0
8
2
.
5
0
0
.
8
7
H
i
g
h
2
LR
8
3
.
6
0
7
9
.
1
0
8
0
.
5
0
7
9
.
1
0
0
.
8
5
H
i
g
h
3
R
B
C
8
4
.
2
0
7
5
.
7
0
7
4
.
6
0
7
5
.
9
0
0
.
8
V
e
r
y
h
i
g
h
B
l
a
c
k
box
-
mo
d
e
l
1
C
N
N
+
LST
M
9
4
.
3
0
8
9
.
8
0
9
1
.
7
0
9
0
.
1
0
0
.
9
5
Lo
w
2
S
V
M
c
l
a
ss
i
f
i
e
r
8
8
.
2
0
9
2
.
5
0
9
4
.
2
0
9
2
.
1
0
0
.
9
5
Lo
w
3
RF
9
1
.
5
0
9
4
.
3
0
9
4
.
9
0
9
3
.
7
0
0
.
9
6
Lo
w
T
h
e
v
i
s
u
a
l
i
z
a
t
io
n
p
r
e
s
en
t
ed
h
i
g
h
l
i
g
h
t
s
a
c
l
e
ar
t
r
ad
e
-
o
f
f
b
e
t
w
e
e
n
m
o
d
e
l
a
c
cu
r
a
cy
a
n
d
i
n
t
e
r
p
r
e
t
ab
i
l
i
t
y
w
i
t
h
in
t
h
e
c
o
n
t
e
x
t
o
f
r
e
a
l
-
t
im
e
m
o
to
r
cy
c
l
e
r
i
d
e
r
f
a
t
ig
u
e
d
e
t
e
c
t
io
n
.
T
h
i
s
t
r
a
d
e
-
o
f
f
i
s
c
en
t
r
a
l
t
o
t
h
e
s
e
l
e
ct
i
o
n
o
f
a
p
p
r
o
p
r
ia
t
e
m
a
c
h
in
e
l
e
ar
n
in
g
m
o
d
e
l
s
,
e
s
p
e
c
i
a
l
ly
w
h
en
b
a
la
n
c
in
g
p
r
ed
i
c
t
i
v
e
p
e
r
f
o
r
m
a
n
ce
w
i
t
h
s
y
s
t
e
m
t
r
an
s
p
ar
e
n
cy
.
i)
Hig
h
-
ac
cu
r
ac
y
,
lo
w
-
in
ter
p
r
et
ab
ilit
y
m
o
d
els:
m
o
d
els
s
u
ch
as
C
NN,
L
STM
,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
,
an
d
R
F
ac
h
iev
e
h
i
g
h
ac
cu
r
ac
y
b
y
ca
p
t
u
r
in
g
c
o
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le
x
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n
o
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ea
r
p
atter
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in
p
h
y
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io
lo
g
ical
d
ata
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Ho
wev
er
,
th
eir
d
ec
is
io
n
-
m
a
k
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g
p
r
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ce
s
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es
ar
e
o
p
a
q
u
e,
class
if
y
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g
th
em
as
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lack
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o
x
"
m
o
d
els
.
Sp
ec
ialized
XAI
tech
n
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u
es li
k
e
SHAP o
r
L
I
ME
ar
e
n
ee
d
ed
to
in
ter
p
r
et
t
h
eir
p
r
e
d
ictio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
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:
2
2
5
2
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8
9
3
8
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tif
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,
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.
2
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Ap
r
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20
26
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1
4
0
9
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4
1
7
1414
ii)
Hig
h
-
in
ter
p
r
eta
b
ilit
y
,
lo
wer
-
a
cc
u
r
ac
y
m
o
d
els:
in
ter
p
r
etab
le
o
r
"g
lass
-
b
o
x
"
m
o
d
els,
s
u
ch
as
DT
,
L
R
,
an
d
R
B
C
,
p
r
o
v
id
e
tr
an
s
p
ar
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n
t
d
ec
is
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-
m
ak
in
g
th
r
o
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g
h
r
ea
d
ab
l
e
r
u
les
o
r
f
ea
t
u
r
e
weig
h
ts
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T
h
ey
s
h
o
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h
o
w
v
ar
iab
les
lik
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HR
V
o
r
s
wea
t
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d
u
ctiv
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in
f
lu
e
n
ce
p
r
e
d
ictio
n
s
.
Ho
wev
er
,
th
eir
p
r
ed
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e
p
er
f
o
r
m
an
c
e
is
g
en
er
ally
lo
wer
,
esp
ec
ially
with
h
ig
h
-
d
im
e
n
s
io
n
al
o
r
n
o
n
lin
ea
r
d
ata.
T
o
illu
s
tr
ate
th
e
b
alan
ce
b
et
wee
n
p
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p
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f
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m
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m
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el
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ter
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,
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tr
ad
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f
co
m
p
ar
is
o
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am
o
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g
th
e
ev
alu
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ted
m
ac
h
in
e
lear
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g
m
o
d
els
is
v
is
u
alize
d
in
Fig
u
r
e
4
.
T
h
is
f
ig
u
r
e
h
ig
h
lig
h
ts
h
o
w
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lack
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b
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x
m
o
d
els
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en
er
ally
ac
h
iev
e
h
ig
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er
ac
cu
r
ac
y
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th
e
ex
p
en
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e
o
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ar
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ile
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m
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f
e
r
g
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r
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ter
p
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d
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elec
tio
n
with
ap
p
licatio
n
-
s
p
ec
if
ic
r
eq
u
ir
em
e
n
ts
.
I
n
th
e
d
ev
elo
p
m
en
t
o
f
m
ac
h
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ased
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atig
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s
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p
r
o
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r
iate
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r
eq
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ir
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ef
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lly
b
alan
ci
n
g
two
cr
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cial
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ac
to
r
s
:
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ter
p
r
etab
ilit
y
an
d
ac
c
u
r
ac
y
.
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h
is
s
ec
tio
n
p
r
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d
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ailed
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m
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ar
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is
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o
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s
th
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im
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n
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icted
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m
p
a
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g
tr
ad
e
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f
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t.
T
h
e
p
l
o
t m
ap
s
s
ix
m
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n
a
two
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d
im
en
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s
p
ac
e
with
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ter
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n
t
h
e
x
-
ax
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an
d
class
if
icatio
n
ac
cu
r
ac
y
o
n
th
e
y
-
a
x
is
.
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R
an
d
DT
s
h
o
w
h
ig
h
in
ter
p
r
eta
b
ilit
y
with
m
o
d
er
ate
ac
cu
r
a
cy
(
~
0
.
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2
–
0
.
8
5
)
,
s
u
itab
le
f
o
r
c
o
n
t
ex
ts
r
eq
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ir
in
g
clea
r
r
atio
n
ale.
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h
e
R
B
C
o
f
f
er
s
v
er
y
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ig
h
in
ter
p
r
etab
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(
~1
.
0
)
with
s
l
ig
h
tly
lo
wer
ac
cu
r
ac
y
(
~0
.
8
1
)
,
id
ea
l
f
o
r
s
af
ety
-
cr
itical
en
v
ir
o
n
m
en
ts
.
SVM
an
d
R
F
b
alan
ce
in
ter
p
r
etab
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y
an
d
ac
cu
r
ac
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,
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ile
C
NN+
L
ST
M
ac
h
iev
es
th
e
h
ig
h
est
ac
cu
r
ac
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(
~0
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4
)
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t
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west
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ak
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it
v
alu
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le
wh
er
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p
r
ed
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n
p
er
f
o
r
m
an
ce
is
p
r
io
r
itized
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v
e
r
tr
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s
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ar
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u
r
e
4
.
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r
ad
e
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o
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f
o
f
ML
m
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d
els
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h
e
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ad
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o
f
f
cu
r
v
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p
h
asizes
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at
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el
s
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m
u
s
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lig
n
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g
o
als
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n
s
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c
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itical
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p
licatio
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s
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ar
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ile
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ay
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e
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to
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e
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r
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o
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s
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e
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ic
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o
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CO
NCLU
SI
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N
T
h
e
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tal
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o
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ate
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ican
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ad
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e
r
f
o
r
m
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ce
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d
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ter
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n
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atig
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tem
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ased
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h
y
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io
lo
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ig
n
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lack
-
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o
x
m
o
d
els,
p
a
r
ticu
lar
ly
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NN+
L
T
SM
(
9
4
.
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%
ac
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r
ac
y
)
,
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(
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1
.
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%
ac
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r
ac
y
)
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d
SVM
(
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8
.
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%
ac
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r
ac
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o
n
t
h
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test
in
g
d
ataset,
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n
s
is
ten
tly
o
u
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er
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o
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m
e
d
g
l
ass
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m
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d
els
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ter
m
s
o
f
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
a
n
d
AUROC
.
T
h
ese
m
o
d
els
ef
f
ec
tiv
ely
ca
p
tu
r
e
c
o
m
p
lex
,
n
o
n
lin
ea
r
p
atter
n
s
with
in
th
e
d
ata
b
u
t
lac
k
in
t
r
in
s
ic
tr
an
s
p
ar
en
cy
,
n
ec
ess
itatin
g
p
o
s
t
-
h
o
c
in
ter
p
r
etab
ilit
y
m
eth
o
d
s
s
u
ch
as
SHAP
o
r
L
I
ME
.
C
o
n
v
er
s
ely
,
g
la
s
s
-
b
o
x
m
o
d
els
s
u
ch
as
DT
,
r
u
le
-
b
ased
class
if
ier
an
d
LR
p
r
o
v
id
e
m
o
r
e
in
ter
p
r
etab
le
o
u
tp
u
ts
cr
itical
in
s
af
ety
-
s
e
n
s
itiv
e
ap
p
licatio
n
s
lik
e
r
id
er
m
o
n
ito
r
in
g
y
et
d
el
iv
er
r
elativ
ely
lo
wer
p
er
f
o
r
m
an
ce
(
e.
g
.
,
8
5
.
1
%,
8
4
.
2
%
an
d
8
3
.
6
%
ac
cu
r
ac
y
,
r
esp
ec
tiv
ely
)
.
T
h
ese
f
in
d
in
g
s
u
n
d
er
s
co
r
e
th
e
im
p
o
r
tan
ce
o
f
alig
n
in
g
m
o
d
el
s
elec
tio
n
with
ap
p
licatio
n
-
s
p
ec
if
ic
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ar
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N:
2252
-
8
9
3
8
R
ea
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time
d
etec
tio
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o
f rid
er fa
tig
u
e:
a
c
o
mp
a
r
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tive
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y
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f
b
la
ck
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b
o
x
a
n
d
g
la
s
s
-
b
o
x
…
(
C
yn
th
ia
Ha
ya
t)
1415
r
eq
u
ir
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e
n
ts
.
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n
co
n
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ts
wh
er
e
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tim
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ar
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a
n
d
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p
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er
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s
t
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y
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tem
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n
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ilit
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ay
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e
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ef
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ite
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ate
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wev
er
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en
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e
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f
o
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m
a
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ar
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n
t,
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ig
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s
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g
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en
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o
f
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er
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le
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ti
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r
k
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ld
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s
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tim
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ad
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th
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h
y
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atio
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ter
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tech
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es
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t
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m
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r
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Evaluation Warning : The document was created with Spire.PDF for Python.
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1416
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S
[
1
]
A.
-
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.
A
.
A
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.
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[
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S
.
A
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z
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u
m
a
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Fa
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[
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A
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[
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X
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M
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a
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H
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,
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.
[
5
]
Z.
D
w
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k
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sk
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,
K
.
D
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n
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i
,
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.
[
6
]
T.
Le
e
,
J.
N
a
t
a
l
w
a
l
a
,
V
.
C
h
a
p
p
l
e
,
a
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d
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.
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,
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b
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st
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b
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sel
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c
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n
:
f
r
o
m b
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a
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k
-
b
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x
t
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g
l
a
ss
-
b
o
x
,
”
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u
m
a
n
R
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p
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u
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o
n
,
v
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.
3
9
,
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2
,
p
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2
8
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.
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4
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:
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0
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3
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2
5
4
.
[
7
]
R
.
B
i
l
l
o
n
e
s,
J.
K
.
Li
w
a
n
g
,
K
.
B
u
t
l
e
r
,
L.
G
r
a
v
e
s
,
a
n
d
L.
N
.
S
a
l
i
g
a
n
,
“
D
i
ss
e
c
t
i
n
g
t
h
e
f
a
t
i
g
u
e
e
x
p
e
r
i
e
n
c
e
:
a
sc
o
p
i
n
g
r
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v
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e
w
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f
f
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t
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e
f
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d
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n
s
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s
,
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n
d
m
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s
i
n
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c
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l
o
g
i
c
me
d
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c
a
l
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d
i
t
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o
n
s,”
Bra
i
n
,
Be
h
a
v
i
o
r
,
&
I
m
m
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t
y
-
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,
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.
1
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.
b
b
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2
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6
6
.
[
8
]
T.
D
o
b
b
r
i
c
k
,
J.
Ja
k
o
b
,
C
.
-
H
.
C
h
a
n
,
a
n
d
H
.
W
e
ss
l
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r
,
“
E
n
h
a
n
c
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n
g
t
h
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y
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f
o
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d
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a
r
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c
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w
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g
l
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ss
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m
a
c
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l
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a
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n
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:
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Me
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Me
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.
[
9
]
Y
.
X
i
a
,
X
.
L
i
,
F
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