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14
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3
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1156
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ters
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m
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c
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K
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w
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s
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tific
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L
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Me
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Pre
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R
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CC B
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in
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s
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s
in
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s
[
1
]
.
Fu
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th
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m
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th
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g
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m
o
n
ito
r
an
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ass
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2
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3
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T
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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N:
2252
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8
7
7
6
Leve
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lth
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lytics
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P
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ma
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va
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1157
tech
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o
lo
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ab
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s
[
4
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.
Ov
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as
b
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a
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5
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.
A
m
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ML
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n
r
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s
,
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AI
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as
g
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d
p
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in
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m
ed
ical
f
ield
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ec
ially
in
i
lln
ess
d
etec
tio
n
an
d
p
r
ec
is
io
n
m
ed
ici
n
e
[
6
]
.
C
er
tain
m
o
d
els
h
av
e
attain
e
d
d
i
ag
n
o
s
is
ac
cu
r
ac
y
eq
u
i
v
alen
t
t
o
th
at
o
f
s
ea
s
o
n
ed
clin
ician
s
.
T
h
e
liter
atu
r
e
h
as
ex
ten
s
iv
ely
ex
am
i
n
ed
I
o
T
I
n
t
eg
r
atio
n
with
AI
f
o
r
h
ea
lth
m
o
n
ito
r
in
g
in
r
em
o
te
ar
ea
s
[
7
]
,
[
8
]
.
I
n
r
ec
en
t
y
ea
r
s
,
r
em
o
t
e
h
ea
lth
m
o
n
ito
r
in
g
,
lo
w
-
co
s
t
p
er
s
o
n
al
h
ea
lth
ca
r
e
d
ev
ices,
an
d
n
o
n
-
in
v
asiv
e
m
eth
o
d
s
o
f
d
is
ea
s
e
d
iag
n
o
s
is
h
av
e
s
u
r
f
ac
ed
as
v
iab
le
to
o
ls
f
o
r
en
h
an
cin
g
p
atien
t
ca
r
e
an
d
d
im
in
is
h
in
g
h
ea
lth
ca
r
e
e
x
p
en
d
itu
r
es
[
9
]
,
[
1
0
]
c
o
m
p
ar
ed
to
co
n
v
e
n
tio
n
al
h
ea
lt
h
ca
r
e
s
y
s
tem
s
.
I
o
T
-
e
n
ab
led
r
em
o
te
h
ea
lth
m
o
n
ito
r
in
g
an
d
wea
r
ab
le
s
m
ar
t
h
ea
lth
ca
r
e
d
ev
ices
o
f
f
er
s
ev
er
al
b
en
ef
its
[
1
1
]
.
Sp
ec
if
ically
,
th
e
lo
w
-
p
o
wer
wid
e
-
ar
ea
n
etwo
r
k
(
L
PW
AN)
d
esig
n
e
n
ab
les
th
e
ec
o
n
o
m
ical
co
n
n
ec
tio
n
o
f
wea
r
ab
le
d
e
v
ices
to
th
e
clo
u
d
ac
r
o
s
s
ex
ten
s
iv
e
d
is
tan
ce
s
[
1
2
]
.
A
d
v
a
n
c
e
m
e
n
t
s
i
n
A
I
e
n
a
b
l
e
t
h
e
a
n
a
l
y
s
is
a
n
d
i
n
t
e
r
p
r
e
ta
t
i
o
n
o
f
a
v
a
i
l
a
b
l
e
d
a
ta
t
o
f
o
r
m
u
l
a
t
e
h
y
p
o
t
h
e
s
e
s
a
n
d
e
x
t
r
a
c
t
s
i
g
n
i
f
i
c
a
n
t
i
n
s
i
g
h
ts
f
o
r
e
a
r
l
y
d
e
t
e
c
t
i
o
n
a
n
d
t
ai
l
o
r
ed
t
h
e
r
a
p
y
f
o
r
i
n
d
i
v
i
d
u
a
l
p
a
t
i
e
n
ts
[
1
3
]
.
T
h
i
s
p
r
o
j
e
ct
d
e
t
a
i
ls
t
h
e
d
e
s
i
g
n
a
n
d
e
x
e
c
u
ti
o
n
o
f
a
n
I
o
T
-
b
a
s
e
d
p
a
t
i
e
n
t
m
o
n
i
t
o
r
i
n
g
s
y
s
t
e
m
u
t
i
l
iz
i
n
g
lo
n
g
r
a
n
g
e
(
L
o
R
a
)
c
o
m
m
u
n
i
c
a
t
i
o
n
t
e
c
h
n
o
l
o
g
y
.
T
h
i
s
c
o
m
p
l
e
t
e
s
y
s
t
e
m
f
a
ci
l
it
a
t
es
f
a
l
l
d
e
t
e
c
t
i
o
n
a
n
d
q
u
a
n
t
i
f
i
es
,
e
x
h
i
b
i
t
s
,
a
n
d
s
e
n
d
s
e
s
s
e
n
t
i
a
l
h
e
a
l
t
h
m
e
t
r
i
cs
,
i
n
clu
d
i
n
g
h
e
a
r
t
r
a
t
e
a
n
d
b
l
o
o
d
o
x
y
g
e
n
s
a
t
u
r
a
t
i
o
n
le
v
e
l
s
b
o
d
y
t
e
m
p
e
r
a
t
u
r
e
,
a
n
d
e
l
e
c
t
r
o
c
a
r
d
i
o
g
r
a
m
(
E
C
G
)
.
T
h
e
s
u
c
c
e
s
s
f
u
l
i
m
p
le
m
e
n
t
a
ti
o
n
a
n
d
t
e
s
t
i
n
g
o
f
t
h
is
s
y
s
t
e
m
il
l
u
s
t
r
a
t
e
i
ts
e
f
f
i
c
a
c
y
i
n
r
e
a
l
-
t
i
m
e
p
a
t
ie
n
t
m
o
n
i
t
o
r
i
n
g
.
B
y
e
m
p
l
o
y
i
n
g
L
o
R
a
t
e
c
h
n
o
l
o
g
y
a
n
d
I
o
T
p
l
a
t
f
o
r
m
s
,
s
u
c
h
as
A
d
a
f
r
u
it
I
O
,
t
h
e
s
o
l
u
t
i
o
n
g
u
a
r
a
n
t
ee
s
t
h
a
t
r
e
a
l
-
tim
e
h
e
a
l
t
h
d
at
a
i
s
e
a
s
i
l
y
a
v
ai
l
ab
l
e
t
o
c
a
r
e
t
a
k
e
r
s
,
h
e
n
c
e
i
m
p
r
o
v
i
n
g
p
a
t
i
e
n
t
c
a
r
e
a
n
d
f
a
c
i
l
it
a
t
i
n
g
p
r
o
m
p
t
a
c
t
i
o
n
w
h
e
n
r
e
q
u
i
r
e
d
[
1
4
]
.
T
h
e
m
e
t
h
o
d
o
l
o
g
y
a
n
d
m
o
d
e
l
s
p
r
e
s
e
n
t
e
d
i
n
t
h
i
s
s
t
u
d
y
c
a
n
b
e
a
p
p
l
i
e
d
t
o
v
a
r
i
o
u
s
s
i
t
u
a
t
i
o
n
s
w
h
e
r
e
t
h
e
d
e
v
e
l
o
p
m
e
n
t
o
f
a
n
a
c
c
u
r
a
t
e
f
o
r
e
c
as
t
i
n
g
m
o
d
e
l
f
o
r
t
i
m
e
-
s
e
r
i
es
d
a
t
a
i
s
e
s
s
e
n
t
i
a
l
.
T
h
i
s
w
o
r
k
c
o
n
t
r
i
b
u
t
es
t
o
t
h
e
r
a
p
i
d
l
y
g
r
o
w
i
n
g
b
o
d
y
o
f
l
i
t
e
r
a
t
u
r
e
t
h
a
t
u
s
es
ML
t
e
c
h
n
i
q
u
e
s
i
n
h
e
al
t
h
c
a
r
e
p
r
e
d
i
c
t
i
o
n
s
[
1
5
]
.
F
o
r
i
n
s
t
a
n
c
e
,
t
h
e
r
is
k
o
f
i
l
l
n
es
s
i
n
it
i
a
ti
o
n
,
i
n
c
l
u
d
i
n
g
c
a
r
d
i
o
v
a
s
c
u
l
a
r
c
o
n
d
i
t
i
o
n
s
,
a
r
r
h
y
t
h
m
i
a
p
r
e
v
e
n
t
i
o
n
,
d
e
t
e
c
t
i
o
n
o
f
h
e
a
r
t
d
i
s
o
r
d
e
r
s
,
o
r
as
s
e
s
s
m
e
n
t
o
f
m
o
r
ta
l
i
t
y
r
is
k
i
n
i
n
d
i
v
i
d
u
a
ls
w
h
o
h
av
e
a
h
e
a
r
t
a
t
ta
c
k
i
n
t
h
e
p
r
e
c
e
d
i
n
g
y
e
a
r
[
1
6
]
.
L
o
R
a,
a
l
o
w
-
p
o
w
e
r
wi
d
e
a
r
e
a
n
e
two
r
k
(
L
P
W
A
N
)
,
i
s
f
a
v
o
r
e
d
i
n
h
e
a
l
t
h
c
a
r
e
r
e
s
e
a
r
c
h
b
e
c
a
u
s
e
o
f
i
t
s
c
o
s
t
-
e
f
f
e
c
ti
v
e
n
e
s
s
,
e
n
e
r
g
y
e
f
f
i
c
ie
n
c
y
,
a
n
d
ex
t
e
n
s
i
v
e
r
a
n
g
e
c
a
p
a
b
i
li
t
i
es
[
1
7
]
.
D
e
p
l
o
y
i
n
g
I
o
T
s
o
l
u
t
i
o
n
s
u
t
i
li
z
i
n
g
L
o
R
a
-
b
a
s
e
d
s
e
n
s
o
r
s
a
n
d
g
a
t
e
w
a
y
s
f
a
c
il
i
t
at
e
s
o
n
g
o
i
n
g
s
u
r
v
e
i
ll
a
n
c
e
o
f
h
ig
h
-
r
i
s
k
p
a
t
i
e
n
ts
o
r
v
i
t
a
l
s
y
s
t
e
m
s
,
g
u
a
r
a
n
t
e
ei
n
g
t
h
at
h
e
a
l
t
h
a
n
d
m
e
d
i
c
al
s
a
f
e
t
y
a
r
e
p
r
i
o
r
i
t
i
z
e
d
wi
t
h
o
u
t
a
n
y
l
a
p
s
e
s
[
1
8
]
,
[
1
9
]
.
R
esear
ch
er
s
h
av
e
o
n
ly
p
ar
tly
ex
p
lo
r
ed
th
e
u
s
e
o
f
L
o
R
a
tech
n
o
lo
g
y
to
b
u
ild
lo
w
-
co
s
t
wea
r
ab
le
d
ev
ices
with
in
te
g
r
ated
I
o
T
a
n
d
ML
[
2
0
]
,
[
2
1
]
.
T
h
e
u
s
e
o
f
ML
to
s
im
u
ltan
eo
u
s
ly
m
o
d
el
an
d
p
r
e
d
ict
h
ea
r
t
r
ate,
Sp
O2
,
tem
p
e
r
atu
r
e,
b
o
d
y
r
esis
tan
ce
,
an
d
m
an
y
o
th
e
r
v
it
al
p
ar
am
eter
lev
els
is
u
n
d
er
ex
p
lo
r
ed
.
Als
o
,
f
r
o
m
th
e
liter
atu
r
e,
it
is
ev
id
en
t
th
a
t
p
r
ee
x
is
tin
g
ML
m
o
d
els
wer
e
b
u
ilt
u
s
in
g
p
u
b
licly
av
ailab
l
e
d
atasets
,
n
o
t
d
ata
f
r
o
m
e
d
g
e
n
o
d
es.
I
n
o
r
d
er
to
b
u
ild
a
h
ea
lth
ca
r
e
s
y
s
tem
th
a
t
is
b
o
th
ef
f
icien
t
an
d
in
ex
p
e
n
s
iv
e.
T
h
e
p
r
esen
t
s
tu
d
y
ap
p
lies
m
an
y
ML
alg
o
r
ith
m
s
to
d
ata
ac
q
u
i
r
ed
f
r
o
m
s
e
n
s
o
r
n
o
d
es.
2.
M
AT
E
R
I
AL
S AN
D
P
RO
P
O
SE
D
D
E
S
I
G
N
2
.
1
.
Sens
o
rs a
nd
I
o
T
dev
ices
T
h
e
h
ea
lth
m
o
n
ito
r
in
g
s
y
s
tem
u
tili
zin
g
I
o
T
an
d
L
o
R
a
is
co
m
p
r
is
ed
o
f
two
u
n
i
q
u
e
co
m
p
o
n
en
ts
:
th
e
tr
an
s
m
itter
,
wh
ich
f
ac
ilit
ates
b
o
th
I
o
T
an
d
L
o
R
a
co
n
n
ec
tio
n
,
an
d
th
e
r
ec
eiv
e
r
,
wh
ich
ex
clu
s
iv
ely
s
u
p
p
o
r
ts
L
o
R
a
co
m
m
u
n
icatio
n
.
T
h
e
d
esig
n
em
u
lated
a
p
atien
t
m
o
n
ito
r
in
g
s
y
s
tem
,
wh
er
ein
t
h
e
tr
a
n
s
m
itter
wo
u
ld
ass
es
s
th
e
p
atien
t'
s
v
ital
p
ar
am
eter
s
,
u
p
lo
ad
th
e
r
ea
d
in
g
s
t
o
a
n
o
n
lin
e
Ad
af
r
u
it
p
latf
o
r
m
,
a
n
d
tr
an
s
m
it
th
e
d
ata
to
th
e
d
o
cto
r
'
s
m
o
n
ito
r
in
g
c
h
an
n
el
v
ia
L
o
R
a
co
m
m
u
n
ic
atio
n
tech
n
o
lo
g
y
.
E
m
e
r
g
en
c
y
n
o
tific
atio
n
s
ar
e
tr
an
s
m
itted
u
n
if
o
r
m
ly
to
th
e
r
ec
ip
ien
t.
I
n
clu
d
ed
m
ater
ials
ar
e
a
m
icr
o
co
n
tr
o
ller
E
SP
3
2
,
a
p
o
wer
s
u
p
p
ly
m
o
d
u
le,
a
MA
X3
0
1
0
0
p
u
ls
e
o
x
y
g
e
n
s
en
s
o
r
,
a
n
MPU6
0
5
0
ac
ce
ler
o
m
eter
,
a
b
o
d
y
tem
p
e
r
atu
r
e
s
en
s
o
r
,
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id
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tific
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.
Fin
ally
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L
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R
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f
o
r
s
eq
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en
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[
2
4
]
.
Me
m
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atin
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2
5
]
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ac
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if
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ac
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tics
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licated
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eq
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e
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tial
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elativ
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cc
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r
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s
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ti
c
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ed
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icac
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ter
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as in
(
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.
T
h
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f
o
r
m
u
la
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ca
lcu
latin
g
r
elativ
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a
cc
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r
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is
:
=
(
1
−
∑
|
−
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|
1
∑
|
|
1
)
(
1
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
14
,
No
.
3
,
Dec
em
b
er
20
25
:
1
1
5
6
-
1
1
6
2
1160
4
.
RE
SU
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T
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p
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s
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lu
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(
MA
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r
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m
ea
n
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q
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(
R
MSE
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d
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ac
cu
r
ac
y
to
d
eter
m
in
e
th
e
o
p
tim
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m
o
d
el
f
o
r
p
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ed
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u
m
an
v
ital
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a
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eter
s
.
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h
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m
etr
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m
p
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ted
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m
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el'
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er
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teg
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teg
o
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ataset.
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ac
h
p
ar
ticip
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t
h
ad
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d
ee
p
lear
n
i
n
g
alg
o
r
ith
m
r
u
n
5
0
tim
es.
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h
e
ANN
m
o
d
el
was
d
e
t
e
r
m
i
n
e
d
t
o
h
a
v
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t
h
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h
i
g
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s
t
p
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r
f
o
r
m
a
n
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e
;
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l
t
h
o
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g
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t
h
e
m
o
d
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l
s
p
r
o
d
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y
c
o
m
p
a
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l
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f
i
n
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i
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g
s
,
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a
b
l
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illu
s
tr
ates th
e
o
x
y
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en
s
atu
r
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n
,
h
ea
r
t r
ate,
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d
b
o
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esis
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ce
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els f
o
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f
r
o
m
th
e
f
o
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r
m
o
d
els ap
p
lied
to
a
s
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p
ar
ticip
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t'
s
test
s
et
.
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m
T
ab
le
2
,
th
e
r
esu
lts
o
f
th
e
s
im
u
latio
n
s
tu
d
y
s
u
g
g
est
th
at
th
e
ANN
m
o
d
el
g
en
er
ate
d
a
m
o
r
e
p
r
ec
is
e
f
o
r
e
ca
s
t
th
an
th
e
Naïv
e,
C
o
n
v
1
D,
an
d
L
STM
m
o
d
el
s
in
r
elatio
n
to
th
e
p
u
ls
e
r
ate
o
x
y
g
e
n
s
atu
r
atio
n
lev
els
an
d
b
o
d
y
r
esis
tan
ce
o
f
p
ar
ticip
an
t.
T
h
e
r
esu
lts
in
d
ica
ted
th
at
t
h
e
ANN
m
o
d
el
o
u
t
p
er
f
o
r
m
e
d
th
e
o
t
h
er
m
o
d
els,
r
esu
ltin
g
in
a
m
o
r
e
p
r
ec
is
e
f
o
r
ec
ast
with
lo
wer
R
M
SE,
an
d
g
r
ea
ter
a
cc
u
r
ac
y
.
Fig
u
r
e
3
d
ep
icts
r
esu
lts
o
b
tain
ed
f
r
o
m
ANN
ar
ch
itect
u
r
e
wh
ich
f
o
r
ec
asted
th
e
h
ea
r
t
r
ate
an
d
Sp
O
2
lev
els
f
o
r
s
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g
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p
ar
ticip
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t.
T
h
is
r
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ch
co
m
p
ar
ed
t
h
e
p
r
ed
i
ctio
n
s
o
f
f
o
u
r
m
o
d
els
ANN,
C
NN,
NB
,
an
d
L
STM
f
o
r
h
u
m
an
v
ital
d
ata.
A
lth
o
u
g
h
all
f
o
u
r
ML
m
o
d
els
p
er
f
o
r
m
ed
eq
u
ally
,
th
e
f
in
d
in
g
s
s
h
o
w
th
at
ANN
p
r
ed
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n
s
o
u
tp
er
f
o
r
m
ed
th
e
co
m
p
etitio
n
b
y
a
s
tatis
tically
s
ig
n
if
ican
t m
ar
g
in
.
T
ab
le
2
.
Per
f
o
r
m
an
ce
m
etr
ics o
f
test
ed
alg
o
r
ith
m
s
V
i
t
a
l
p
a
r
a
m
e
t
e
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l
g
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t
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m
n
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me
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A
E
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M
S
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A
c
c
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r
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e
a
r
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7
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2
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1
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4
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4
5
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8
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p
O
2
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0
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7
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3
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2
4
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2
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0
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8
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2
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4
1
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7
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4
0
6
B
o
d
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r
e
s
i
st
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n
c
e
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1
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6
2
8
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1
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2
3
4
2
9
8
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3
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5
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1
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4
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1
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5
N
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v
e
m
o
d
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l
4
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4
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2
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5
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2
9
3
8
Fig
u
r
e
3
.
ANN
f
o
r
ec
asted
p
lo
t
f
o
r
h
e
ar
t r
ate
an
d
Sp
O2
f
o
r
s
i
n
g
le
p
ar
ticip
an
t
5.
CO
NCLU
SI
O
N
Usi
n
g
th
e
I
o
T
a
n
d
d
ee
p
lear
n
i
n
g
,
o
u
r
f
in
d
in
g
s
co
n
clu
d
e
wit
h
a
n
ew
p
a
r
ad
ig
m
f
o
r
h
ea
lth
m
o
n
ito
r
in
g
.
B
ased
o
n
r
ea
l
-
tim
e
d
ata
o
b
tain
ed
f
r
o
m
a
lo
w
-
co
s
t
d
ev
ic
e
p
r
o
to
ty
p
e
d
u
s
in
g
L
o
R
a
as
a
co
m
m
u
n
icatio
n
n
etwo
r
k
,
th
is
s
tu
d
y
h
as
s
ev
er
al
p
o
ten
tial
ex
p
an
s
io
n
s
to
m
o
n
ito
r
v
a
r
io
u
s
h
e
alth
p
a
r
am
et
er
s
.
Fu
r
th
er
,
th
ese
m
o
d
els
ca
n
b
e
u
tili
ze
d
to
ass
ess
th
e
q
u
ality
o
f
life
o
f
em
p
lo
y
ee
s
in
s
p
ec
if
ic
d
ep
ar
tm
en
ts
o
f
a
co
m
p
an
y
an
d
m
ak
e
a
d
ju
s
tm
en
ts
to
t
h
eir
d
ai
ly
r
o
u
tin
es
b
ased
o
n
th
eir
p
h
y
s
io
lo
g
ical
r
ea
ctio
n
s
to
s
tr
ess
,
wh
ich
is
a
well
-
k
n
o
wn
co
r
r
elatio
n
with
h
e
ar
t
r
ate,
b
l
o
o
d
o
x
y
g
en
lev
el,
a
n
d
o
v
e
r
all
well
-
b
ein
g
.
T
h
e
n
ee
d
to
s
af
eg
u
ar
d
th
e
co
n
f
id
en
tiality
a
n
d
in
te
g
r
ity
o
f
h
ea
lth
d
ata
is
g
r
o
win
g
in
tan
d
em
with
t
h
e
am
o
u
n
t
o
f
d
ata
g
ath
e
r
ed
an
d
tr
an
s
f
er
r
ed
v
ia
I
o
T
d
e
v
ices.
T
h
er
ef
o
r
e
,
we
m
ay
d
ir
ec
t f
u
tu
r
e
s
tu
d
ies to
cr
ea
te
s
tr
o
n
g
p
ar
ad
i
g
m
s
f
o
r
im
p
r
o
v
in
g
th
e
s
af
ety
an
d
c
o
n
f
id
e
n
tiality
o
f
m
ed
ical
r
ec
o
r
d
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Leve
r
a
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w
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a
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fo
r
p
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h
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a
lth
ca
r
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a
n
a
lytics
(
P
illa
la
ma
r
r
i La
va
n
ya
)
1161
F
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eq
u
est.
RE
F
E
R
E
NC
E
S
[
1
]
E.
M
o
g
h
a
d
a
s
,
J
.
R
e
z
a
z
a
d
e
h
,
a
n
d
R
.
F
a
r
a
h
b
a
k
h
sh
,
“
A
n
I
o
T
p
a
t
i
e
n
t
mo
n
i
t
o
r
i
n
g
b
a
se
d
o
n
f
o
g
c
o
m
p
u
t
i
n
g
a
n
d
d
a
t
a
mi
n
i
n
g
:
c
a
r
d
i
a
c
a
r
r
h
y
t
h
mi
a
u
sec
a
se
,
”
I
n
t
e
rn
e
t
o
f
T
h
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g
s
,
v
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l
.
1
1
,
p
.
1
0
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5
1
,
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e
p
.
2
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,
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0
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1
6
/
j
.
i
o
t
.
2
0
2
0
.
1
0
0
2
5
1
.
[
2
]
S
.
L.
U
l
l
o
a
n
d
G
.
R
.
S
i
n
h
a
,
“
A
d
v
a
n
c
e
s
i
n
smar
t
e
n
v
i
r
o
n
m
e
n
t
m
o
n
i
t
o
r
i
n
g
s
y
st
e
ms
u
s
i
n
g
I
o
T
a
n
d
s
e
n
so
r
s,
”
S
e
n
s
o
r
s
,
v
o
l
.
2
0
,
n
o
.
1
1
,
p
.
3
1
1
3
,
M
a
y
2
0
2
0
,
d
o
i
:
1
0
.
3
3
9
0
/
s2
0
1
1
3
1
1
3
.
[
3
]
P
.
La
v
a
n
y
a
,
D
.
I
.
V
.
S
.
R
e
d
d
y
,
D
.
V
.
S
e
l
v
a
k
u
mar,
a
n
d
S
.
V
D
e
sh
p
a
n
d
e
,
“
A
n
i
n
t
e
l
l
i
g
e
n
t
h
e
a
l
t
h
s
u
r
v
e
i
l
l
a
n
c
e
s
y
st
e
m
:
p
r
e
d
i
c
t
i
v
e
mo
d
e
l
i
n
g
o
f
c
a
r
d
i
o
v
a
sc
u
l
a
r
p
a
r
a
me
t
e
r
s
t
h
r
o
u
g
h
m
a
c
h
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n
e
l
e
a
r
n
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n
g
a
l
g
o
r
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t
h
ms
u
s
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n
g
l
o
r
a
c
o
mm
u
n
i
c
a
t
i
o
n
a
n
d
i
n
t
e
r
n
e
t
o
f
me
d
i
c
a
l
t
h
i
n
g
s
(
I
o
M
T)
,
”
J
o
u
rn
a
l
o
f
I
n
t
e
r
n
e
t
S
e
rv
i
c
e
s
a
n
d
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n
f
o
rm
a
t
i
o
n
S
e
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u
ri
t
y
,
v
o
l
.
1
4
,
n
o
.
1
,
p
p
.
1
6
5
–
1
7
9
,
M
a
r
.
2
0
2
4
,
d
o
i
:
1
0
.
5
8
3
4
6
/
j
i
s
i
s.
2
0
2
4
.
i
1
.
0
1
1
.
[
4
]
L.
S
.
K
o
n
d
a
k
a
,
M
.
T
h
e
n
m
o
z
h
i
,
K
.
V
i
j
a
y
a
k
u
m
a
r
,
a
n
d
R
.
K
o
h
l
i
,
“
A
n
i
n
t
e
n
si
v
e
h
e
a
l
t
h
c
a
r
e
mo
n
i
t
o
r
i
n
g
p
a
r
a
d
i
g
m
b
y
u
si
n
g
I
o
T
b
a
se
d
mac
h
i
n
e
l
e
a
r
n
i
n
g
st
r
a
t
e
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i
e
s,
”
M
u
l
t
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m
e
d
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a
T
o
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s
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n
d
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p
p
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c
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t
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n
s
,
v
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l
.
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1
,
n
o
.
2
6
,
p
p
.
3
6
8
9
1
–
3
6
9
0
5
,
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n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
0
4
2
-
0
2
1
-
1
1
1
1
1
-
8.
[
5
]
A
.
M
.
R
a
h
ma
n
i
,
W
.
S
z
u
-
H
a
n
,
K
.
Y
u
-
H
su
a
n
,
a
n
d
M
.
H
a
g
h
p
a
r
a
st
,
“
T
h
e
i
n
t
e
r
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e
t
o
f
t
h
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n
g
s
f
o
r
a
p
p
l
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c
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t
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o
n
s
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n
w
e
a
r
a
b
l
e
t
e
c
h
n
o
l
o
g
y
,
”
I
EEE
A
c
c
e
ss
,
v
o
l
.
1
0
,
p
p
.
1
2
3
5
7
9
–
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2
3
5
9
4
,
2
0
2
2
,
d
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i
:
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0
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1
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9
/
a
c
c
e
ss.
2
0
2
2
.
3
2
2
4
4
8
7
.
[
6
]
A
.
I
.
P
a
g
a
n
e
l
l
i
e
t
a
l
.
,
“
A
c
o
n
c
e
p
t
u
a
l
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o
T
-
b
a
se
d
e
a
r
l
y
-
w
a
r
n
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n
g
a
r
c
h
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t
e
c
t
u
r
e
f
o
r
r
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mo
t
e
m
o
n
i
t
o
r
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n
g
o
f
C
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V
I
D
-
1
9
p
a
t
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e
n
t
s
i
n
w
a
r
d
s
a
n
d
a
t
h
o
me,
”
I
n
t
e
rn
e
t
o
f
T
h
i
n
g
s
,
v
o
l
.
1
8
,
p
.
1
0
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3
9
9
,
M
a
y
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,
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o
i
:
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0
1
6
/
j
.
i
o
t
.
2
0
2
1
.
1
0
0
3
9
9
.
[
7
]
M
.
S
a
a
r
i
,
A
.
M
.
b
i
n
B
a
h
a
r
u
d
i
n
,
P
.
S
i
l
l
b
e
r
g
,
S
.
H
y
r
y
n
s
a
l
m
i
,
a
n
d
W
.
Y
a
n
,
“
Lo
R
a
—
a
su
r
v
e
y
o
f
r
e
c
e
n
t
r
e
sea
r
c
h
t
r
e
n
d
s,
”
i
n
2
0
1
8
4
1
s
t
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
v
e
n
t
i
o
n
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n
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n
f
o
rm
a
t
i
o
n
a
n
d
C
o
m
m
u
n
i
c
a
t
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o
n
T
e
c
h
n
o
l
o
g
y
,
El
e
c
t
r
o
n
i
c
s
a
n
d
Mi
c
r
o
e
l
e
c
t
r
o
n
i
c
s
(
MIPRO
)
,
M
a
y
2
0
1
8
,
p
p
.
0
8
7
2
–
0
8
7
7
,
d
o
i
:
1
0
.
2
3
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1
9
/
mi
p
r
o
.
2
0
1
8
.
8
4
0
0
1
6
1
.
[
8
]
Q
.
Zh
o
u
,
K
.
Z
h
e
n
g
,
L.
H
o
u
,
J
.
X
i
n
g
,
a
n
d
R
.
X
u
,
“
D
e
si
g
n
a
n
d
i
mp
l
e
m
e
n
t
a
t
i
o
n
o
f
o
p
e
n
L
o
R
a
f
o
r
I
o
T,
”
I
EE
E
A
c
c
e
ss
,
v
o
l
.
7
,
p
p
.
1
0
0
6
4
9
–
1
0
0
6
5
7
,
2
0
1
9
,
d
o
i
:
1
0
.
1
1
0
9
/
a
c
c
e
ss
.
2
0
1
9
.
2
9
3
0
2
4
3
.
[
9
]
B
.
S
u
mat
h
y
,
S
.
K
a
v
i
mu
l
l
a
i
,
S
.
S
h
u
s
h
mi
t
h
a
a
,
a
n
d
S
.
S
.
A
n
u
s
h
a
,
“
W
e
a
r
a
b
l
e
n
o
n
-
i
n
v
a
s
i
v
e
h
e
a
l
t
h
mo
n
i
t
o
r
i
n
g
d
e
v
i
c
e
f
o
r
e
l
d
e
r
l
y
u
s
i
n
g
I
O
T,
”
I
O
P
C
o
n
f
e
re
n
c
e
S
e
ri
e
s:
Ma
t
e
ri
a
l
s
S
c
i
e
n
c
e
a
n
d
En
g
i
n
e
e
r
i
n
g
,
v
o
l
.
1
0
1
2
,
n
o
.
1
,
p
.
1
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0
1
1
,
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a
n
.
2
0
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1
,
d
o
i
:
1
0
.
1
0
8
8
/
1
7
5
7
-
8
9
9
x
/
1
0
1
2
/
1
/
0
1
2
0
1
1
.
[
1
0
]
Z.
U
.
A
h
m
e
d
,
M
.
G
.
M
o
r
t
u
z
a
,
M
.
J
.
U
d
d
i
n
,
M
.
H
.
K
a
b
i
r
,
M
.
M
a
h
i
u
d
d
i
n
,
a
n
d
M
.
D
.
J.
H
o
q
u
e
,
“
I
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
b
a
s
e
d
p
a
t
i
e
n
t
h
e
a
l
t
h
m
o
n
i
t
o
r
i
n
g
sy
s
t
e
m
u
si
n
g
w
e
a
r
a
b
l
e
b
i
o
m
e
d
i
c
a
l
d
e
v
i
c
e
,
”
i
n
2
0
1
8
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
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n
n
o
v
a
t
i
o
n
i
n
En
g
i
n
e
e
ri
n
g
a
n
d
T
e
c
h
n
o
l
o
g
y
(
I
C
I
ET)
,
D
e
c
.
2
0
1
8
,
p
p
.
1
–
5
,
d
o
i
:
1
0
.
1
1
0
9
/
c
i
e
t
.
2
0
1
8
.
8
6
6
0
8
4
6
.
[
1
1
]
Y
.
W
a
n
g
e
t
a
l
.
,
“
R
e
c
e
n
t
a
d
v
a
n
c
e
me
n
t
s
i
n
f
l
e
x
i
b
l
e
a
n
d
w
e
a
r
a
b
l
e
s
e
n
s
o
r
s
f
o
r
b
i
o
me
d
i
c
a
l
a
n
d
h
e
a
l
t
h
c
a
r
e
a
p
p
l
i
c
a
t
i
o
n
s
,
”
J
o
u
r
n
a
l
o
f
Ph
y
s
i
c
s
D
:
A
p
p
l
i
e
d
P
h
y
si
c
s
,
v
o
l
.
5
5
,
n
o
.
1
3
,
p
.
1
3
4
0
0
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,
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e
c
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
8
8
/
1
3
6
1
-
6
4
6
3
/
a
c
3
c
7
3
.
[
1
2
]
S
.
M
i
r
j
a
l
a
l
i
,
S
.
P
e
n
g
,
Z.
F
a
n
g
,
C
.
W
a
n
g
,
a
n
d
S
.
W
u
,
“
W
e
a
r
a
b
l
e
se
n
s
o
r
s
f
o
r
r
e
mo
t
e
h
e
a
l
t
h
m
o
n
i
t
o
r
i
n
g
:
p
o
t
e
n
t
i
a
l
a
p
p
l
i
c
a
t
i
o
n
s
f
o
r
e
a
r
l
y
d
i
a
g
n
o
si
s
o
f
C
O
V
I
D
‐
1
9
,
”
Ad
v
a
n
c
e
d
M
a
t
e
ri
a
l
s
T
e
c
h
n
o
l
o
g
i
e
s
,
v
o
l
.
7
,
n
o
.
1
,
S
e
p
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
2
/
a
d
mt
.
2
0
2
1
0
0
5
4
5
.
[
1
3
]
K
.
T.
K
a
d
h
i
m
,
A
.
M
.
A
l
s
a
h
l
a
n
y
,
S
.
M
.
W
a
d
i
,
a
n
d
H
.
T.
K
a
d
h
u
m,
“
A
n
o
v
e
r
v
i
e
w
o
f
p
a
t
i
e
n
t
’
s h
e
a
l
t
h
st
a
t
u
s m
o
n
i
t
o
r
i
n
g
sy
s
t
e
m
b
a
se
d
o
n
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
(
I
o
T)
,
”
Wi
r
e
l
e
ss
Pe
rso
n
a
l
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
1
1
4
,
n
o
.
3
,
p
p
.
2
2
3
5
–
2
2
6
2
,
M
a
y
2
0
2
0
,
d
o
i
:
1
0
.
1
0
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tatisti
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a
v
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k
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h
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d
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p
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c
a
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tac
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iv
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v
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m
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u
.
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