I
AE
S In
t
er
na
t
io
na
l J
o
urna
l o
f
Art
if
icia
l In
t
ellig
ence
(
I
J
-
AI
)
Vo
l.
1
5
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
6
,
p
p
.
1
~
11
I
SS
N:
2
2
5
2
-
8
9
3
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijai.v
15
.i
1
.
p
p
1
-
11
1
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
a
i
.
ia
esco
r
e.
co
m
Rev
iew of ar
tif
ici
a
l int
ellig
ence
in
s
ma
rt
wea
ra
ble
de
v
ices under
interne
t
o
f
things
co
mm
unica
tion
M
in
h L
o
ng
H
o
a
ng
,
G
uid
o
M
a
t
re
lla
,
P
a
o
lo
Cia
m
po
lin
i
D
e
p
a
r
t
me
n
t
o
f
En
g
i
n
e
e
r
i
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g
a
n
d
A
r
c
h
i
t
e
c
t
u
r
e
,
F
a
c
u
l
t
y
o
f
El
e
c
t
r
o
n
i
c
E
n
g
i
n
e
e
r
i
n
g
,
U
n
i
v
e
r
s
i
t
y
o
f
P
a
r
m
a
,
P
a
r
ma,
I
t
a
l
y
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
l 2
7
,
2
0
2
4
R
ev
is
ed
Oct
2
9
,
2
0
2
5
Acc
ep
ted
No
v
8
,
2
0
2
5
Th
is
p
a
p
e
r
a
ims
to
p
ro
v
id
e
a
re
v
iew
a
b
o
u
t
th
e
r
o
le
o
f
a
rti
ficia
l
in
telli
g
e
n
c
e
(AI)
in
we
a
ra
b
le
d
e
v
ice
s,
sp
e
c
ifi
c
a
ll
y
sm
a
rtwa
tch
e
s,
fit
n
e
ss
trac
k
e
rs,
sm
a
rt
c
lo
th
e
s,
a
n
d
sm
a
rt
e
y
e
we
a
r.
M
a
c
h
in
e
lea
rn
in
g
(M
L)
a
n
d
d
e
e
p
lea
rn
in
g
(DL)
p
lay
e
ss
e
n
ti
a
l
ro
les
in
t
h
e
d
e
v
e
lo
p
m
e
n
t
o
f
t
h
e
se
d
e
v
ice
s,
th
a
n
k
s
to
th
e
ir
a
d
v
a
n
c
e
d
a
l
g
o
rit
h
m
s
wit
h
t
h
e
su
p
p
o
rt
o
f
th
e
i
n
tern
e
t
o
f
th
i
n
g
s
(I
o
T)
fra
m
e
wo
rk
.
AI
f
u
n
c
ti
o
n
a
li
t
ies
a
n
d
m
e
tro
l
o
g
y
a
re
d
e
tailed
in
th
e
se
we
a
ra
b
les
,
h
ig
h
li
g
h
ti
n
g
t
h
e
u
se
o
f
c
o
n
v
o
l
u
ti
o
n
a
l
n
e
u
ra
l
n
e
two
r
k
s
(CNN
)
a
n
d
re
c
u
rre
n
t
n
e
u
ra
l
n
e
two
r
k
s
(RNN
)
f
o
r
a
p
p
l
i
c
a
ti
o
n
s
s
u
c
h
a
s
a
c
ti
v
it
y
re
c
o
g
n
it
i
o
n
,
h
e
a
lt
h
m
o
n
it
o
r
in
g
,
a
n
d
p
e
rso
n
a
li
z
e
d
re
c
o
m
m
e
n
d
a
ti
o
n
s.
Th
e
p
a
p
e
r
d
e
m
o
n
stra
tes
th
e
AI
imp
lem
e
n
tatio
n
i
n
sm
a
rt
d
e
v
i
c
e
s,
in
c
lu
d
in
g
stre
ss
d
e
tec
ti
o
n
b
y
h
e
a
rt
ra
te
v
a
riab
il
it
y
(HRV
)
,
p
e
rso
n
a
li
z
in
g
fit
n
e
ss
re
c
o
m
m
e
n
d
a
ti
o
n
s,
m
u
sc
l
e
a
c
ti
v
it
y
m
o
n
it
o
r
in
g
,
a
n
d
re
a
l
-
ti
m
e
ima
g
e
re
c
o
g
n
it
i
o
n
.
C
h
a
ll
e
n
g
e
s
a
n
d
p
o
ten
ti
a
l
so
lu
ti
o
n
s
a
re
d
isc
u
ss
e
d
f
o
r
a
d
e
e
p
c
o
m
p
re
h
e
n
sio
n
o
f
t
h
e
AI
d
e
v
e
l
o
p
m
e
n
t
in
we
a
ra
b
le d
e
v
ice
s.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
Dee
p
lear
n
in
g
I
n
ter
n
et
o
f
th
in
g
s
Ma
ch
in
e
lear
n
in
g
W
ea
r
ab
le
d
ev
ices
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Min
h
L
o
n
g
Ho
an
g
Dep
ar
tm
en
t o
f
E
n
g
in
ee
r
in
g
a
n
d
Ar
ch
itectu
r
e,
Facu
lty
o
f
E
le
ctr
o
n
ic
E
n
g
in
ee
r
in
g
,
Un
iv
er
s
it
y
o
f
Par
m
a
Par
m
a
4
3
1
2
4
,
I
tal
y
E
m
ail: m
in
h
lo
n
g
.
h
o
a
n
g
@
u
n
ip
r
.
it
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
in
teg
r
atio
n
o
f
ar
tific
ial
in
tellig
en
ce
(
AI
)
[
1
]
–
[
3
]
lik
e
m
ac
h
in
e
lear
n
in
g
(
ML
)
[
4
]
,
[
5
]
an
d
d
ee
p
lear
n
in
g
(
DL
)
[
6
]
,
[
7
]
with
wea
r
ab
le
tech
n
o
lo
g
y
:
s
m
ar
tw
atch
es,
f
itn
ess
tr
ac
k
er
s
,
s
m
ar
t
clo
th
es,
an
d
s
m
ar
t
ey
ewe
ar
h
as
b
r
o
u
g
h
t
ab
o
u
t
a
n
ew
er
a
o
f
i
n
n
o
v
atio
n
an
d
f
u
n
ctio
n
ality
in
th
is
f
ast
-
ch
an
g
i
n
g
f
ield
.
W
ea
r
ab
le
g
ad
g
ets,
s
m
o
o
th
l
y
in
co
r
p
o
r
ated
in
to
o
u
r
d
aily
r
o
u
tin
es,
p
lay
a
cr
u
cial
r
o
le
in
tr
ac
k
in
g
o
u
r
h
ea
lth
,
im
p
r
o
v
in
g
o
u
r
ef
f
icien
cy
,
a
n
d
en
r
ich
in
g
o
u
r
en
g
a
g
em
en
t
with
th
e
en
v
ir
o
n
m
en
t.
W
ith
in
th
e
co
n
tex
t
o
f
in
ter
n
et
o
f
th
i
n
g
s
(
I
o
T
)
co
n
n
ec
tiv
ity
,
n
etwo
r
k
e
d
n
o
d
es
ex
ch
a
n
g
e
d
ata
an
d
i
n
s
ig
h
ts
to
o
f
f
er
u
s
er
s
tailo
r
ed
an
d
co
n
te
x
tu
ally
r
elev
an
t e
x
p
e
r
ien
ce
s
.
Sm
ar
twatch
es
[
8
]
,
[
9
]
h
av
e
e
v
o
lv
ed
f
r
o
m
s
im
p
le
ac
ce
s
s
o
r
i
es
to
ad
v
an
ce
d
c
o
m
p
u
ter
d
ev
ices
with
m
an
y
s
en
s
o
r
s
[
1
0
]
an
d
f
u
n
ctio
n
s
.
ML
an
d
DL
alg
o
r
ith
m
s
in
s
m
ar
twatch
es
m
ea
s
u
r
e
f
u
n
d
a
m
en
tal
d
ata
s
u
ch
as
s
tep
s
an
d
ca
lo
r
ies
wh
ile
o
f
f
e
r
i
n
g
in
-
d
ep
th
in
s
ig
h
ts
ab
o
u
t
t
h
e
wea
r
er
'
s
h
ea
lth
an
d
b
e
h
av
io
r
.
T
h
ese
d
e
v
ices
u
s
e
ad
v
an
ce
d
an
aly
tics
to
p
r
o
v
id
e
u
s
er
s
with
v
alu
ab
le
in
f
o
r
m
atio
n
ab
o
u
t th
eir
h
ea
lth
,
s
u
ch
as h
ea
r
t r
ate
v
ar
iab
ilit
y
(
HR
V)
an
d
s
leep
p
atter
n
s
,
en
ab
lin
g
th
em
to
m
ak
e
in
f
o
r
m
ed
d
ec
is
io
n
s
ab
o
u
t
th
eir
well
-
b
ein
g
.
Fit
n
ess
tr
ac
k
er
s
[
1
1
]
,
s
im
ilar
to
s
m
ar
twatch
e
s
b
u
t
with
a
s
p
ec
if
ic
em
p
h
asis
o
n
h
ea
lth
an
d
ex
er
cise,
u
tili
ze
ML
an
d
DL
m
eth
o
d
s
to
p
r
ec
is
ely
id
e
n
tif
y
an
d
ca
teg
o
r
ize
p
h
y
s
ical
ac
tiv
ity
.
T
h
is
ty
p
e
o
f
d
e
v
ice
a
n
aly
s
es
d
ata
f
r
o
m
o
n
b
o
a
r
d
s
en
s
o
r
s
lik
e
ac
ce
ler
o
m
eter
s
an
d
g
y
r
o
s
co
p
es
[
1
2
]
t
o
d
if
f
er
en
tiate
b
etwe
en
ac
tiv
ities
s
u
ch
as
walk
in
g
,
r
u
n
n
in
g
,
cy
clin
g
,
an
d
s
wim
m
in
g
.
T
h
ey
o
f
f
e
r
u
s
er
s
co
m
p
r
e
h
en
s
iv
e
s
u
m
m
a
r
ies
o
f
th
eir
wo
r
k
o
u
ts
a
n
d
th
eir
ad
v
an
ce
m
e
n
t
to
war
d
s
f
itn
ess
o
b
jectiv
es.
ML
alg
o
r
ith
m
s
allo
w
tr
ac
k
er
s
to
ad
ju
s
t
to
i
n
d
iv
id
u
al
tast
es
an
d
b
eh
av
io
r
s
,
p
r
o
v
id
i
n
g
in
d
iv
id
u
alize
d
r
ec
o
m
m
en
d
atio
n
s
to
en
h
an
ce
wo
r
k
o
u
t
r
o
u
tin
es
an
d
im
p
r
o
v
e
o
u
tc
o
m
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
5
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
6
:
1
-
11
2
[
1
3
]
.
Sm
a
r
t
clo
th
es
[
1
4
]
,
[
1
5
]
,
s
u
ch
as
s
m
ar
t
s
o
ck
s
,
s
m
ar
t
p
a
n
ts
,
an
d
s
m
ar
t
s
h
ir
ts
,
ar
e
cr
ea
t
ed
b
y
i
n
co
r
p
o
r
atin
g
s
en
s
o
r
s
[
1
6
]
an
d
ac
tu
ato
r
s
in
t
o
clo
th
in
g
to
f
u
n
ctio
n
as
wea
r
ab
le
d
ev
ices
f
o
r
tr
ac
k
in
g
v
ital
s
ig
n
s
,
p
o
s
tu
r
e,
an
d
en
v
ir
o
n
m
en
tal
v
ar
iab
les.
ML
alg
o
r
ith
m
s
ar
e
ess
en
tial
f
o
r
an
aly
zin
g
th
e
d
ata
g
ath
er
e
d
b
y
s
en
s
o
r
s
in
s
m
ar
t
clo
th
in
g
to
o
f
f
e
r
im
m
ed
iate
f
e
ed
b
ac
k
o
n
co
r
r
ec
tin
g
p
o
s
tu
r
e,
m
o
n
ito
r
in
g
m
u
s
cle
ac
tiv
ity
,
a
n
d
en
s
u
r
in
g
th
er
m
al
co
m
f
o
r
t
[
1
7
]
.
DL
m
eth
o
d
s
allo
w
th
ese
clo
th
es
to
lear
n
an
d
ad
ju
s
t
to
th
e
wea
r
er
'
s
ac
tio
n
s
an
d
p
r
ef
er
e
n
ce
s
,
p
r
o
v
id
i
n
g
p
e
r
s
o
n
alize
d
ex
p
er
ien
ce
s
d
esig
n
ed
f
o
r
p
ar
ticu
la
r
r
eq
u
ir
e
m
en
ts
[
1
8
]
.
Sm
ar
t
e
y
ewe
ar
[
1
9
]
is
th
e
cu
ttin
g
-
ed
g
e
f
o
r
m
o
f
wea
r
ab
le
tech
n
o
lo
g
y
th
at
p
r
o
v
id
es
au
g
m
en
ted
r
ea
lity
(
AR
)
[
2
0
]
ex
p
e
r
ien
ce
s
,
h
an
d
s
-
f
r
ee
co
m
m
u
n
icatio
n
,
an
d
in
d
iv
id
u
a
lized
h
elp
i
n
d
if
f
er
en
t
s
itu
atio
n
s
.
ML
an
d
DL
al
g
o
r
ith
m
s
e
n
ab
le
f
u
n
ctio
n
alities
lik
e
g
estu
r
e
d
etec
tio
n
,
e
n
ab
li
n
g
u
s
er
s
to
o
p
e
r
ate
g
ad
g
ets
an
d
en
g
a
g
e
with
v
ir
tu
al
in
ter
f
ac
es
u
s
in
g
n
atu
r
al
g
estu
r
es
[
2
1
]
.
Fu
r
th
er
m
o
r
e,
th
ese
alg
o
r
ith
m
s
allo
w
s
m
ar
t
ey
ewe
ar
to
p
r
o
ce
s
s
v
is
u
al
d
ata
i
n
s
tan
tly
,
im
p
r
o
v
in
g
awa
r
en
ess
o
f
th
e
s
itu
atio
n
an
d
o
f
f
er
in
g
p
e
r
tin
en
t c
o
n
tex
tu
al
i
n
f
o
r
m
atio
n
ab
o
u
t th
e
u
s
er
'
s
en
v
ir
o
n
m
e
n
t.
C
en
tr
al
to
th
e
f
u
n
ctio
n
ality
o
f
th
ese
wea
r
ab
le
d
ev
ices
is
th
eir
ab
ilit
y
to
co
m
m
u
n
icate
an
d
ex
ch
an
g
e
d
ata
with
in
th
e
b
r
o
a
d
er
I
o
T
ec
o
s
y
s
tem
[
2
2
]
,
[
2
3
]
.
T
h
r
o
u
g
h
wir
eless
co
n
n
ec
tiv
ity
s
tan
d
ar
d
s
s
u
ch
as
B
lu
eto
o
th
,
Wi
-
Fi,
an
d
ce
llu
lar
n
etwo
r
k
s
,
s
m
ar
twatch
es,
f
itn
ess
tr
a
ck
er
s
,
s
m
ar
t
cl
o
th
es,
a
n
d
s
m
ar
t
ey
ewe
ar
ca
n
s
y
n
ch
r
o
n
ize
d
ata
with
co
m
p
a
n
io
n
ap
p
s
,
clo
u
d
-
b
ased
p
latf
o
r
m
s
,
an
d
o
th
e
r
co
n
n
ec
ted
d
e
v
ices.
ML
an
d
DL
alg
o
r
ith
m
s
o
p
tim
ize
d
ata
tr
an
s
m
is
s
io
n
an
d
p
r
o
ce
s
s
in
g
,
en
s
u
r
in
g
tim
ely
d
eliv
er
y
o
f
in
s
ig
h
ts
an
d
m
in
im
izin
g
laten
cy
f
o
r
a
s
ea
m
less
u
s
er
ex
p
er
ien
ce
.
I
n
ess
en
ce
,
th
e
in
teg
r
atio
n
o
f
ML
an
d
DL
in
s
m
ar
twatch
es,
f
itn
ess
tr
ac
k
er
s
,
s
m
ar
t
clo
th
es,
an
d
s
m
ar
t
ey
ewe
ar
u
n
d
e
r
th
e
u
m
b
r
ella
o
f
I
o
T
co
m
m
u
n
icatio
n
r
ep
r
es
en
ts
a
co
n
v
er
g
en
ce
o
f
cu
ttin
g
-
ed
g
e
tech
n
o
lo
g
ies
aim
ed
at
en
h
an
cin
g
h
u
m
an
c
ap
ab
ilit
ies
an
d
well
-
b
ein
g
.
T
h
e
d
ev
ice
p
er
f
o
r
m
a
n
ce
with
AI
ca
n
b
e
ev
alu
ated
with
s
p
ec
if
ic
m
etr
o
lo
g
ies.
As
th
ese
d
ev
ices
co
n
tin
u
e
to
ev
o
lv
e
an
d
in
n
o
v
ate,
d
r
iv
en
b
y
ad
v
an
ce
s
in
AI
an
d
co
n
n
ec
tiv
ity
,
th
e
y
h
o
ld
th
e
p
r
o
m
is
e
o
f
tr
an
s
f
o
r
m
in
g
h
o
w
we
m
o
n
ito
r
o
u
r
h
ea
lth
,
in
ter
ac
t w
i
th
tech
n
o
lo
g
y
,
an
d
ex
p
er
ien
ce
th
e
wo
r
ld
a
r
o
u
n
d
u
s
.
Ov
er
all,
th
is
r
esear
ch
p
r
o
v
id
es
th
e
f
o
llo
win
g
co
n
tr
i
b
u
tio
n
s
to
th
e
s
cien
tific
r
esear
ch
as f
o
llo
ws:
i)
T
h
is
ar
ticle
ex
p
lo
r
es
th
e
in
n
o
v
ativ
e
f
u
s
io
n
o
f
AI
,
wh
ich
en
co
m
p
ass
es
ML
an
d
DL
,
with
wea
r
ab
le
tech
n
o
lo
g
ies.
T
h
is
co
n
v
er
g
e
n
ce
m
ar
k
s
th
e
b
eg
in
n
in
g
o
f
a
n
ew
p
er
i
o
d
o
f
in
n
o
v
ati
o
n
,
in
wh
ic
h
s
m
ar
twatch
es,
f
itn
ess
tr
ac
k
er
s
,
s
m
ar
t
g
ar
m
en
ts
,
an
d
s
m
ar
t
e
y
eg
lass
es
n
o
t
o
n
ly
im
p
r
o
v
e
o
u
r
d
aily
liv
es
b
u
t a
ls
o
r
ev
o
lu
tio
n
ize
h
o
w
we
in
ter
ac
t w
ith
th
e
en
v
i
r
o
n
m
e
n
t
.
ii)
T
h
is
o
v
er
v
iew
p
r
o
v
id
es
th
e
wo
r
k
in
g
p
r
in
cip
le,
m
o
d
el
im
p
le
m
en
tatio
n
,
an
d
s
tr
u
ctu
r
e
o
f
A
I
in
wea
r
ab
l
e
d
ev
ices.
T
h
is
ar
ticle
ex
p
lo
r
es
th
e
tr
an
s
f
o
r
m
ativ
e
ca
p
ab
ilit
ies
o
f
AI
-
p
o
wer
ed
wea
r
a
b
les,
s
u
ch
as
s
m
ar
twatch
es
with
ad
v
an
ce
d
d
ata
an
aly
tics
th
at
ass
is
t
in
m
ak
in
g
in
f
o
r
m
e
d
d
ec
is
io
n
s
a
b
o
u
t
well
-
b
ein
g
an
d
f
itn
ess
tr
ac
k
er
s
th
at
p
r
o
v
i
d
e
p
er
s
o
n
alize
d
wo
r
k
o
u
t r
ec
o
m
m
en
d
atio
n
s
.
iii)
W
e
ex
p
lo
r
e
th
e
cu
ttin
g
-
ed
g
e
s
m
ar
t
clo
th
in
g
th
at
ad
a
p
ts
to
t
h
e
u
s
er
b
o
d
y
'
s
n
ee
d
s
,
en
s
u
r
in
g
co
m
f
o
r
t
an
d
im
p
r
o
v
in
g
p
o
s
tu
r
e,
an
d
th
e
f
u
tu
r
is
tic
s
m
ar
t
ey
ewe
ar
th
at
b
r
in
g
s
AR
in
to
e
v
er
y
d
a
y
u
s
e,
en
h
an
cin
g
s
itu
atio
n
al
awa
r
en
ess
an
d
in
te
r
ac
tio
n
th
r
o
u
g
h
n
atu
r
al
g
estu
r
es.
T
h
e
k
ey
f
ac
to
r
b
eh
in
d
th
es
e
p
r
o
g
r
ess
io
n
s
is
th
e
r
o
b
u
s
t
I
o
T
ec
o
s
y
s
tem
th
at
f
ac
ilit
ates
im
m
ed
iate
d
ata
s
h
ar
in
g
,
en
h
an
cin
g
th
e
ef
f
e
ctiv
en
ess
an
d
ag
ilit
y
o
f
th
ese
d
e
v
ices.
iv
)
T
h
is
co
m
p
r
eh
en
s
iv
e
ex
am
i
n
a
tio
n
n
o
t
o
n
ly
h
i
g
h
lig
h
ts
th
e
latest
tech
n
o
lo
g
ical
in
n
o
v
at
io
n
s
b
u
t
also
p
r
o
v
id
es
in
s
ig
h
ts
in
to
th
e
f
u
tu
r
e
tr
ajec
to
r
y
o
f
wea
r
a
b
le
tech
n
o
lo
g
y
.
W
ith
th
e
o
n
g
o
in
g
ad
v
an
ce
m
en
ts
in
AI
an
d
co
n
n
ec
tiv
ity
,
th
ese
i
n
tellig
en
t
d
ev
ices
h
o
ld
t
h
e
p
o
ten
tial
to
r
ev
o
lu
tio
n
ize
h
ea
l
th
m
o
n
ito
r
i
n
g
,
tech
n
o
lo
g
ical
e
n
g
ag
em
e
n
t,
an
d
o
u
r
g
en
er
al
p
er
ce
p
tio
n
o
f
th
e
wo
r
ld
.
T
h
e
p
ap
er
is
o
r
g
a
n
ized
as
f
o
llo
ws:
s
ec
t
io
n
1
is
ab
o
u
t
th
e
g
r
o
wth
s
tatis
tic
o
f
th
e
co
n
s
id
er
in
g
AI
wea
r
ab
le
d
ev
ice.
Sectio
n
2
d
escr
ib
es
ab
o
u
t
th
e
ML
/DL
m
eth
o
d
s
an
d
m
etr
o
l
o
g
y
in
th
ese
s
m
ar
t
wea
r
ab
le
d
ev
ices.
Sectio
n
3
a
r
e
ab
o
u
t
t
h
e
p
r
ac
tical
an
aly
s
is
with
ca
s
e
s
tu
d
y
an
d
r
ea
l
-
wo
r
ld
ap
p
licatio
n
with
I
o
T
.
T
h
e
n
,
s
ec
tio
n
4
will
d
is
cu
s
s
d
ata
p
r
iv
ac
y
an
d
s
ec
u
r
ity
.
Fin
ally
,
in
s
ec
tio
n
5
,
ch
allen
g
es,
s
o
lu
tio
n
s
,
an
d
f
u
tu
r
e
d
ir
ec
tio
n
s
ar
e
an
aly
z
ed
ap
p
r
o
p
r
iately
,
ac
co
m
p
a
n
ied
b
y
co
n
clu
s
io
n
s
at
th
e
en
d
.
2.
G
RO
WT
H
ST
AT
I
ST
I
C
O
F
SM
AR
T
W
E
ARA
B
L
E
D
E
V
I
CE
S
T
h
e
wo
r
ld
wid
e
m
a
r
k
et
f
o
r
we
ar
ab
le
AI
was e
s
tim
ated
at
US
D
2
1
.
2
b
illi
o
n
in
2
0
2
2
a
n
d
it i
s
p
r
o
jecte
d
to
in
cr
ea
s
e
at
a
co
m
p
o
u
n
d
a
n
n
u
al
g
r
o
wth
r
ate
(
C
AGR)
o
f
2
9
.
8
%
f
r
o
m
2
0
2
3
to
2
0
3
0
[
2
4
]
.
T
h
e
g
r
o
win
g
n
u
m
b
er
o
f
AI
-
en
a
b
led
s
m
ar
t
wea
r
ab
les
is
r
elate
d
to
th
e
ad
v
an
ce
m
en
t
o
f
I
o
T
,
with
th
e
w
ir
eless
tech
n
o
lo
g
y
’
s
in
co
r
p
o
r
atio
n
.
T
h
is
s
ec
tio
n
an
aly
ze
s
th
e
g
r
o
wth
o
f
s
m
ar
t
wea
r
ab
le
d
ev
ices
an
d
d
em
o
n
s
tr
ates
th
e
im
p
o
r
tan
t
r
o
les in
s
tatis
tic
s
.
2
.
1
.
S
m
a
rt
wa
t
ches
Sm
ar
twatch
es
ac
co
m
p
lis
h
ed
a
h
ig
h
r
e
v
en
u
e
s
h
ar
e
o
f
o
v
er
3
0
.
2
%
in
2
0
2
2
d
u
e
to
th
e
r
is
in
g
awa
r
en
ess
an
d
co
n
ce
r
n
am
o
n
g
co
n
s
u
m
er
s
r
eg
ar
d
in
g
p
eo
p
le’
s
h
ea
lth
.
Mo
r
eo
v
er
,
it
is
p
r
o
jecte
d
to
in
cr
ea
s
e
at
a
p
r
o
f
itab
le
p
ac
e
d
u
r
in
g
th
e
f
o
r
ec
asted
tim
ef
r
am
e
as
a
r
esu
lt
o
f
t
h
e
g
r
o
win
g
s
ig
n
if
ica
n
ce
o
f
in
ter
c
o
n
n
ec
ted
d
ev
ices
in
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
ev
iew
o
f a
r
tifi
cia
l in
tellig
en
ce
in
s
ma
r
t wea
r
a
b
le
d
ev
ices u
n
d
er in
tern
et
o
f th
in
g
s
…
(
Min
h
Lo
n
g
Ho
a
n
g
)
3
d
iv
er
s
e
s
ec
to
r
s
an
d
th
e
escalatin
g
d
em
an
d
f
o
r
a
co
n
n
ec
te
d
en
v
ir
o
n
m
en
t.
I
n
2
0
2
2
,
No
r
th
Am
er
ica
h
ad
th
e
h
ig
h
est
m
ar
k
et
s
h
ar
e
o
f
2
7
.
2
%
in
ter
m
s
o
f
r
ev
e
n
u
e
b
ec
au
s
e
o
f
th
e
in
v
estme
n
ts
m
ad
e
b
y
p
r
iv
ate
c
o
m
p
an
ies
an
d
th
e
s
u
p
p
o
r
t
o
f
g
o
v
e
r
n
m
e
n
t
p
r
o
g
r
am
s
to
p
r
o
m
o
te
th
e
u
s
e
o
f
AI
tech
n
o
lo
g
y
[
2
4
]
.
Ac
co
r
d
in
g
t
o
C
an
aly
s
r
esear
ch
[
2
5
]
,
th
er
e
will
b
e
a
s
ig
n
if
ican
t
in
cr
ea
s
e
in
s
m
ar
tw
atch
s
h
ip
m
en
ts
in
em
er
g
in
g
r
eg
io
n
s
,
p
a
r
ticu
lar
ly
in
th
e
Mid
d
le
E
ast
an
d
C
en
tr
a
l
an
d
E
aster
n
E
u
r
o
p
e,
with
a
p
r
o
jecte
d
g
r
o
wth
r
ate
o
f
2
7
a
n
d
2
2
%
r
esp
ec
tiv
ely
,
in
2
0
2
4
.
Pra
ctica
lly
,
th
e
in
teg
r
atio
n
o
f
AI
in
to
watc
h
es
h
as
b
r
o
u
g
h
t
a
s
ig
n
if
ican
t
ev
o
l
u
tio
n
o
f
th
is
d
ev
ice
co
n
s
u
m
p
tio
n
d
em
an
d
,
as r
ep
o
r
ted
in
T
ab
les 1
a
n
d
2
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R
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f a
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tern
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5
3.
AI M
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VIC
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ar
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3
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3
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e
d
i
f
f
e
r
e
n
t
a
c
t
i
v
i
ty
p
a
t
t
e
r
n
s
b
a
s
e
d
o
n
f
e
a
t
u
r
e
s
e
x
t
r
a
c
t
e
d
f
r
o
m
s
e
n
s
o
r
d
a
t
a
.
F
o
r
i
n
s
t
a
n
c
e
,
a
c
c
el
e
r
o
m
e
t
e
r
d
a
t
a
m
i
g
h
t
b
e
t
r
a
n
s
f
o
r
m
ed
u
s
i
n
g
te
c
h
n
i
q
u
e
s
s
u
c
h
as
F
o
u
r
i
e
r
t
r
a
n
s
f
o
r
m
o
r
w
a
v
e
l
e
t
t
r
a
n
s
f
o
r
m
t
o
e
x
t
r
a
c
t
t
im
e
-
f
r
e
q
u
e
n
c
y
f
e
a
t
u
r
e
s
,
w
h
i
c
h
a
r
e
t
h
e
n
u
s
e
d
b
y
t
h
e
c
l
a
s
s
i
f
ie
r
.
D
L
m
o
d
e
l
s
e
n
h
a
n
c
e
t
h
e
p
r
e
c
is
i
o
n
o
f
ac
t
i
v
it
y
r
e
c
o
g
n
i
t
i
o
n
i
n
v
a
r
i
e
d
a
n
d
c
h
a
n
g
i
n
g
s
et
t
i
n
g
s
b
y
e
x
t
r
a
ct
i
n
g
i
n
t
r
i
c
a
te
p
a
t
te
r
n
s
f
r
o
m
u
n
p
r
o
c
es
s
e
d
d
at
a
[
3
5
]
.
C
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s
(
C
N
N
)
[
3
6
]
an
d
r
e
c
u
r
r
e
n
t
n
e
u
r
a
l
n
e
t
w
o
r
k
s
(
R
NN
)
[
3
7
]
,
i
n
c
l
u
d
i
n
g
l
o
n
g
s
h
o
r
t
-
t
e
r
m
m
e
m
o
r
y
(
L
ST
M
)
n
e
t
w
o
r
k
[
3
8
]
,
a
r
e
p
a
r
t
i
c
u
la
r
l
y
e
f
f
e
c
ti
v
e
h
e
r
e
.
C
N
N
ca
n
a
u
t
o
m
a
t
i
ca
l
l
y
l
ea
r
n
s
p
a
t
i
al
h
i
e
r
a
r
c
h
i
es
o
f
f
e
a
t
u
r
es
f
r
o
m
r
a
w
s
e
n
s
o
r
d
a
t
a
(
e
.
g
.
,
m
u
l
t
i
-
d
i
m
e
n
s
i
o
n
a
l
t
i
m
e
-
s
e
r
i
e
s
d
a
t
a
f
r
o
m
a
cc
e
l
e
r
o
m
e
t
e
r
s
a
n
d
g
y
r
o
s
c
o
p
es
)
,
w
h
il
e
R
N
N
,
p
a
r
t
ic
u
l
a
r
l
y
L
S
T
M
a
n
d
g
a
t
e
d
r
e
c
u
r
r
e
n
t
u
n
its
(
G
R
Us
)
[
3
9
]
,
e
x
c
e
l
i
n
c
a
p
t
u
r
i
n
g
t
e
m
p
o
r
a
l
d
e
p
e
n
d
e
n
c
i
e
s
a
n
d
s
e
q
u
e
n
c
e
s
i
n
t
h
e
d
at
a
.
Sm
ar
twatch
es
with
h
ea
r
t
r
ate
m
o
n
ito
r
s
u
tili
ze
ML
tech
n
iq
u
es
to
ev
alu
ate
h
ea
r
t
r
ate
d
ata
to
p
r
o
v
id
e
in
s
ig
h
ts
o
n
s
tr
ess
lev
els,
r
ec
u
p
er
atio
n
,
an
d
ca
r
d
io
v
ascu
lar
h
e
alth
[
4
0
]
.
T
ec
h
n
iq
u
es su
ch
as K
-
m
ea
n
s
clu
s
ter
in
g
[
4
1
]
ca
n
b
e
u
s
ed
to
s
eg
m
en
t
h
ea
r
t
r
ate
d
ata
in
t
o
d
if
f
er
en
t
s
tates
(
e.
g
.
,
r
esti
n
g
,
m
o
d
er
ate
ac
tiv
ity
,
a
n
d
h
ig
h
ac
tiv
ity
)
b
ased
o
n
d
ata
ac
q
u
i
s
itio
n
m
eth
o
d
[
4
2
]
.
Ad
d
itio
n
ally
,
h
id
d
en
Ma
r
k
o
v
m
o
d
els
(
HM
Ms)
[
4
3
]
ca
n
m
o
d
el
th
e
s
eq
u
en
tial
n
at
u
r
e
o
f
HR
V
o
v
e
r
tim
e
to
in
f
e
r
u
n
d
er
ly
in
g
p
h
y
s
io
lo
g
ical
s
tates.
DL
m
o
d
els,
p
ar
ticu
lar
ly
t
h
o
s
e
in
v
o
lv
in
g
a
u
to
en
co
d
er
s
an
d
atten
tio
n
m
e
ch
an
is
m
s
,
ca
n
e
v
alu
ate
in
tr
ic
ate
ch
ar
ac
ter
is
tics
f
r
o
m
HR
V
d
ata
[
4
4
]
–
[
4
6
]
,
o
f
f
er
in
g
an
in
-
d
ep
th
u
n
d
e
r
s
tan
d
in
g
o
f
p
h
y
s
io
lo
g
ical
co
n
d
i
tio
n
s
an
d
p
o
ten
tial
h
ea
lth
co
n
ce
r
n
s
.
Au
to
e
n
co
d
e
r
s
ca
n
lear
n
a
co
m
p
r
ess
ed
r
ep
r
esen
tatio
n
o
f
HR
V
d
ata,
id
en
tify
in
g
s
u
b
tle
an
o
m
alies
in
d
icativ
e
o
f
h
ea
lth
is
s
u
es.
Atten
tio
n
m
ec
h
an
is
m
s
with
in
s
eq
u
en
ce
m
o
d
els
(
lik
e
t
r
an
s
f
o
r
m
er
s
)
ca
n
f
o
cu
s
o
n
c
r
itical
tim
e
p
o
in
ts
in
th
e
HR
V
d
ata,
im
p
r
o
v
in
g
th
e
ac
cu
r
ac
y
o
f
h
ea
lth
ass
ess
m
en
ts
.
3
.
2
.
Fi
t
nes
s
t
ra
ck
er
s
I
n
f
itn
ess
tr
ac
k
er
s
,
C
NN
is
h
ig
h
ly
p
r
o
f
icien
t
at
id
e
n
tify
in
g
co
m
p
licated
p
atter
n
s
in
r
aw
s
en
s
o
r
d
ata,
lead
in
g
to
im
p
r
o
v
ed
ac
cu
r
ac
y
in
r
ec
o
g
n
izin
g
ac
tiv
ities
ev
e
n
in
ch
allen
g
in
g
s
itu
atio
n
s
.
Fu
r
th
er
m
o
r
e,
ca
lo
r
ie
ex
p
en
d
itu
r
e
esti
m
atio
n
ca
n
b
e
im
p
r
o
v
e
d
b
y
u
tili
zin
g
M
L
alg
o
r
ith
m
s
th
at
c
o
m
b
in
e
ac
tiv
ity
d
ata
with
u
s
er
-
s
p
ec
if
ic
ch
ar
ac
ter
is
tics
lik
e
ag
e,
weig
h
t
,
an
d
h
ea
r
t
r
ate.
DL
alg
o
r
ith
m
s
ca
n
d
is
ce
r
n
in
tr
icate
co
n
n
ec
tio
n
s
b
etwe
en
s
en
s
o
r
in
p
u
ts
an
d
en
e
r
g
y
ex
p
en
d
itu
r
e,
r
esu
ltin
g
i
n
m
o
r
e
ac
cu
r
ate
ca
l
o
r
ie
b
u
r
n
p
r
e
d
ictio
n
s
cu
s
to
m
ized
f
o
r
ea
c
h
u
s
er
.
I
n
p
er
f
o
r
m
an
ce
o
p
tim
izatio
n
,
ML
alg
o
r
ith
m
s
ca
n
ass
ess
p
ast
ac
tiv
ity
d
ata
to
s
u
g
g
est
cu
s
to
m
ized
wo
r
k
o
u
t
p
r
o
g
r
a
m
s
b
ased
o
n
s
p
ec
if
ic
f
itn
ess
o
b
je
ctiv
es,
p
r
ef
er
e
n
ce
s
,
a
n
d
a
b
ilit
ies.
DL
m
o
d
els
ca
n
d
etec
t
r
elatio
n
s
h
ip
s
b
etwe
en
v
ar
io
u
s
s
o
r
ts
o
f
wo
r
k
o
u
ts
,
aid
in
g
u
s
er
s
in
ac
h
iev
in
g
a
well
-
r
o
u
n
d
ed
a
n
d
ef
f
icien
t
tr
ain
in
g
r
o
u
tin
e.
I
n
ju
r
y
p
r
e
v
en
tio
n
is
also
i
n
clu
d
ed
in
th
e
ML
f
u
n
ctio
n
,
wh
ic
h
i
d
en
tifie
s
tr
en
d
s
in
b
io
m
ec
h
an
ical
d
ata
f
r
o
m
p
h
y
s
ical
ac
tiv
ities
th
at
s
u
g
g
est p
o
o
r
f
o
r
m
o
r
a
h
ig
h
e
r
r
is
k
o
f
in
ju
r
y
.
R
NN,
in
clu
d
in
g
v
ar
ian
ts
lik
e
L
STM
n
etwo
r
k
,
o
f
f
er
s
r
ea
l
-
ti
m
e
f
ee
d
b
ac
k
an
d
c
o
ac
h
in
g
t
o
ass
is
t
u
s
er
s
in
m
ain
tain
in
g
g
o
o
d
tech
n
i
q
u
e
an
d
m
i
n
im
izin
g
th
e
r
is
k
o
f
in
ju
r
y
.
C
NN
ca
n
p
r
o
ce
s
s
s
p
atial
d
ata
f
r
o
m
wea
r
ab
les
to
id
en
tif
y
in
c
o
r
r
e
ct
m
o
v
em
e
n
t
p
atter
n
s
,
wh
ile
L
STM
s
ca
n
m
o
d
el
tem
p
o
r
a
l
d
ep
en
d
en
cies
to
m
o
n
ito
r
c
h
an
g
es
o
v
er
tim
e.
T
h
ese
m
o
d
els
ad
a
p
t
to
e
v
o
lv
in
g
u
s
er
p
r
ef
er
e
n
ce
s
an
d
r
eq
u
i
r
em
en
ts
b
y
lear
n
in
g
f
r
o
m
h
is
to
r
ical
d
ata
an
d
u
s
er
f
ee
d
b
ac
k
,
en
h
a
n
cin
g
th
e
u
s
er
ex
p
er
ien
ce
with
p
er
s
o
n
alize
d
r
ec
o
m
m
en
d
atio
n
s
.
Mo
r
eo
v
er
,
ad
v
an
c
ed
DL
ar
ch
itectu
r
es
lik
e
t
r
an
s
f
o
r
m
er
s
[
4
7
]
an
d
g
r
ap
h
n
eu
r
al
n
etwo
r
k
s
(
GNNs)
[
4
8
]
ar
e
b
ein
g
ex
p
lo
r
ed
f
o
r
th
eir
p
o
te
n
tial
to
m
o
d
el
c
o
m
p
lex
in
ter
ac
tio
n
s
in
b
io
m
ec
h
an
ical
d
at
a
an
d
p
r
o
v
i
d
e
m
o
r
e
s
o
p
h
is
ticated
in
ju
r
y
p
r
ev
en
tio
n
in
s
ig
h
ts
.
3
.
3
.
S
m
a
rt
c
lo
t
hes
Sm
a
r
t
cl
o
t
h
i
n
g
e
m
b
e
d
d
e
d
w
it
h
s
e
n
s
o
r
s
c
a
n
m
o
n
it
o
r
v
it
al
s
i
g
n
s
s
u
c
h
as
h
ea
r
t
r
at
e,
r
es
p
i
r
at
io
n
r
at
e,
a
n
d
b
o
d
y
te
m
p
er
at
u
r
e
.
M
L
al
g
o
r
it
h
m
s
s
t
u
d
y
t
h
is
d
a
ta
to
d
et
ec
t
p
at
te
r
n
s
a
n
d
ab
n
o
r
m
alit
ies
,
g
i
v
i
n
g
u
s
e
r
s
r
e
al
-
ti
m
e
in
s
i
g
h
ts
i
n
t
o
t
h
e
ir
h
ea
lt
h
s
tat
u
s
[
4
9
]
.
ML
al
g
o
r
it
h
m
s
a
ls
o
s
u
p
p
o
r
t
p
o
s
t
u
r
e
a
n
a
ly
s
is
,
w
h
ic
h
p
r
o
ce
s
s
es
d
at
a
f
r
o
m
m
o
ti
o
n
s
e
n
s
o
r
s
e
m
b
e
d
d
e
d
i
n
s
m
a
r
t
c
lo
th
in
g
to
a
n
al
y
z
e
p
o
s
tu
r
e
an
d
m
o
v
em
en
t
p
at
te
r
n
s
.
T
h
is
f
ea
t
u
r
e
h
e
lp
s
u
s
e
r
s
to
m
ai
n
t
ai
n
p
r
o
p
er
p
o
s
tu
r
e
,
p
r
e
v
e
n
t
m
u
s
cu
lo
s
k
elet
al
i
n
j
u
r
ies
,
an
d
i
m
p
r
o
v
e
o
v
e
r
all
b
o
d
y
m
e
c
h
a
n
ics
.
D
e
c
is
i
o
n
t
r
e
e
a
l
g
o
r
i
t
h
m
[
5
0
]
i
s
u
t
i
l
i
ze
d
i
n
s
m
a
r
t
c
l
o
t
h
e
s
t
o
a
n
a
l
y
z
e
s
e
n
s
o
r
d
a
t
a
p
a
t
t
e
r
n
s
a
n
d
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d
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r
a
v
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l
e
t
r
a
d
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l
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e
ls
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M
L
a
l
g
o
r
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t
h
m
s
e
x
p
l
o
r
e
t
h
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d
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f
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u
s
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o
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iz
e
d
s
u
g
g
e
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o
n
s
,
s
u
c
h
as
m
o
d
i
f
y
i
n
g
c
l
o
t
h
i
n
g
l
a
y
e
r
s
o
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u
s
i
n
g
s
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h
e
u
s
e
r
'
s
s
u
r
r
o
u
n
d
i
n
g
s
.
3
.
4
.
S
m
a
rt
e
y
ewe
a
r
ML
alg
o
r
ith
m
s
p
r
o
ce
s
s
v
is
u
al
in
p
u
t
f
r
o
m
s
m
ar
t
ey
ew
ea
r
ca
m
er
as
to
id
en
tify
o
b
j
ec
ts
,
tex
t,
lan
d
m
ar
k
s
,
an
d
f
ac
es
in
v
is
u
al
r
ec
o
g
n
itio
n
an
d
AR
.
T
h
is
f
ea
tu
r
e
allo
ws
f
o
r
f
u
n
ctio
n
alitie
s
lik
e
in
s
tan
tan
eo
u
s
tr
an
s
latio
n
,
d
escr
ip
tio
n
o
f
s
u
r
r
o
u
n
d
in
g
s
,
an
d
id
en
tific
atio
n
o
f
f
ac
es.
SVM
is
ap
p
lied
in
s
m
ar
t
ey
ewe
ar
f
o
r
task
s
s
u
ch
as
h
an
d
g
estu
r
e
r
ec
o
g
n
itio
n
o
r
em
o
tio
n
d
etec
tio
n
f
r
o
m
f
ac
ial
ex
p
r
ess
io
n
s
,
en
ab
lin
g
in
tu
itiv
e
in
ter
ac
tio
n
s
with
wea
r
ab
le
d
ev
ices a
n
d
en
h
an
ci
n
g
co
m
m
u
n
ic
atio
n
ex
p
er
ie
n
ce
s
.
AR
ap
p
licatio
n
s
in
v
o
lv
e
DL
m
o
d
els
s
u
ch
as
g
e
n
er
ativ
e
ad
v
er
s
ar
ial
n
etwo
r
k
s
(
GAN)
[
5
1
]
th
at
an
aly
ze
ca
m
er
a
f
ee
d
s
an
d
s
u
p
er
im
p
o
s
e
d
i
g
ital
in
f
o
r
m
atio
n
o
n
to
th
e
u
s
er
'
s
f
ield
o
f
v
i
ew,
en
h
an
cin
g
AR
ex
p
er
ien
ce
s
.
T
h
is
m
eth
o
d
en
c
o
m
p
ass
es
v
ir
tu
al
n
av
ig
atio
n
s
ig
n
als,
co
n
tex
tu
al
d
etails
ab
o
u
t
th
e
en
v
ir
o
n
m
en
t
,
an
d
in
ter
ac
tiv
e
o
v
e
r
lay
s
f
o
r
g
a
m
in
g
an
d
en
ter
tai
n
m
en
t.
M
o
r
e
o
v
er
,
t
h
e
s
m
ar
t
g
lass
s
y
s
tem
c
an
also
ass
is
t
b
lin
d
an
d
v
is
u
ally
im
p
air
e
d
in
d
iv
id
u
als
u
s
in
g
c
o
m
p
u
ter
v
is
io
n
te
ch
n
iq
u
es
[
5
2
]
,
DL
m
o
d
els,
a
u
d
io
f
ee
d
b
ac
k
,
a
n
d
tactile
g
r
ap
h
ics to
h
elp
t
h
em
n
av
ig
ate
in
d
ep
e
n
d
en
tly
in
lo
w
-
l
ig
h
t c
o
n
d
itio
n
s
[
5
3
]
.
3
.
5
.
AI
m
e
t
ro
lo
g
y
in s
m
a
rt
dev
ices
Me
t
r
o
lo
g
y
in
ML
a
n
d
D
L
f
o
r
s
m
a
r
t
w
ea
r
a
b
l
es
i
n
I
o
T
c
o
m
m
u
n
ic
ati
o
n
[
5
4
]
–
[
6
4
]
f
o
cu
s
es o
n
m
e
as
u
r
i
n
g
an
d
e
v
al
u
ati
n
g
t
h
e
p
e
r
f
o
r
m
a
n
c
e,
ac
c
u
r
ac
y
,
r
el
ia
b
il
it
y
,
a
n
d
ef
f
icie
n
cy
o
f
t
h
es
e
d
e
v
i
ce
s
'
ML
/
DL
m
o
d
els
.
i)
I
n
d
ata
q
u
ality
an
d
ac
q
u
is
itio
n
,
e
n
s
u
r
in
g
d
ata
q
u
ality
a
n
d
d
ep
e
n
d
ab
ilit
y
is
th
e
in
itial
s
tep
in
th
e
m
etr
o
lo
g
y
p
r
o
ce
s
s
f
o
r
d
ata
g
ath
er
ed
b
y
wea
r
ab
les.
T
h
e
m
ea
s
u
r
in
g
p
ar
am
eter
s
i
n
clu
d
e
d
ata
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
s
am
p
lin
g
r
ate,
an
d
s
ig
n
al
-
to
-
n
o
is
e
r
atio
.
Ad
d
itio
n
ally
,
it
is
cr
u
cial
to
ev
alu
ate
th
e
ac
cu
r
ac
y
o
f
s
en
s
o
r
d
ata
an
d
i
d
en
tify
a
n
y
b
iases
o
r
ar
tifa
cts th
at
m
ay
h
av
e
b
ee
n
in
tr
o
d
u
ce
d
d
u
r
in
g
d
ata
c
o
llectio
n
.
ii)
Featu
r
e
ex
tr
ac
tio
n
is
ess
en
tial
in
ML
/DL
ap
p
licatio
n
s
as
it
co
n
v
er
ts
r
aw
s
en
s
o
r
d
ata
in
to
r
elev
an
t
in
p
u
t
f
ea
tu
r
es
f
o
r
th
e
m
o
d
els.
Me
tr
o
lo
g
y
ass
ess
es
th
e
ef
f
icien
cy
o
f
f
ea
tu
r
e
ex
t
r
ac
tio
n
m
eth
o
d
s
in
ac
q
u
ir
in
g
p
er
tin
en
t
d
ata
wh
ile
r
ed
u
cin
g
r
e
d
u
n
d
an
cy
an
d
n
o
is
e.
Fea
tu
r
e
s
elec
tio
n
ap
p
r
o
ac
h
es
a
r
e
ev
alu
ated
to
m
ea
s
u
r
e
th
eir
in
f
l
u
en
ce
o
n
m
o
d
el
ac
cu
r
ac
y
a
n
d
c
o
m
p
u
tatio
n
al
s
p
ee
d
.
iii)
Me
tr
o
lo
g
y
in
m
o
d
el
cr
ea
tio
n
i
n
v
o
lv
es
tr
ain
in
g
,
v
alid
atin
g
,
a
n
d
o
p
tim
izin
g
th
e
m
o
d
el.
T
h
e
p
er
f
o
r
m
an
ce
o
f
ML
/DL
m
o
d
els
is
ev
alu
ate
d
u
s
in
g
k
e
y
m
ea
s
u
r
es
lik
e
ac
c
u
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e
[
6
3
]
–
[
6
5
]
,
an
d
ar
ea
u
n
d
e
r
th
e
cu
r
v
e
(
AUC)
[
6
6
]
.
Mo
d
el
in
ter
p
r
etab
ilit
y
,
s
ca
lab
ilit
y
,
an
d
r
o
b
u
s
tn
ess
to
en
v
ir
o
n
m
en
tal
f
lu
ct
u
atio
n
s
m
u
s
t
b
e
ev
alu
ated
in
s
ev
er
al
I
o
T
co
m
m
u
n
icatio
n
s
ce
n
ar
io
s
.
Ad
d
itio
n
ally
,
co
n
s
id
er
atio
n
s
s
u
ch
as
m
o
d
el
in
ter
p
r
etab
ilit
y
,
s
ca
lab
ilit
y
,
an
d
r
o
b
u
s
tn
ess
to
en
v
ir
o
n
m
en
tal
v
ar
iatio
n
s
ar
e
ass
es
s
ed
u
n
d
er
d
if
f
er
en
t
I
o
T
c
o
m
m
u
n
icatio
n
s
ce
n
ar
io
s
.
iv
)
Sm
ar
t
wea
r
ab
les
o
p
er
ate
u
n
d
er
lim
ited
p
o
wer
an
d
c
o
m
p
u
tatio
n
al
r
eso
u
r
ce
s
.
T
h
u
s
,
m
etr
o
lo
g
y
i
n
v
o
lv
e
s
m
ea
s
u
r
in
g
th
e
en
er
g
y
c
o
n
s
u
m
p
tio
n
a
n
d
r
eso
u
r
ce
u
tili
za
tio
n
o
f
ML
/DL
alg
o
r
ith
m
s
to
en
s
u
r
e
o
p
tim
al
p
er
f
o
r
m
an
ce
wh
ile
m
in
im
izin
g
p
o
wer
co
n
s
u
m
p
tio
n
.
Mo
d
el
q
u
an
tizatio
n
,
co
m
p
r
ess
io
n
,
an
d
s
p
ar
s
ity
o
p
tim
izatio
n
ar
e
e
v
alu
ated
to
b
alan
ce
ac
cu
r
ac
y
an
d
en
er
g
y
e
f
f
icien
cy
.
v)
R
ea
l
-
tim
e
in
f
er
en
ce
an
d
late
n
cy
a
r
e
cr
u
cial
in
I
o
T
a
p
p
li
ca
tio
n
s
,
p
ar
ticu
lar
l
y
in
ac
tiv
i
ty
r
ec
o
g
n
itio
n
,
h
ea
lth
m
o
n
ito
r
in
g
,
a
n
d
g
estu
r
e
r
ec
o
g
n
itio
n
.
Me
tr
o
lo
g
y
m
o
n
ito
r
s
th
e
laten
cy
an
d
t
h
r
o
u
g
h
p
u
t
o
f
ML
/DL
m
o
d
els
r
u
n
n
i
n
g
o
n
s
m
ar
t
wea
r
ab
le
d
ev
ices.
Me
th
o
d
s
in
clu
d
in
g
m
o
d
el
tr
im
m
in
g
,
h
ar
d
war
e
ac
ce
ler
atio
n
,
an
d
ed
g
e
co
m
p
u
tin
g
a
r
e
ev
alu
ated
to
f
u
lf
ill r
ea
l
-
tim
e
n
ee
d
s
wh
ile
p
r
eser
v
in
g
ac
c
u
r
ac
y
.
4.
CH
AL
L
E
NG
E
S AN
D
P
O
T
E
NT
I
A
L
SO
L
U
T
I
O
N
S
4
.
1
.
Cha
lleng
es
T
h
e
cu
r
r
e
n
t
s
ta
te
o
f
AI
i
n
w
e
ar
ab
le
d
ev
ices
h
i
g
h
li
g
h
ts
s
i
g
n
if
i
ca
n
t
a
d
v
a
n
ce
m
e
n
ts
a
n
d
p
o
t
en
t
ial
,
b
u
t
th
e
r
e
a
r
e
s
till
v
ar
io
u
s
ch
all
e
n
g
es
[
6
7
]
–
[
7
0
]
.
W
e
ar
a
b
le
d
e
v
ic
es
g
e
n
e
r
a
te
v
as
t
a
m
o
u
n
ts
o
f
d
at
a,
in
cl
u
d
i
n
g
p
h
y
s
i
ca
l
a
cti
v
i
ty
,
h
e
ar
t
r
at
e,
an
d
s
l
ee
p
p
att
er
n
s
,
w
h
i
ch
a
r
e
p
r
o
ce
s
s
ed
wi
th
in
t
h
e
I
o
T
ec
o
s
y
s
te
m
.
H
o
we
v
e
r
,
en
s
u
r
i
n
g
h
i
g
h
-
q
u
a
lit
y
d
a
ta
c
o
llec
ti
o
n
wh
ile
m
a
n
ag
in
g
p
o
w
er
co
n
s
u
m
p
t
io
n
p
r
ese
n
ts
a
s
i
g
n
if
ic
an
t
c
h
al
le
n
g
e
.
R
ea
l
-
ti
m
e
d
at
a
p
r
o
ce
s
s
in
g
is
f
u
r
th
e
r
co
m
p
li
ca
t
ed
b
y
th
e
c
o
n
s
tr
a
in
ts
o
f
lim
ite
d
b
att
e
r
y
li
f
e
an
d
c
o
m
p
u
t
ati
o
n
al
p
o
we
r
i
n
we
a
r
a
b
le
d
ev
ices
.
A
d
d
iti
o
n
al
ly
,
i
n
t
er
o
p
e
r
a
b
i
lit
y
r
e
m
ai
n
s
a
c
o
n
ce
r
n
,
as
d
if
f
e
r
e
n
t
m
a
n
u
f
ac
t
u
r
e
r
s
u
til
ize
v
a
r
i
o
u
s
p
r
o
t
o
c
o
ls
,
m
a
k
i
n
g
s
e
a
m
less
in
te
g
r
at
io
n
d
i
f
f
ic
u
lt
wit
h
o
u
t
u
n
i
v
er
s
al
s
t
a
n
d
ar
d
s
.
D
ata
tr
an
s
m
is
s
i
o
n
t
o
th
e
clo
u
d
i
n
t
r
o
d
u
ce
s
l
at
en
cy
,
w
h
i
ch
is
p
ar
tic
u
l
a
r
l
y
p
r
o
b
le
m
at
ic
f
o
r
tim
e
-
s
e
n
s
iti
v
e
a
p
p
li
ca
t
io
n
s
.
E
d
g
e
co
m
p
u
ti
n
g
ca
n
m
iti
g
ate
t
h
is
is
s
u
e
,
b
u
t
it
d
em
a
n
d
s
p
o
w
er
f
u
l
o
n
-
d
ev
ic
e
p
r
o
c
ess
i
n
g
c
ap
a
b
ili
ties
.
Mo
r
eo
v
e
r
,
DL
m
o
d
e
ls
s
u
c
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ar
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I
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tell
I
SS
N:
2252
-
8
9
3
8
R
ev
iew
o
f a
r
tifi
cia
l in
tellig
en
ce
in
s
ma
r
t wea
r
a
b
le
d
ev
ices u
n
d
er in
tern
et
o
f th
in
g
s
…
(
Min
h
Lo
n
g
Ho
a
n
g
)
7
as
C
N
Ns
a
n
d
R
NNs
r
e
q
u
ir
e
s
u
b
s
ta
n
t
ial
c
o
m
p
u
ta
ti
o
n
al
p
o
we
r
,
w
h
i
ch
wea
r
ab
les
m
a
y
s
t
r
u
g
g
l
e
to
s
u
p
p
o
r
t
d
u
e
t
o
th
e
ir
s
i
ze
an
d
b
at
te
r
y
li
m
ita
ti
o
n
s
.
T
h
e
v
a
r
ia
b
i
lit
y
a
n
d
n
o
is
e
i
n
s
e
n
s
o
r
d
a
ta
[
7
1
]
–
[
7
3
]
ca
n
c
o
m
p
lic
ate
AI
t
r
a
in
in
g
,
m
a
k
i
n
g
h
i
g
h
-
q
u
al
it
y
a
n
d
s
u
f
f
i
cie
n
t
d
ata
c
r
u
ci
al
f
o
r
e
f
f
ec
ti
v
e
m
o
d
e
l
p
e
r
f
o
r
m
a
n
ce
.
Op
ti
m
i
zin
g
AI
m
o
d
e
ls
f
o
r
wea
r
a
b
l
es
i
n
v
o
l
v
es
te
ch
n
i
q
u
es
l
ik
e
m
o
d
e
l
p
r
u
n
in
g
,
q
u
an
tiz
ati
o
n
,
an
d
k
n
o
wle
d
g
e
d
is
t
ill
ati
o
n
,
w
h
i
ch
h
el
p
r
e
d
u
ce
m
o
d
el
s
ize
a
n
d
c
o
m
p
l
ex
it
y
wh
ile
m
ai
n
ta
in
in
g
e
f
f
ici
en
c
y
an
d
ac
c
u
r
a
cy
.
4
.
2
.
So
lutio
ns
I
n
s
m
ar
t
wea
r
ab
le
d
ev
ices,
AI
in
teg
r
atio
n
r
e
q
u
ir
es
th
e
ap
p
r
o
p
r
iate
ap
p
r
o
ac
h
an
d
p
r
o
ce
s
s
to
m
ak
e
th
e
im
p
lem
en
ted
m
o
d
els
o
p
tim
al
ly
ef
f
ec
tiv
e
[
7
4
]
–
[
7
9
]
.
Sev
e
r
al
s
o
lu
tio
n
s
an
d
p
r
o
p
o
s
als
ca
n
b
e
co
n
s
id
er
ed
to
ad
d
r
ess
ch
allen
g
es
in
wea
r
ab
le
d
ev
ice
d
ata
p
r
o
ce
s
s
in
g
an
d
AI
im
p
lem
en
tatio
n
.
Op
tim
i
ze
d
d
ata
co
llectio
n
s
tr
ateg
ies,
s
u
ch
as
s
en
s
o
r
f
u
s
io
n
tech
n
i
q
u
es,
ca
n
h
elp
r
ed
u
ce
p
o
wer
co
n
s
u
m
p
tio
n
wh
ile
m
ain
tain
in
g
d
ata
ac
cu
r
ac
y
b
y
u
tili
zin
g
l
o
w
-
p
o
w
er
s
en
s
o
r
s
f
o
r
c
o
n
tin
u
o
u
s
m
o
n
ito
r
in
g
an
d
ac
tiv
atin
g
h
ig
h
e
r
-
p
o
wer
s
en
s
o
r
s
o
n
ly
wh
en
n
ec
ess
ar
y
.
R
ea
l
-
tim
e
p
r
o
ce
s
s
in
g
ca
n
b
e
im
p
r
o
v
e
d
th
r
o
u
g
h
lig
h
tweig
h
t
alg
o
r
ith
m
s
d
esig
n
ed
to
m
in
im
ize
co
m
p
u
tatio
n
al
lo
ad
,
wh
ile
e
d
g
e
co
m
p
u
tin
g
en
ab
les
lo
ca
l
d
at
a
p
r
ep
r
o
ce
s
s
in
g
,
r
ed
u
ci
n
g
late
n
cy
a
n
d
c
o
n
s
er
v
in
g
b
atter
y
life
.
I
n
ter
o
p
er
a
b
ilit
y
i
s
s
u
es
ca
n
b
e
m
itig
ated
b
y
ad
v
o
ca
tin
g
s
tan
d
ar
d
ized
p
r
o
to
c
o
ls
an
d
API
s
ac
r
o
s
s
m
an
u
f
ac
tu
r
er
s
,
p
r
o
m
o
tin
g
in
d
u
s
tr
y
co
llab
o
r
atio
n
t
o
estab
lis
h
u
n
i
v
er
s
al
co
m
m
u
n
icatio
n
s
tan
d
ar
d
s
.
T
o
a
d
d
r
ess
laten
cy
,
ed
g
e
c
o
m
p
u
tin
g
ca
p
a
b
ilit
ies
s
h
o
u
ld
b
e
lev
er
a
g
ed
f
o
r
lo
ca
l
d
ata
p
r
o
ce
s
s
in
g
an
d
i
n
f
er
en
ce
,
d
e
p
lo
y
in
g
lig
h
tweig
h
t
ML
m
o
d
els
o
p
ti
m
ized
f
o
r
r
ea
l
-
tim
e
r
esp
o
n
s
iv
en
ess
.
C
o
m
p
u
tatio
n
al
r
eso
u
r
ce
co
n
s
tr
ain
ts
in
wea
r
ab
le
d
ev
ices
ca
n
b
e
m
an
ag
ed
b
y
d
ev
elo
p
in
g
a
n
d
o
p
tim
izin
g
DL
m
o
d
els
s
p
ec
if
ically
f
o
r
th
ese
en
v
ir
o
n
m
en
ts
,
in
co
r
p
o
r
atin
g
tech
n
iq
u
es
lik
e
m
o
d
el
co
m
p
r
ess
io
n
,
p
r
u
n
in
g
,
q
u
an
tizatio
n
,
an
d
k
n
o
wled
g
e
d
is
till
atio
n
to
r
ed
u
ce
s
ize
an
d
co
m
p
le
x
ity
wh
ile
m
ain
ta
in
in
g
p
er
f
o
r
m
a
n
ce
.
E
n
s
u
r
in
g
h
ig
h
-
q
u
ality
an
d
s
u
f
f
icien
t
tr
ain
in
g
d
ata
r
eq
u
ir
es
r
o
b
u
s
t
p
r
ep
r
o
ce
s
s
in
g
tech
n
iq
u
es,
in
cl
u
d
in
g
n
o
is
e
r
ed
u
ctio
n
a
n
d
d
ata
au
g
m
en
tatio
n
,
to
im
p
r
o
v
e
co
n
s
is
ten
cy
an
d
r
eliab
ilit
y
.
Fin
ally
,
ad
v
an
ce
d
m
o
d
el
o
p
tim
iza
tio
n
tech
n
iq
u
es
ar
e
ess
en
tial
f
o
r
en
h
an
cin
g
ef
f
ici
en
cy
with
o
u
t
co
m
p
r
o
m
is
in
g
a
cc
u
r
ac
y
,
with
co
n
tin
u
o
u
s
r
ef
i
n
em
en
t
f
o
cu
s
ed
o
n
r
ed
u
cin
g
co
m
p
u
tatio
n
al
o
v
er
h
ea
d
an
d
e
n
er
g
y
co
n
s
u
m
p
tio
n
.
5.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
h
as
ex
p
lain
e
d
t
h
e
v
ital
s
ig
n
if
ican
ce
o
f
AI
in
wea
r
ab
le
tech
n
o
lo
g
y
,
f
o
cu
s
in
g
o
n
s
m
ar
twatch
es,
f
itn
ess
tr
ac
k
er
s
,
s
m
ar
t
clo
th
es,
an
d
s
m
ar
t
ey
ewe
ar
.
ML
an
d
DL
alg
o
r
ith
m
s
h
av
e
s
ig
n
if
ican
tly
ad
v
an
ce
d
th
ese
d
ev
ices,
s
u
p
p
o
r
ted
b
y
th
e
I
o
T
f
r
a
m
ewo
r
k
.
T
h
e
d
iv
er
s
e
u
s
es
o
f
AI
in
w
ea
r
ab
le
tech
n
o
l
o
g
y
h
av
e
b
ee
n
em
p
h
asized
,
d
e
m
o
n
s
tr
atin
g
its
s
ig
n
if
ican
t
in
f
lu
en
ce
o
n
u
s
er
ex
p
er
ien
c
e
an
d
f
u
n
ctio
n
in
g
.
Fu
r
th
er
m
o
r
e
,
th
e
ar
ticle
h
as
em
p
h
asized
th
e
cr
u
cial
im
p
o
r
tan
ce
o
f
AI
in
p
r
o
tectin
g
u
s
e
r
p
r
iv
ac
y
an
d
d
ata
s
ec
u
r
ity
in
th
e
wea
r
a
b
le
tech
n
o
lo
g
y
en
v
i
r
o
n
m
e
n
t.
T
h
e
g
r
o
wi
n
g
in
te
g
r
atio
n
o
f
th
ese
d
ev
ice
s
in
to
o
u
r
ev
er
y
d
ay
r
o
u
tin
es
h
i
g
h
lig
h
ts
t
h
e
c
r
itical
n
ec
ess
ity
f
o
r
r
o
b
u
s
t
AI
-
d
r
iv
e
n
s
y
s
tem
s
to
s
af
eg
u
ar
d
s
en
s
itiv
e
d
ata.
I
n
a
d
d
itio
n
to
p
r
iv
ac
y
a
n
d
s
ec
u
r
it
y
,
AI
en
h
an
ce
s
th
e
p
er
s
o
n
aliza
tio
n
an
d
ad
ap
tab
ilit
y
o
f
wea
r
ab
l
e
d
ev
ices,
tailo
r
i
n
g
f
u
n
ctio
n
alities
to
in
d
iv
id
u
al
u
s
er
n
ee
d
s
an
d
p
r
ef
er
e
n
ce
s
.
Fo
r
in
s
tan
ce
,
AI
-
d
r
iv
e
n
an
aly
tic
s
ca
n
p
r
o
v
id
e
u
s
er
s
with
cu
s
to
m
ized
h
ea
lth
in
s
ig
h
ts
,
p
r
o
ac
tiv
e
h
ea
lth
m
o
n
it
o
r
in
g
,
a
n
d
ea
r
ly
d
etec
tio
n
o
f
p
o
ten
tial
m
ed
ical
co
n
d
itio
n
s
.
T
h
is
p
e
r
s
o
n
alize
d
ap
p
r
o
ac
h
im
p
r
o
v
es
u
s
er
e
n
g
a
g
em
en
t
a
n
d
co
n
tr
ib
u
tes
to
o
v
er
all
well
-
b
ein
g
b
y
p
r
o
m
o
tin
g
h
ea
lth
ier
life
s
ty
le
s
an
d
in
f
o
r
m
e
d
d
ec
is
io
n
-
m
ak
in
g
.
Mo
r
eo
v
er
,
AI
'
s
ev
o
lu
tio
n
in
wea
r
ab
le
tech
n
o
lo
g
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o
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p
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ai
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in
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[
1
]
P
.
O
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g
s
u
l
e
e
,
“
A
r
t
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f
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6
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[
2
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M
.
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d
A
.
P
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[
3
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5
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.
[
6
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W
.
H
.
Lv
a
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J.
Y
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L
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,
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6
7
.
[
4
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]
F
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l
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,
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.
[
4
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]
E.
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,
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5
1
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L.
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o
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,
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A
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e
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.
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[
5
3
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.
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[
5
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]
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[
5
6
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,
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.
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u
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.
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4
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[
5
7
]
A
.
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.
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,
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.
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EEE
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w
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(
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)
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[
5
9
]
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.
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.
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.
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d
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.
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.
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A
.
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