I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
15
,
No
.
5
,
Octo
b
er
20
25
,
p
p
.
4
5
9
3
~
4
6
0
4
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
15
i
5
.
pp
4
5
9
3
-
4
6
0
4
4593
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Decom
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K
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w
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d
s
:
Dec
o
m
p
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s
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Fin
g
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m
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s
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MG
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Dep
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r
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m
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f
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ab
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cle
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ity
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o
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tain
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m
ea
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elec
tr
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u
r
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ts
p
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ce
d
in
m
u
s
cles
d
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r
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o
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tr
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.
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h
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r
aw
E
MG
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ix
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lap
p
in
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m
o
to
r
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n
it
ac
tio
n
p
o
ten
tials
(
MU
APs
)
f
r
o
m
m
u
ltip
le
m
u
s
cle
f
ib
e
r
s
.
Dec
o
m
p
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in
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th
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p
lex
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p
s
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APs
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t
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wav
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c
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th
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s
tatis
tics
o
f
th
e
in
ter
-
p
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ls
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in
ter
v
als
[
1
]
.
T
h
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u
n
iq
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o
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r
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ab
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t
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tate
o
f
th
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v
o
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s
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em
—
in
f
o
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m
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at
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n
ec
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s
s
ar
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f
o
r
clin
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d
iag
n
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s
in
g
m
y
o
p
at
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an
d
n
eu
r
o
p
ath
ies
[
2
]
,
s
tr
o
k
e
p
atien
ts
[
3
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,
r
esear
c
h
in
to
th
e
n
e
u
r
o
m
u
s
cu
lar
co
n
tr
o
l
lo
o
p
[
3
]
,
a
n
d
th
e
p
r
ed
ictio
n
o
f
h
u
m
an
m
o
v
em
e
n
ts
in
p
r
o
s
th
etics
an
d
ex
o
s
k
eleto
n
s
[
4
]
,
[
5
]
.
Dec
o
m
p
o
s
itio
n
o
f
s
E
MG
d
a
ta
in
to
m
o
to
r
u
n
it
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
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8
8
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8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
5
9
3
-
4
6
0
4
4594
d
is
ch
ar
g
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p
atter
n
s
p
r
o
v
id
es
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n
f
o
r
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[
6
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Var
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(
FDM
)
th
at
h
a
v
e
b
ee
n
u
s
ed
f
o
r
s
ev
er
al
d
ec
ad
es
to
d
ec
o
d
e
m
o
to
r
n
eu
r
o
n
ac
tiv
ities
in
th
e
s
E
MG
-
b
ased
s
y
s
tem
.
Neg
r
o
et
a
l.
[
7
]
a
n
d
Mo
h
e
b
ian
et
a
l.
[
8
]
ap
p
lied
t
h
e
c
o
n
v
o
lu
tiv
e
B
SS
m
eth
o
d
in
d
ec
o
m
p
o
s
itio
n
alg
o
r
ith
m
s
to
s
eg
r
eg
ate
in
d
iv
i
d
u
al
m
o
to
r
u
n
it
ac
tio
n
p
o
ten
ti
als.
T
h
e
B
SS
m
eth
o
d
o
f
s
E
MG
en
h
an
ce
s
m
o
to
r
u
n
it
s
tu
d
y
with
n
o
n
-
in
v
asiv
e
r
ec
o
r
d
in
g
s
,
b
u
t
ch
allen
g
es
p
er
s
is
t,
in
clu
d
in
g
v
ar
y
in
g
s
u
cc
ess
r
ates
ac
r
o
s
s
co
n
d
itio
n
s
,
m
u
s
cles,
an
d
i
n
d
iv
id
u
als
[
9
]
.
B
esid
es,
it
b
r
in
g
s
s
u
b
s
tan
tial
ch
allen
g
es
d
u
e
to
its
u
n
iq
u
e
ch
ar
ac
ter
is
tics
,
in
clu
d
in
g
lo
w
s
ig
n
al
-
to
-
n
o
is
e
r
atio
,
h
ig
h
s
im
ilar
ity
,
an
d
s
ev
er
e
s
u
p
er
p
o
s
itio
n
o
f
MU
AP
wav
ef
o
r
m
s
[
1
0
]
.
T
h
e
E
MD
b
a
s
e
d
ec
o
m
p
o
s
itio
n
was
u
s
ed
b
y
W
ei
et
a
l.
[
1
1
]
f
o
r
th
e
r
ec
o
g
n
itio
n
o
f
lo
wer
lim
b
m
o
v
em
en
ts
,
b
u
t
E
MD
h
as
l
im
itatio
n
s
d
u
e
t
o
th
e
m
ix
-
m
o
d
e
e
f
f
ec
t
b
r
o
u
g
h
t
o
n
b
y
in
ter
m
itten
t
s
ig
n
a
l
co
m
p
o
n
en
ts
[
1
2
]
.
T
h
e
m
ix
-
m
o
d
e
ef
f
ec
t
was
ad
d
r
ess
ed
b
y
an
en
h
an
ce
d
v
er
s
io
n
ca
ll
ed
en
s
em
b
le
E
MD
(
E
E
MD
)
,
wh
ich
also
b
r
o
u
g
h
t
th
e
d
if
f
icu
lty
o
f
in
cl
u
d
i
n
g
r
esid
u
al
s
u
p
p
lem
en
tal
n
o
is
es
d
u
r
in
g
s
ig
n
al
r
ec
o
n
s
tr
u
ctio
n
[
1
3
]
.
Fatim
ah
e
t
a
l.
[
1
4
]
ap
p
lie
d
th
e
Fo
u
r
ier
d
ec
o
m
p
o
s
itio
n
m
eth
o
d
to
d
ec
o
m
p
o
s
e
th
e
s
u
r
f
ac
e
E
MG
s
ig
n
al
f
o
r
th
e
r
ec
o
g
n
itio
n
o
f
h
a
n
d
g
estu
r
es.
Fo
u
r
ier
an
aly
s
is
'
s
ass
u
m
p
tio
n
o
f
s
ig
n
a
l
s
tatio
n
ar
ity
r
esu
lts
in
in
ac
cu
r
ate
f
r
eq
u
en
cy
r
ep
r
e
s
en
tatio
n
o
v
er
tim
e
an
d
lack
s
tim
e
r
eso
lu
tio
n
,
m
ak
in
g
it
c
h
allen
g
in
g
to
tr
ac
k
tr
an
s
ien
t
f
ea
tu
r
es
lik
e
m
u
s
cle
ac
tiv
atio
n
p
atter
n
s
.
C
h
en
et
a
l.
[
1
5
]
a
p
p
lied
th
e
C
KC
m
e
th
o
d
f
o
r
in
d
iv
id
u
al
s
eg
m
en
ts
to
d
ec
o
d
e
th
e
m
o
to
r
u
n
it
d
is
ch
ar
g
es
f
r
o
m
ea
ch
m
o
to
r
n
eu
r
o
n
.
Acc
o
r
d
in
g
to
t
h
e
s
tu
d
ies
[
1
6
]
[
1
7
]
wh
en
m
o
r
e
m
o
to
r
ac
tiv
ities
ar
e
in
v
o
lv
ed
,
th
e
tr
a
d
itio
n
al
C
KC
ap
p
r
o
ac
h
is
u
n
a
b
le
to
f
in
d
en
o
u
g
h
MU
s
f
o
r
m
y
o
elec
tr
ic
co
n
tr
o
l.
T
h
e
wav
elet
tr
an
s
f
o
r
m
d
ec
o
m
p
o
s
itio
n
m
eth
o
d
was
u
s
ed
b
y
L
iu
et
a
l.
[
1
8
]
an
d
Du
an
et
a
l.
[
1
9
]
an
d
Ph
in
y
o
m
ar
k
[
2
0
]
t
o
r
ec
o
g
n
ize
d
if
f
er
en
t
h
a
n
d
m
o
tio
n
s
f
o
r
p
r
o
s
th
etic
h
a
n
d
s
.
W
av
elet
-
b
ased
m
eth
o
d
s
h
a
v
e
ad
v
an
ce
d
,
b
u
t
h
av
e
d
r
awb
ac
k
s
lik
e
d
ep
e
n
d
en
cy
o
n
wav
elet
f
u
n
ctio
n
s
elec
tio
n
,
in
ab
ilit
y
t
o
co
m
b
in
e
s
m
o
o
th
n
ess
with
n
u
m
er
ical
ch
ar
ac
ter
is
tics
,
an
d
d
if
f
icu
lty
h
a
n
d
lin
g
n
o
n
-
s
tatio
n
ar
y
E
MG
s
ig
n
als,
lim
itin
g
p
r
ec
is
e
d
en
o
is
in
g
an
d
r
ec
o
n
s
tr
u
ctio
n
[
2
1
]
–
[
2
3
]
.
T
h
is
s
tu
d
y
u
s
es
a
m
u
ltire
s
o
lu
t
io
n
d
ec
o
m
p
o
s
itio
n
m
eth
o
d
b
a
s
ed
o
n
th
e
m
ax
im
al
o
v
er
lap
p
in
g
d
is
cr
ete
wav
elet
tr
an
s
f
o
r
m
(
MO
DW
T
)
to
o
f
f
e
r
ad
e
q
u
ate
d
e
n
o
is
in
g
an
d
r
ec
o
n
s
tr
u
ctio
n
o
f
m
u
lti
-
class
E
MG
s
ig
n
als.
B
ec
au
s
e
o
f
its
im
p
r
o
v
e
d
n
o
is
e
r
ed
u
ctio
n
ca
p
ab
ilit
ies
u
s
in
g
t
h
e
wav
elet
co
ef
f
icien
t,
th
e
M
ODWT
is
a
s
u
itab
le
m
eth
o
d
f
o
r
m
o
r
e
ac
cu
r
ate
m
u
l
tire
s
o
lu
tio
n
an
aly
s
is
o
f
co
m
p
lex
,
n
o
is
y
d
ata
[
2
4
]
.
T
h
e
m
ain
co
n
tr
ib
u
tio
n
o
f
th
is
r
esear
ch
is
:
a.
T
h
e
wo
r
k
p
r
o
p
o
s
es
a
n
o
v
el
t
ec
h
n
iq
u
e
b
ased
o
n
m
u
ltire
s
o
lu
tio
n
d
ec
o
m
p
o
s
itio
n
u
s
in
g
MO
DW
T
f
o
r
th
e
ap
p
r
o
p
r
iate
d
en
o
is
in
g
d
ec
o
m
p
o
s
itio
n
an
d
r
ec
o
n
s
tr
u
ctio
n
o
f
m
u
lti
-
class
E
MG
s
ig
n
als.
b.
T
h
e
m
eth
o
d
p
o
ten
tially
id
en
ti
f
ied
th
e
s
p
ec
if
ic
f
r
eq
u
e
n
cy
b
a
n
d
wh
er
e
th
e
m
o
t
o
r
n
e
u
r
o
n
s
ac
tiv
ate
d
u
r
in
g
d
if
f
er
en
t m
o
v
em
e
n
ts
o
f
s
in
g
le
an
d
m
u
ltip
le
f
i
n
g
er
s
.
c.
I
d
en
tifie
d
t
h
e
d
o
m
in
an
t
c
h
an
n
els
f
r
o
m
ei
g
h
t
-
ch
a
n
n
el
s
E
M
G
d
ata
b
y
ef
f
ec
tiv
ely
m
ea
s
u
r
in
g
th
e
a
v
er
ag
e
r
elativ
e
en
er
g
y
.
2.
M
AT
E
R
I
AL
S
AND
M
E
T
H
O
DS
2
.
1
.
Wo
rk
f
lo
wcha
rt
T
h
e
r
esear
ch
wo
r
k
was
co
m
p
leted
in
s
ev
er
al
p
h
ases
.
T
h
e
d
i
f
f
er
en
t
p
h
ases
o
f
th
e
wo
r
k
ar
e
p
r
esen
ted
b
y
a
f
lo
w
d
iag
r
am
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
Flo
w
d
ia
g
r
am
o
f
th
e
wo
r
k
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J E
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&
C
o
m
p
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n
g
I
SS
N:
2088
-
8
7
0
8
Dec
o
mp
o
s
itio
n
a
n
d
mu
lti
-
s
ca
le
a
n
a
lysi
s
o
f su
r
fa
ce
elec
tr
o
myo
g
r
a
p
h
ic
…
(
A
fr
o
z
a
S
u
lta
n
a
)
4595
2
.
2
.
Da
t
a
a
cquis
it
io
n
T
h
is
s
t
u
d
y
m
a
d
e
u
s
e
o
f
t
h
e
d
at
aset
o
b
t
ai
n
e
d
f
r
o
m
K
h
u
s
h
ab
a
an
d
Ko
d
ag
o
d
a
[
2
5
]
w
h
er
e
f
i
f
t
e
en
cl
ass
es
o
f
m
o
v
e
m
e
n
t
d
a
ta
w
er
e
o
b
tai
n
e
d
f
r
o
m
ei
g
h
t
v
o
l
u
n
te
er
s
(
s
i
x
m
al
es
a
n
d
tw
o
f
e
m
a
les
,
a
g
e
d
b
e
twe
en
2
0
t
o
3
5
)
u
s
i
n
g
8
c
h
an
n
e
ls
.
Fi
f
t
ee
n
c
las
s
es
o
f
m
o
v
e
m
e
n
ts
w
er
e
c
o
ll
e
cte
d
d
u
r
i
n
g
th
e
fle
x
i
o
n
o
f
e
ac
h
o
f
t
h
e
i
n
d
i
v
i
d
u
al
fin
g
e
r
s
,
i.
e.
,
t
h
u
m
b
(
T
T
)
,
in
d
e
x
(
I
I
)
,
m
i
d
d
le
(
MM
)
,
r
i
n
g
(
R
R
)
,
litt
le
(
L
L
)
,
a
n
d
th
e
co
m
b
i
n
e
d
f
i
n
g
e
r
s
-
th
u
m
b
-
in
d
e
x
(
T
I
)
,
t
h
u
m
b
-
m
id
d
l
e
(
T
M)
,
t
h
u
m
b
-
r
i
n
g
(
T
R
)
,
t
h
u
m
b
-
litt
l
e
(
T
L
)
,
i
n
d
e
x
-
m
i
d
d
le
(
I
M)
,
m
i
d
d
le
-
r
in
g
(
MR)
,
r
i
n
g
-
l
ittl
e
(
R
L
)
,
in
d
e
x
-
m
id
d
l
e
-
r
i
n
g
(
I
MR)
,
m
i
d
d
le
-
r
i
n
g
-
l
ittl
e
(
MRL)
,
a
n
d
h
a
n
d
cl
o
s
e
cl
ass
(
HC
)
.
2
.
3
.
P
re
pro
ce
s
s
ing
a
nd
s
eg
m
ent
a
t
io
n
T
h
e
s
E
MG
s
ig
n
als
ar
e
o
f
ten
tain
ted
b
y
b
ac
k
g
r
o
u
n
d
n
o
is
es
ca
u
s
ed
b
y
elec
tr
o
n
ic
eq
u
ip
m
e
n
t,
s
u
b
ject
m
o
v
em
en
ts
,
an
d
p
h
y
s
io
lo
g
ic
al
f
ac
to
r
s
.
Pro
p
er
d
etec
tio
n
an
d
p
r
o
ce
s
s
in
g
u
s
in
g
ef
f
icie
n
t
an
d
cu
ttin
g
-
ed
g
e
tech
n
iq
u
es
ca
n
b
e
a
b
asic
p
r
er
eq
u
is
ite
f
o
r
its
u
s
e
in
v
ar
i
o
u
s
d
o
m
ain
s
.
T
h
e
E
MG
p
o
wer
s
p
ec
tr
u
m
ca
n
b
e
s
h
ap
ed
u
s
in
g
a
v
ar
iety
o
f
b
an
d
-
p
ass
,
n
o
tch
,
h
ig
h
-
p
ass
,
an
d
lo
w
-
p
ass
f
ilter
s
.
T
y
p
ically
,
s
u
r
f
ac
e
E
MG
s
ig
n
als
ar
e
b
a
n
d
-
p
ass
f
ilter
e
d
b
etwe
e
n
2
0
Hz
an
d
5
0
0
Hz
to
r
em
o
v
e
n
o
is
e
at
f
r
eq
u
e
n
cies
b
elo
w
2
0
Hz
an
d
a
b
o
v
e
5
0
0
Hz
[
2
6
]
–
[
2
8
]
.
I
n
th
is
s
tu
d
y
,
th
e
co
llected
d
ata
was
s
a
m
p
led
at
4
0
0
0
Hz,
am
p
lifie
d
to
a
to
tal
g
ain
o
f
1
0
0
0
d
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2
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̃
[
]
ℎ̃
−
(
t)
+
∑
∑
̃
[
]
=
1
̃
−
(
t)
(
1
5
)
B
y
r
ec
o
m
b
in
in
g
all
d
etail
c
o
ef
f
icien
ts
an
d
ap
p
r
o
x
im
atio
n
lev
els,
th
e
in
v
er
s
e
MO
DW
T
r
ec
o
n
s
tr
u
cts
th
e
o
r
ig
in
al
s
ig
n
al
wh
ile
p
r
eser
v
in
g
its
len
g
th
an
d
r
eso
lu
tio
n
[
3
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Dec
o
mp
o
s
itio
n
a
n
d
mu
lti
-
s
ca
le
a
n
a
lysi
s
o
f su
r
fa
ce
elec
tr
o
myo
g
r
a
p
h
ic
…
(
A
fr
o
z
a
S
u
lta
n
a
)
4597
Fig
u
r
e
3
.
Dec
o
m
p
o
s
ed
a
n
d
r
ec
o
n
s
tr
u
cted
s
ig
n
al
u
s
in
g
MO
DW
T
[
3
8
]
Alg
o
r
ith
m
1
.
Alg
o
r
ith
m
o
f
s
E
MG
s
ig
n
al
d
ec
o
m
p
o
s
itio
n
a
n
d
r
ec
o
n
s
tr
u
ctio
n
Initialization
•
Input the sEMG signal
X(t)
of length
N
.
•
Specify the level of decomposition
J
.
•
Select the wavelet filter
Decomposition using MODWT
1.
For
l
=1, 2...,
L
2.
Apply the MODWT scaling filter
h̃
l
to obtain approximation coefficients
l
3.
Apply the MODWT wavelet filter
g̃
l
to extract detail coefficients
D̃
l
4.
Repeat steps 1 to 3 for all levels
l
until the maximum level L is reached
5.
Store the approximation coefficients
l
from the highest level
and the
detail
coefficients
D̃
l
from each level
l
Reconstruction using Inverse MODWT
1.
Initialize the reconstructed signal
X
r
(t)
as a zero vector of length
N.
2.
Reconstruct the signal using the approximation and detail coefficients from all levels:
X
r
(t)=
l
(t)
3.
For each level
l
=1, 2...,
L
, reconstruct the contribution from the detail coefficients:
X
r
(t)= X
r
(t) +
D̃
l
(t)
4. End
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
5
9
3
-
4
6
0
4
4598
3.
RE
SU
L
T
S AN
D
AN
AL
Y
SI
S
T
h
e
s
E
MG
s
ig
n
al
was
d
ec
o
m
p
o
s
ed
with
m
u
ltire
s
o
lu
ti
o
n
an
aly
s
is
m
eth
o
d
s
u
s
in
g
a
4
th
-
o
r
d
er
Dau
b
ec
h
ies
f
ilter
(
d
b
4
)
u
p
to
lev
el
6
.
Up
o
n
in
cr
ea
s
in
g
th
e
lev
els
f
r
o
m
4
to
7
,
we
f
o
u
n
d
th
at
th
e
o
p
tim
al
r
esu
lts
wer
e
o
b
tain
ed
at
lev
el
6
.
I
n
v
er
s
e
MO
DW
T
was
u
s
ed
to
r
ec
o
n
s
tr
u
ct
th
e
o
r
ig
in
al
s
ig
n
al.
T
h
e
f
r
eq
u
e
n
cy
r
an
g
e
t
h
at
m
atch
e
d
th
e
d
eta
il
an
d
a
p
p
r
o
x
im
atio
n
co
e
f
f
ic
ien
ts
at
each
wav
elet
lev
el
o
f
d
ec
o
m
p
o
s
itio
n
is
d
is
p
lay
ed
in
T
a
b
le
1
,
an
d
th
e
r
esu
lt
o
f
d
ec
o
m
p
o
s
itio
n
is
p
r
esen
ted
in
Fig
u
r
e
4
,
wh
er
e
Fig
u
r
e
4
(
a)
r
ep
r
esen
ts
th
e
o
r
ig
in
al
s
ig
n
al
a
n
d
Fig
u
r
e
4
(
b
)
s
h
o
ws th
e
d
ec
o
m
p
o
s
ed
s
ig
n
als.
T
o
id
en
tify
th
e
p
o
s
s
ib
le
f
r
eq
u
en
cy
r
a
n
g
e
o
f
MU
f
ir
in
g
s
an
d
f
in
d
th
e
r
elativ
e
en
er
g
y
at
ea
c
h
lev
el,
w
e
d
ec
o
m
p
o
s
ed
th
e
d
ataset
f
o
r
e
ac
h
o
f
th
e
f
if
teen
class
es
o
f
m
o
v
em
en
t.
T
ab
le
2
p
r
esen
ts
th
e
r
esu
lts
o
f
d
ec
o
m
p
o
s
itio
n
f
o
r
ea
ch
class
o
f
m
o
v
em
en
t
s
ig
n
al
f
r
o
m
d
if
f
er
en
t
ch
an
n
els,
an
d
T
ab
le
3
d
i
s
p
lay
s
th
e
r
esu
lts
o
f
th
e
av
er
ag
e
MU
AP f
o
r
all
1
5
class
es,
co
n
s
id
er
in
g
all
s
u
b
jects.
T
ab
le
1
.
Fre
q
u
en
cy
b
an
d
co
r
r
e
s
p
o
n
d
in
g
to
ea
ch
wa
v
elet
lev
e
l
D
e
c
o
m
p
o
si
t
i
o
n
l
e
v
e
l
F
r
e
q
u
e
n
c
y
r
a
n
g
e
(
H
z
)
B
a
n
d
w
i
d
t
h
(
H
z
)
O
v
e
r
l
a
p
p
i
n
g
f
r
e
q
u
e
n
c
y
(
H
z
)
Le
v
e
l
-
1
1
0
0
0
-
2
0
0
0
1
0
0
0
40
Le
v
e
l
-
2
4
8
3
-
1
0
4
0
5
5
7
34
Le
v
e
l
-
3
2
4
1
-
5
1
7
2
7
6
17
Le
v
e
l
-
4
1
2
1
-
2
5
8
1
3
7
8
Le
v
e
l
-
5
6
0
.
3
-
1
2
9
6
8
.
7
4
.
3
Le
v
e
l
-
6
3
0
.
2
-
6
4
.
6
3
4
.
4
A
p
p
r
o
x
.
0
-
3
1
.
1
(
a)
(
b
)
Fig
u
r
e
4
.
Dec
o
m
p
o
s
itio
n
u
s
in
g
MO
DW
T
(
a)
o
r
ig
in
al
s
E
MG
s
ig
n
al
an
d
(
b
)
s
ig
n
al
af
te
r
d
ec
o
m
p
o
s
itio
n
in
to
6
lev
els
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Dec
o
mp
o
s
itio
n
a
n
d
mu
lti
-
s
ca
le
a
n
a
lysi
s
o
f su
r
fa
ce
elec
tr
o
myo
g
r
a
p
h
ic
…
(
A
fr
o
z
a
S
u
lta
n
a
)
4599
T
ab
le
2
.
T
h
e
p
er
ce
n
tag
e
o
f
th
e
r
elativ
e
en
er
g
y
o
f
th
e
d
ec
o
m
p
o
s
ed
s
ig
n
al
f
o
r
d
if
f
er
e
n
t m
o
v
e
m
en
ts
(
Su
b
-
2)
F
r
e
q
l
e
ve
ls
HC
II
IM
I
M
R
LL
MM
MR
M
R
L
RL
RR
TI
TL
TM
TR
TT
L
e
ve
l
-
1
0.
01
0.
01
0.
03
0.
01
0.
01
0.
02
0.
02
0.
01
0.
01
0.
01
0.
01
0.
01
0.
02
0.
01
0.
01
L
e
ve
l
-
2
0.
43
0.
58
0.
96
0.
50
0.
50
0.
75
0.
60
0.
49
0.
44
0.
45
0.
52
0.
38
0.
59
0.
45
0.
62
L
e
ve
l
-
3
4.
84
5.
40
6.
42
4.
63
5.
05
6.
76
5.
25
4.
21
3.
61
4.
38
5.
18
3.
95
5.
32
4.
41
5.
80
L
e
ve
l
-
4
18.
27
14.
58
14.
10
13.
12
15.
09
14.
77
12.
02
11.
69
10.
54
13.
78
16.
11
14.
51
17.
24
15.
73
15.
53
L
e
ve
l
-
5
34.
81
28.
93
27.
99
30.
87
32.
41
28.
25
27.
58
28.
73
30.
28
31.
75
34.
71
31.
60
33.
19
34.
72
29.
27
L
e
ve
l
-
6
37.
10
42.
91
42.
57
46.
45
40.
36
42.
36
48.
93
50.
99
50.
20
42.
65
38.
52
44.
35
38.
47
39.
61
41.
45
Appr
ox.
4.
55
7.
59
7.
94
4.
43
6.
58
7.
10
5.
60
3.
82
4.
92
6.
98
4.
96
5.
20
5.
19
5.
02
7.
31
T
ab
le
3
.
Av
e
r
ag
e
o
f
th
e
r
elativ
e
en
er
g
y
f
o
r
d
if
f
e
r
en
t m
o
t
o
r
n
eu
r
o
n
ac
tiv
ities
(
C
h
-
2)
F
r
e
q
l
e
ve
ls
HC
II
IM
I
M
R
LL
MM
MR
M
R
L
RL
RR
TI
TL
TM
TR
TT
L
e
ve
l
-
1
0.
02
0.
02
0.
01
0.
02
0.
01
0.
01
0.
01
0.
01
0.
01
0.
01
0.
01
0.
01
0.
02
0.
01
0.
01
L
e
ve
l
-
2
0.
89
0.
72
0.
88
1.
00
0.
48
0.
55
0.
73
0.
30
0.
20
0.
44
0.
66
0.
54
0.
96
0.
52
0.
78
L
e
ve
l
-
3
7.
42
9.
94
10.
83
9.
27
6.
92
8.
06
7.
69
2.
20
2.
91
6.
39
7.
77
7.
35
9.
94
6.
85
8.
95
L
e
ve
l
-
4
11.
61
28.
35
26.
66
14.
86
25.
87
27.
78
13.
71
3.
62
16.
20
25.
95
24.
48
27.
84
22.
95
24.
72
26.
68
L
e
ve
l
-
5
21.
75
35.
63
29.
14
21.
02
34.
79
38.
12
21.
08
19.
67
34.
46
37.
31
36.
88
38.
53
30.
20
36.
61
35.
52
L
e
ve
l
-
6
54.
53
21.
8
29.
94
52.
17
27.
58
21.
45
54.
87
73.
00
42.
14
25.
13
25.
87
22.
44
32.
80
27.
32
24.
15
Appr
ox.
3.
87
3.
55
2.
52
1.
66
4.
35
4.
02
1.
9
1.
20
4.
09
4.
77
4.
33
3.
30
3.
13
3.
96
3.
90
Acc
o
r
d
in
g
to
th
e
d
ec
o
m
p
o
s
itio
n
r
esu
lt,
lev
el
6
,
o
r
th
e
f
r
eq
u
en
cy
r
an
g
e
o
f
3
0
.
2
to
6
4
.
6
Hz,
s
h
o
wed
th
e
m
ax
im
u
m
c
o
n
ce
n
tr
atio
n
o
f
ac
tio
n
p
o
ten
tials
as
s
h
o
wn
in
Fig
u
r
e
5
.
Fu
r
th
er
m
o
r
e,
we
f
o
u
n
d
th
at
n
eith
e
r
th
e
s
u
b
ject,
as
illu
s
tr
ated
in
Fig
u
r
e
6
,
n
o
r
th
e
s
p
ec
if
ic
m
o
v
e
m
en
t
o
f
an
y
s
u
b
ject,
as
illu
s
tr
ated
in
Fig
u
r
e
7
,
af
f
ec
ts
th
e
av
er
ag
e
r
elativ
e
en
er
g
y
o
f
an
y
lev
el
Fo
r
ea
ch
class
o
f
m
o
v
em
e
n
t,
th
e
s
ig
n
al
was
r
ec
o
n
s
tr
u
cted
b
y
co
m
b
in
in
g
all
th
e
d
ec
o
m
p
o
s
ed
lev
els
u
s
in
g
in
v
er
s
e
MO
DW
T
;
th
e
o
r
ig
in
al
s
ig
n
al
is
s
h
o
wn
in
Fig
u
r
e
8
,
an
d
th
e
r
ec
o
n
s
tr
u
cted
s
ig
n
al
o
f
th
e
co
r
r
esp
o
n
d
in
g
m
o
v
em
en
t
is
s
h
o
wn
in
Fig
u
r
e
9
.
T
h
e
s
ig
n
al
was
al
s
o
r
ec
o
n
s
tr
u
cted
f
r
o
m
th
e
l
ev
el
-
5
an
d
l
ev
el
-
6
co
ef
f
icien
ts
,
as
s
h
o
wn
in
Fig
u
r
e
1
0
,
b
ec
au
s
e
th
e
h
i
g
h
est
en
er
g
y
was
f
o
u
n
d
at
t
h
ese
lev
els,
an
d
we
o
b
s
er
v
ed
th
at
th
e
r
esu
ltin
g
s
ig
n
al
ap
p
ea
r
s
clo
s
e
to
th
e
s
ig
n
al
r
ec
o
n
s
tr
u
cted
f
r
o
m
t
h
e
co
ef
f
icien
ts
o
f
all
lev
els.
Fig
u
r
e
5
.
Av
e
r
ag
e
MU
AP a
t d
if
f
er
en
t f
r
e
q
u
en
cies f
o
r
lev
els
Fig
u
r
e
6
.
Av
e
r
ag
e
MU
AP o
f
d
if
f
er
en
t f
r
e
q
u
en
c
y
lev
els o
f
d
if
f
er
en
t su
b
jects
0.
01
0.
50
4.
68
14.
13
31.
31
43.
91
5.
45
Av
era
g
e
M
UAP
L
eve
l
-
1
L
e
ve
l-
2
L
e
ve
l-
3
L
e
ve
l-
4
L
e
ve
l-
5
L
e
ve
l-
6
Appr
x
0
.
0
1
0
.
0
2
0
.
0
3
0
.
0
4
0
.
0
5
0
.
0
6
0
.
0
7
0
.
0
8
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ig
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ased
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e.
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n
d
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ased
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th
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ir
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ig
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tio
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o
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ar
ticu
lar
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e
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cy
b
an
d
s
t
h
at
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r
r
elate
with
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e
f
ir
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m
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to
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r
o
n
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d
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s
is
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r
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ar
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lt.
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h
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k
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as
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io
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ical
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am
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f
icatio
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s
s
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it
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elp
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ith
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u
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d
n
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r
o
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s
th
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tiv
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tr
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ith
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ter
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ter
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we
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e
to
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l sy
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r
e
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ased
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te
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eh
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ep
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