Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
Vol.
6, No. 6, Decem
ber
2016, pp. 2878~
2
886
I
S
SN
: 208
8-8
7
0
8
,
D
O
I
:
10.115
91
/ij
ece.v6
i
6.1
145
2
2
878
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJECE
Design and Implementation of
W
h
eel
chai
r Cont
roller Bas
e
d
Electroencephalogram Signal
using Microcontroller
M.I
.
A
r
z
a
k
1
, U. Sun
ar
ya
2
,
S.
H
a
di
yoso
3
1
School of
Electrical Eng
i
neerin
g, Telkom University
, B
a
ndung, I
ndonesia
2,3
Telkom Applied Science Scho
ol,
Te
lkom Univ
ersity
, Bandung
, Indonesia
Article Info
A
B
STRAC
T
Article histo
r
y:
Received
J
u
n 06, 2016
Rev
i
sed
O
c
t 25
, 20
16
Accepted Nov 09, 2016
W
h
eelcha
i
r is
a m
e
dical dev
i
ce t
h
at can help pa
ti
ents
, es
pec
i
al
l
y
f
o
r pers
ons
with ph
y
s
ic
al di
sabilit
ies. In this
research has de
signed a wheelc
h
air that
can
be con
t
rolled us
ing brain wave. Mind wa
ve device is used
as a
sensor
to
captur
e
brain
wa
ves
.
F
u
zz
y m
e
th
od is
used to pro
cess data from
mind wave
.
In the design was used a
modified wheel
chair (or
i
ginal wheelch
a
ir modified
with addition d
c
m
o
tor that ca
n be
control using microcontro
ller)
. After
processing data
from mindwave using fu
zzy
m
e
thod,
then microcontroller
ordered d
c
motor to rota
t
e
.
T
he
dc m
o
tor
conn
ect
ed to
ge
ar of
wheel
chair
us
ing cha
i
n.
S
o
when th
e d
c
m
o
tor rot
a
ted
th
e
wheelch
air
rota
t
e
d as
we
ll
.
Controlling of
DC motor used
PID cont
rol method. Input enco
der was used
as
feedba
ck for
P
I
D control at
each whe
e
l. F
r
om
the experim
e
nta
l
res
u
lts
concen
tration level data of th
e h
u
man br
ain wav
e
s
can be
us
ed t
o
adjus
t
th
e
rate
of spe
e
d of
the whe
e
lch
a
ir
.
The
leve
l
accur
a
c
y
of r
e
spons Fuzz
y
m
e
tho
d
ton s
y
stem obtained b
y
d
e
vides total tru
e
respons data with
total tested data
and the res
u
l
t
is
85.71 %. W
h
eel
chairs
can run at a maximum
speed of 31.5
cm
/s when the batt
er
y
vo
ltag
e
is m
o
re than 24.05V. Moreover, th
e m
a
xim
u
m
load of
wheelch
a
ir
is 110 kg
.
Keyword:
Electroe
n
cephalogram
Fuzzy
M
i
nd wa
ve
PID
Wheelc
h
airs
Copyright ©
201
6 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
S. H
a
d
i
yoso,
Telk
o
m
App
lied
Scien
ce Schoo
l,
Telk
o
m
Un
i
v
ersity,
Tel
e
kom
uni
ka
si
R
d
, Ter
u
sa
n B
u
ah
B
a
t
u
,
Ban
dun
g-
402
57
I
ndo
n
e
sia
Em
a
il: in
fo
@t
elk
o
m
u
n
i
v
e
rsity.ac.id
1.
INTRODUCTION
In t
h
i
s
w
o
rl
d t
h
ere i
s
a
qua
d
r
i
p
l
e
gi
c
wh
o
was pa
ral
y
zed
i
n
ad
di
t
i
on al
so ha
s ot
her s
h
o
r
t
c
om
i
ngs,
su
ch
as the d
i
fficu
lty to
m
o
v
e
th
e m
o
to
r
n
e
rv
es th
rou
gho
ut th
e bod
y wh
ich
cau
s
es so
meti
m
e
s b
e
v
e
ry
stiff,
an
d h
a
d
d
i
fficulty in
sp
eak
i
n
g. Th
e shortco
m
in
g
s
m
a
k
e
it d
i
fficu
lt to
con
t
ro
l th
e
wh
eelchair eith
er m
a
n
u
al o
r
aut
o
m
a
t
i
c
. So
t
o
per
f
o
r
m
dai
l
y
act
i
v
i
t
i
e
s sho
u
l
d
be
hel
p
e
d
by
som
e
one. Not
eve
r
y
t
i
m
e
som
e
body
can
hel
p
,
so
we
nee
d
a
w
h
eel
chai
r
t
h
at
i
s
co
nt
r
o
l
l
e
d
by
t
h
e
phy
si
cal
l
y
di
sabl
e
d
are
f
o
un
d l
a
c
k
i
n
g.
In th
is
research
, au
thors
h
a
ve m
a
d
e
a wh
eelch
a
ir
wh
ich
is con
t
ro
lled
usin
g hu
m
a
n
b
r
ain
sign
al
s
(El
ect
roe
n
ce
ph
al
og
ram
)
, whe
r
e t
h
e
hum
an b
r
ai
n si
g
n
al
rea
d
i
n
gs
usi
n
g m
i
nd
wa
ve m
odu
l
e
. The
hum
an bra
i
n
sig
n
a
ls will b
e
u
s
ed
to
con
t
ro
l
th
e sp
eed
an
d
d
i
rection
of th
e wh
eelch
ai
r. Th
e related
research
are i
n
trodu
ction
of c
o
nt
rol
l
e
d
wheel
c
h
ai
r
usi
ng B
C
I was
p
e
rf
orm
e
d usi
n
g SS
VEP
feat
ure
[1]
,
c
o
nt
r
o
l
l
e
d wheel
c
h
ai
r usi
ng
asyn
chr
ono
us m
o
to
r
-
i
m
a
g
e
r
y
b
a
sed
BCI
p
r
o
t
o
c
o
l
[
2
], con
t
ro
llin
g
a wheelch
air
in
door
s fo
r
a Myo
t
ro
ph
ic
Lateral Sclero
sis [3
], co
ord
i
n
a
t co
n
t
ro
l o
f
an
in
tellig
en
t wh
eelch
a
ir b
a
sed
on
BCI an
d
sp
eech
reco
gn
ition
[4
],
BCI base
d real
tim
e
control
of wheelchai
r using E
E
G
a
n
d
WPT as
feature extractio
n [5], Feature e
x
traction
for m
u
lti-class BCI u
s
i
n
g Cano
n
i
cal
v
a
riates
an
alysis [6
].
An
ot
he
r st
u
d
y
by
B
h
av
na p
r
e
s
ent
EEG si
g
n
a
l
anal
y
s
i
s
using f
r
act
al
di
m
e
nsi
o
n Hi
g
u
c
h
i
t
o
obser
ve
o
v
e
rall effect on
b
r
ai
n
.
Th
is an
alysis is u
s
ed
to
d
e
te
rm
in
e th
e con
d
ition
o
f
th
e brain b
e
fo
re and
after ch
an
ting
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Desi
g
n
a
n
d
I
m
pl
eme
n
t
a
t
i
o
n
o
f
Wheel
ch
ai
r C
ont
r
o
l
l
e
r B
a
se
d El
ect
roe
n
ce
p
hal
og
ra
m …
(
M
.I.
Arz
a
k)
2
879
OM [7
]. Related
research
by Ho
ssai
n
p
r
esen
t EEG si
gnal classificatio
n
b
a
sed on
wav
e
let tran
sfo
r
m
fo
r
featu
r
e ex
t
r
actio
n to
d
e
term
in
e righ
t an
d left
h
a
nd
m
o
v
e
m
e
n
t
s [8
].
In
t
h
i
s
pa
per
,
we pre
s
ent
a
c
ont
rol
l
e
d
w
h
ee
l
c
hai
r
usi
n
g si
ngl
e
l
ead EE
G
m
odul
e by
ne
ur
os
ky
b
a
sed
on c
o
ncent
r
at
i
on l
e
vel
o
f
t
h
e
m
i
nd (
f
oc
us
l
e
vel
)
t
o
det
e
rm
i
n
e t
h
e for
w
ar
d,
ri
g
h
t
an
d l
e
ft
com
m
and. T
h
i
s
sy
st
em
i
s
al
so enabl
i
n
g s
p
ee
d
cont
r
o
l
usi
n
g a PID al
go
ri
t
h
m
.
The
m
a
i
n
cont
ri
b
u
t
i
ons
of
t
h
i
s
wo
rk a
r
e
m
a
ke
an autom
a
tic wheelchair c
ont
rol use sim
p
le f
eatures and
algo
rith
m
as well
as th
e lo
w co
st
o
f
sing
le lead
EEG
machines.
2.
BASIC THE
O
RY
2.
1.
Electroencephalogram (EE
G
)
Electroe
n
cephalogram
is an instru
m
e
nt to
capture the
brain'
s elect
rical
activ
ity. Th
e
a
m
p
litu
d
e
of
EEG si
g
n
al
i
s
very
sm
all
and
ran
d
o
m
.
EEG i
t
s
el
f i
s
i
n
fl
ue
nced
by
m
a
ny
vari
a
b
l
e
s,
m
e
nt
al
st
at
e,
heal
t
h
,
act
i
v
i
t
y
, reco
rd
i
ng e
n
vi
r
onm
en
t, electrical interfere
nce
from
other orga
ns, the e
x
ternal
stim
uli and a
g
e. T
h
e
characte
r
istics of EE
G signal are
gene
rally non-sta
tion
a
ry and
rand
o
m
wh
ich
ad
d co
m
p
lex
ity in
t
h
e
pr
ocessi
ng
of
EEG si
g
n
al
. C
l
assi
fi
cat
i
on o
f
EEG si
g
n
al
s i
n
cha
ngi
ng o
f
cert
a
i
n
va
riables can explain the
activ
ity o
f
th
e
b
r
ai
n
an
d cap
t
u
re th
e ch
an
g
i
n
g
on
brain
act
iv
ity.
To
sim
p
lify th
e classificatio
n o
f
t
h
e EEG si
g
n
a
l, it is
needed tra
n
sform
a
tion
si
gn
al th
at id
en
tifies
and
qua
nt
i
f
i
e
s t
h
e EEG si
g
n
al
sp
ect
rum
.
EG si
gnal
s
c
onsi
s
t
i
n
g of al
pha
wa
ves (
8
-
1
3
)
Hz
wi
t
h
a consci
ous
,
clo
s
ed
eyes an
d
relax
e
d
con
d
ition
s
,
b
e
ta
wav
e
s (1
4-
2
6
) Hz often
arise wh
en
co
nd
it
io
n
s
are th
i
n
k
i
n
g
or
activity
, theta
wave
s (4
-
7
.
5
)
Hz occ
u
rs
w
h
e
n
o
u
r circ
um
stances bei
ng a
light sleeper ,
sleepy or em
otional
sy
st
em
, and
d
e
l
t
a
waves
(
0
.
5
-
4
)
Hz
occ
u
r
s
w
h
e
n
we ar
e sl
eepi
n
g.
In
the researc
h
was use
d
beta
wa
ve
fre
que
ncy
bec
a
use t
h
e
bet
a
si
gnal
o
f
t
e
n a
r
i
s
ed w
h
en c
o
n
d
i
t
i
ons
were t
h
i
n
ki
n
g
an
d ac
t
i
v
i
t
y
. The fea
t
ure o
f
fre
que
ncy
ba
n
d
we
re l
e
vel
si
gnal
a
nd a
v
era
g
e o
f
am
pl
i
t
ude.
There
f
ore, t
h
e re
prese
n
t
a
t
i
on
of t
h
e EEG
si
gna
l
i
n
t
h
e
fre
que
n
c
y
dom
ai
n
m
o
st
l
y
done
i
n
re
search
rel
a
t
e
d
t
o
EE
G si
g
n
al
anal
y
s
i
s
. Fi
g
u
r
e
1 i
s
a
n
e
x
am
pl
e o
f
th
e EEG
sig
n
a
l [9
].
Figure 1.
Sam
p
le
EEG Signal [9]
2.
2.
M
i
d
w
av
e N
e
uro
s
ky
Neuro S
k
y Mindwa
ve is m
odule
for E
E
G
acquisition
bas
e
d
on single lead electrode
.
There
f
ore it
onl
y
ge
ne
rat
e
d
one si
gnal
EE
G.
Neu
r
o
Sky
M
i
ndw
ave
use
d
t
o
rea
d
EE
G
si
gnal
w
a
ve
fo
r
m
. W
h
e
r
e t
h
i
s
devi
ce
can c
o
m
m
uni
cat
e wi
t
h
ot
he
r
devi
ces
suc
h
a
s
com
put
er,
l
a
pt
o
p
, a
n
d m
i
croco
n
t
r
ol
l
e
r
vi
a
a wi
rel
e
ss
net
w
o
r
k
(Bluetooth).
T
h
e s
h
a
p
e
of t
h
e
m
obile
m
i
ndwavecan be
see
n
in Fi
gure
2
Fi
gu
re
2.
M
i
nd
wave
Ne
u
r
o
S
k
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJECE Vol. 6, No. 6, D
ecem
ber 2016
: 2878 – 2886
2
880
Specifications
a.
W
e
igh
s
90
g
b.
Sens
o
r
a
r
m
up
:
Hei
g
ht
:
2
25m
m
x W
i
dt
h:
15
5
m
m
x Dept
h:
9
2
m
m
c.
Sens
o
r
Arm
d
o
w
n
:
hei
g
ht
:
2
25m
m
x wi
dt
h:
15
5m
m
x dept
h:
1
65m
m
d.
30
m
W
r
a
te pow
er
;
50
m
W
max
p
o
w
e
r
e.
2
.
42
0 - 2.471G
H
z
RF
f
r
e
quen
c
y
f.
6d
Bm
RF
m
a
x
po
wer
g.
25
0kb
it/s RF
d
a
ta rate
h.
10
m
RF r
a
ng
e
i.
5%
pac
k
et
l
o
s
s
o
f
by
t
e
s vi
a
wi
rel
e
ss
j.
UA
RT Bau
d
r
a
te: 5
7
,
60
0 Baud
k.
1m
V pk
-
p
k
EE
G m
a
xim
u
m
signal
i
n
p
u
t
ran
g
e
l.
3Hz –
1
00Hz h
a
rdware filter
ran
g
e
m.
12
b
its ADC
resu
ltio
n
n.
51
2H
z sam
p
li
n
g
r
a
te
o.
1Hz
eSe
n
se cal
culation
rate
Measurem
ents:
a.
Raw sign
al
b.
Neuroscience defi
ned
EE
G powe
r
s
p
ectrum
(Al
p
ha,
Beta, Tetha, Delta, Gamma)
c.
eSen
se m
e
ter fo
r Atten
tion
d.
eSen
se m
e
ter fo
r Med
itatio
n
e.
eSense
Blink
Detection
f.
O
n
-
h
ea
d
det
ect
i
o
n
2.
3.
PID
(Pr
o
porsi
o
nal, Inte
gral, Derivatif)
PID (Pro
po
rti
o
n
a
l, In
tegral,
Deri
v
a
tiv
e) is
a con
t
ro
ller that h
a
s fun
c
tions to
process t
h
e error signal
in
to
a con
t
ro
l sig
n
a
l wit
h
feed
b
a
ck’s ch
aracteristics to
g
e
t th
e p
r
ecisio
n
of th
e system
. P
I
D co
nsists o
f
th
ree
t
y
pes of c
o
m
pone
nt
s,
nam
e
l
y
pr
op
o
r
t
i
onal
,
i
n
t
e
g
r
al
, an
d
de
ri
vat
i
v
e. T
h
ese
t
h
ree t
y
pes
of
com
pone
nt
s c
a
n be
use
d
t
o
get
h
e
r
o
r
i
n
di
vi
dual
l
y
i
n
acc
or
da
nce
wi
t
h
t
h
e
de
si
re
d
resp
o
n
se i
n
a
sy
st
em
.
PID con
t
ro
l is a co
m
b
in
atio
n
of
p
r
op
ortion
a
l, in
t
e
gral
,
and
de
ri
vat
i
v
e
.
Thi
s
c
o
nt
rol
l
er has t
h
e
adva
nt
age
of
anot
her t
y
pe
of co
nt
r
o
l
l
e
r
due t
o
i
t
s
cha
r
act
eri
s
t
i
c
s i
s
a com
b
i
n
at
i
o
n
of co
nt
r
o
l
l
e
rs
P, PI
cont
rol
l
e
r,
a
n
d
PID
co
nt
r
o
l
l
e
rs
. PI
D c
o
nt
r
o
l
r
e
l
a
t
i
onshi
ps ca
n
be e
x
p
r
esse
d
i
n
t
h
e
f
o
l
l
o
wi
n
g
e
quat
i
o
n.
.
.
(1
)
or
an
ot
he
r e
q
u
a
t
i
on
usi
n
g t
r
a
n
sfe
r
fu
nct
i
o
n
1
(2
)
(3
)
PID
co
nt
r
o
l
i
s
descri
bed
i
n
t
h
e f
o
l
l
o
wi
ng
bl
o
c
k
di
ag
ram
as sho
w
n i
n
Fi
gu
r
e
3.
Fi
gu
re
3.
B
l
oc
k
Di
ag
ram
PID
C
o
nt
rol
l
e
r
2.
4.
F
u
zzy
Lo
g
i
c
Fu
zzy lo
g
i
c meth
od
first in
tro
d
u
c
ed
b
y
Lo
t
f
i A. Zadeh, that h
a
s a d
e
gree o
f
m
e
m
b
ersh
ip
in
a rang
e
of 0
(zer
o) t
o
1 (o
ne
), i
n
co
n
t
rast
t
o
di
gi
t
a
l
l
ogi
c t
h
at
has
onl
y
t
w
o
val
u
e
s
:
1 (o
ne) o
r
0
(zer
o)
. Fuzzy
l
ogi
c i
s
u
s
ed
to translate a qu
an
tity th
at is exp
r
essed
usin
g lin
gu
istic.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Desi
g
n
a
n
d
I
m
pl
eme
n
t
a
t
i
o
n
o
f
Wheel
ch
ai
r C
ont
r
o
l
l
e
r B
a
se
d El
ect
roe
n
ce
p
hal
og
ra
m …
(
M
.I.
Arz
a
k)
2
881
3.
R
E
SEARC
H M
ETHOD
3.
1.
Sys
t
em
Desi
g
n
In
d
e
sign
ing
th
is au
to
m
a
tic
wh
eelch
air is in
clud
es h
a
rdware and
so
ft
ware d
e
sign
. Th
e syste
m
u
s
es
m
obi
l
e
m
i
nd wave as i
n
p
u
t
devi
ce t
o
rea
d
s t
h
e EEG si
g
n
al. The
n
the
com
puter is
us
ed to proces
s the Raw
EEG signal and use
r
interfa
c
e
. Pre
v
ious re
search a
b
out proces
sing and spectral
analys
is of the ra
w EEG
si
gnal
fr
om
t
h
e m
i
nd
wave
[
1
0]
.
Si
gnal
s
EEG
fr
om
m
obi
l
e
m
i
nd wa
ve se
nt
t
o
a c
o
m
put
er a
n
d
i
n
t
h
e com
put
er
w
a
s desai
gne
d
ap
p
lication
th
at co
u
l
d
filter beta frequ
en
cy fro
m
o
t
h
e
rs
freq
u
e
n
c
y (lev
el sig
n
a
l and
av
erag
e of am
p
litu
d
e
b
e
ta
fre
que
ncy
)
whi
c
h i
s
t
h
en pa
ss
ed t
o
t
h
e m
i
crocont
rol
l
e
r.
Micro
c
on
tro
ller typ
e
th
at u
s
ed
is STM32
F
4
th
e main
cont
rol
l
e
r.
Le
vel
si
g
n
al
an
d a
v
era
g
e
of
am
pl
it
ude t
h
en
used
as i
n
p
u
t
of
fuz
z
y
m
e
t
hod
(i
n
s
i
d
e
o
f
m
i
croco
n
t
r
ol
l
e
r) t
o
cl
assi
fy
.
The
resul
t
of c
l
assi
fy
i
ng
was
deci
si
o
n
t
o
m
ove
dc m
o
t
o
r (t
ur
n l
e
ft
, t
u
r
n
ri
ght
o
r
straig
h
t
fo
rward
)
. In
t
h
e
research PD was
u
s
ed
for co
n
t
ro
llin
g speed of
d
c
m
o
to
r.
Th
is micro
c
ontro
ller is u
s
ed as th
e
m
a
in
co
n
t
ro
ller wh
i
c
h
fu
n
c
tion
s
to
reg
u
l
ate th
e rate o
f
th
e
wheelc
h
air.
On the out
put side there
are
2 pieces of
m
i
croc
ontroller
dc
m
o
tor dri
v
er is connected
to the
actuator wheel
chair. DC m
o
tors
are
use
d
a
s
actuators
of
wh
eelch
air. This wh
eelch
a
ir i
s
equ
i
pp
ed
with
an
encode
r as sensor to calc
u
late the num
ber
of turns on
ea
ch wheel. T
h
is sensor is use
d
as fee
dbac
k
to the
micro
c
on
tro
ller so
th
at t
h
e
rate
of wheelc
h
air becom
e
s
sm
o
o
th.
Fi
gu
re
4.
Sy
st
em
Desi
gn
3.
2.
Desi
gn
o
f
Wh
eel
chai
r
Wheel
c
h
ai
r
ha
s si
ze 10
2 cm
x 61 cm
x 83 cm
of (l
engt
h x
wi
dt
h
x hei
ght
)
.
Pl
a
nni
ng
ro
b
o
t
wheelc
h
air m
echanics
done
by
m
odifyin
g the ex
istin
g m
a
n
u
a
l wh
eelch
ai
r. Th
en
th
e en
cod
e
r m
o
un
ted
d
i
rectly
to
th
e ax
le. Com
p
arin
g
g
e
ar ratio
m
o
to
r
with
wh
eels is
1:
3. Speci
fication
of
wheel
chai
r i
s
sh
ow
n i
n
Ta
bl
e 1
and wheelchai
r desi
gn is s
h
own in Figure
5.
Sy
st
em
i
n
t
h
e ro
b
o
t
'
s
m
o
ti
on
usi
n
g
di
ff
eren
tial wh
eel syste
m
, wh
ich
h
a
s two
-
wh
eel wh
ich
diffe
re
ntial fre
e wheel can move
forwa
r
d or bac
k
wa
rd.
Since each
wheel
dri
v
en by a
DC
m
o
tor.
Tabl
e 1. Speci
f
i
cat
i
on
o
f
Wh
e
e
l
c
hai
r
Dim
e
nsion of Rob
o
t
102 cm
x 61 cm
x
83 cm
Contr
o
ller
ST
M
32F4Discover
y
M
a
xim
u
m
of Speed
30 cm
/s
Voltage
Accu
m
u
lator 24 V
o
lt
Mobile Syste
m
Differential Wheel
Actuator
DC Motor 24 Volt
Ratio of Gear
Motor : wheel
1:3
M
a
xim
u
m
L
o
ad
120 kg
Fi
gu
re 5.
Whe
e
l
c
hai
r
Desi
g
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJECE Vol. 6, No. 6, D
ecem
ber 2016
: 2878 – 2886
2
882
3.
3.
Fuz
z
y
L
ogi
c
Desi
gn
The controls are use
d
on robotic
wheelchai
r is fuzzy logi
c control.
In research
pre
v
iously is used
LDA as cl
assi
f
i
cat
i
on m
e
t
hod
[1
1]
and
whe
e
l
c
hai
r
wi
t
h
E
E
G [
12]
. EE
G
and ey
e-
bl
i
n
k
i
ng si
g
n
al
s [
1
3
]
, a
n
d
g
e
sture recog
n
itio
n
fo
r au
tomatic wh
eelchair [1
4
]
.
In
t
h
i
s
syste
m
, th
e m
o
b
ile will tra
n
sm
it d
a
ta
min
d
wave
l
e
vel
of
foc
u
s
and a
pers
o
n
'
s
brai
n wa
ve dat
a
The l
e
vel
dat
a
i
s
obt
ai
ne
d u
s
i
ng t
r
i
a
l
an
d e
r
r
o
r
.
I
n
t
h
e e
x
p
e
ri
m
e
nt
u
s
er t
r
ies to
t
h
ink
g
o
st
raigh
t
, turn left, t
u
rn
ri
gh
t, and sto
p
th
en
the lev
e
l sign
al can
b
e
seen
o
n
t
h
e
appl
i
cat
i
o
n o
n
t
h
e com
put
er.
From
t
h
e expe
ri
m
e
nt
t
h
e user can m
a
ke deci
si
on t
o
m
a
ke t
h
e l
i
m
i
t
dat
a
as l
e
vel
o
f
ev
ery
si
g
n
a
l
b
r
ai
n
wav
e
. Data lev
e
l o
f
fo
cus an
d
b
r
ain wav
e
d
a
ta is wh
at will b
e
th
e in
pu
t to
th
e fu
zzy
logic. T
h
e out
put of this system form
speed dri
v
e m
o
tor
on each wheel chair. The outp
ut
of the syste
m
in the
fo
rm
of a ro
b
o
t
vel
o
ci
t
y
o
b
t
ai
ned f
r
om
a gi
ve
n P
W
M
DC
m
o
t
o
r. Fu
zzy
l
ogi
c fl
o
w
cha
r
t
i
s
sh
o
w
n i
n
Fi
gu
re 6.
Fi
gu
re
6.
F
u
zz
y
Lo
gi
c Fl
o
w
c
h
art
3.
3.
1. Fuz
z
y
fi
cati
on
Data lev
e
l
o
f
fo
cu
s and
b
r
ai
n
wav
e
d
a
ta tran
sm
itted
b
y
th
e m
o
b
ile
min
d
wav
e
receiv
e
d
b
y
a
com
put
er
or
P
C
. Th
e
dat
a
i
s
t
h
e
n
pr
ocesse
d i
n
t
h
e
F
u
zzy
fi
cat
i
on t
h
e
pr
ocess
o
f
c
h
an
g
i
ng t
h
e
val
u
e
of
t
h
e
sens
or
output data (c
risp i
n
puts) i
n
to the
form
of
fuzzy
sets according to the m
e
m
b
ershi
p
function. In t
h
e
researc
h
u
s
ed t
w
o i
n
p
u
t
s
as
m
e
m
b
ershi
p
fu
nct
i
on
of
f
u
zz
y
t
h
ey
are l
e
vel
conce
n
t
r
at
i
o
n am
pl
i
t
ude Fi
gu
re 7
and
ave
r
a
g
e
of
am
pl
i
t
ude Fi
gu
re
8. M
e
m
b
ers
h
i
p
o
f
m
o
t
o
r
o
u
t
p
ut
i
s
s
h
o
w
n
i
n
Fi
g
u
r
e
9.
As f
o
r t
h
e
bra
i
n wa
ve dat
a
i
n
p
u
t
ha
s 3 l
i
n
gui
st
i
c
val
u
es,
nam
e
ly
Low,
M
e
di
um
, Hi
g
h
t
r
a
p
ezoi
d
me
m
b
ersh
ip
fun
c
tio
ns.
Mem
b
ersh
i
p
fun
c
tions can be
seen in the
picture.
In t
h
i
s
wo
rk
, t
h
e
out
put
sy
st
em
i
s
usi
n
g F
u
zzy
Su
gen
o
m
odel
s
. T
h
e
out
put
of
t
h
i
s
sy
st
em
i
s
m
a
de
there are two that right DC motor sp
ee
d an
d
l
e
ft
DC
m
o
t
o
r
speed
. Fo
r t
h
e
sy
st
em
out
put
i
n
t
h
e fo
rm
of spee
d
h
a
s
7
lingu
istic v
a
lu
es: slowest, slow
er, slow, norm
al, fast, faster,
fastes
t
.
M
e
m
b
ershi
p
of
M
o
t
o
r Out
put
Fi
gu
re
7.
M
e
m
b
ers
h
i
p
F
unct
i
on
o
f
Lev
e
l Con
c
en
t
r
atio
n
Fi
gu
re
8.
M
e
m
b
ers
h
i
p
F
unct
i
on
o
f
Ave
r
a
g
e Brai
nwave
s
Fi
gu
re
9.
M
e
m
b
ers
h
i
p
o
f
M
o
t
o
r
Out
put
3.
3.
2 Def
f
uz
yf
i
c
ati
o
n
The
fi
nal
st
ep
i
s
def
f
uzi
f
i
cat
i
on, m
a
ppi
ng
t
h
e f
u
zzy
o
u
t
p
ut
val
u
es g
e
ne
rat
e
d at
t
h
i
s
st
age t
o
t
h
e
in
feren
ce ru
les q
u
a
n
titativ
e o
u
t
pu
t v
a
lu
es. In
d
e
sign
ing
th
i
s
wh
eelch
ai
r ro
bo
t d
e
ffu
zificatio
n
p
r
o
cess Weigh
t
Ave
r
a
g
e m
e
t
hod a
n
d
t
h
e
o
u
t
p
ut
o
f
t
h
e
pr
oce
ss de
ff
uzi
f
i
cat
i
o
n
f
o
rm
on
any
DC
m
o
t
o
r
spe
e
d.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Desi
g
n
a
n
d
I
m
pl
eme
n
t
a
t
i
o
n
o
f
Wheel
ch
ai
r C
ont
r
o
l
l
e
r B
a
se
d El
ect
roe
n
ce
p
hal
og
ra
m …
(
M
.I.
Arz
a
k)
2
883
3.
4.
PID
C
o
ntr
o
l
l
e
r
In
th
is sch
e
m
e
PD is u
s
ed
as a co
n
t
ro
ller for co
n
t
ro
lling
the sp
eed
o
f
a DC
m
o
to
r to
a wh
eel ch
ai
r.
PD co
n
t
ro
ller i
s
represen
ted
by eq
u
a
tion
(4
an
d
5
)
.
Wh
ere th
is con
t
ro
l will d
e
term
in
e th
e o
u
t
p
u
t
o
f
th
e
PWM
to
b
e
prov
id
ed
to
th
e
DC m
o
to
r. Feed
b
a
ck
fo
r
PD con
t
ro
l is ob
tain
ed from
th
e ro
tary enco
d
e
r sen
s
o
r
.
Med
o
t
a
trial erro
r is u
s
ed
to g
e
t t
h
e
v
a
lu
eof
k
p
and
kd
effectiv
e when
im
p
l
an
ted
i
n
to
t
h
e system
.
.
(
4
)
or
an
ot
he
r e
q
u
a
t
i
on
usi
n
g t
r
a
n
sfe
r
fu
nct
i
o
n:
1
(
5
)
3.
5.
Sof
t
w
a
re
De
si
gn
Features
incl
uded in s
o
ftwa
re
applications s
u
ch as:
a.
Feat
ures m
i
nd
wave c
o
m
m
uni
cat
i
on bet
w
ee
n com
put
er
wi
t
h
m
obi
l
e
and
m
i
croco
n
t
r
ol
l
e
r. A
feat
u
r
e t
h
at
can connect c
o
m
puter with
comm
unication bet
w
een
contro
ller wh
eelch
air an
d
co
m
p
u
t
er
with
m
o
b
ile
m
i
ndwa
v
e.
b.
Features
to
dis
p
lay graphics
i
n
real tim
e
EEG signal.
c.
Featu
r
e to
show th
e d
i
rectio
n of th
e wh
eelchair b
a
sed
on
EEG si
g
n
a
l cond
itio
n
s
.
GU
I
desi
g
n
o
f
t
h
e s
o
ft
wa
re ca
n
be see
n
i
n
Fi
gu
re
1
0
.
Fi
gu
re 1
0
. G
U
I
Desi
gn
4.
R
E
SU
LTS AN
D ANA
LY
SIS
To r
un t
h
e w
h
eel
chai
r co
nt
ro
l
i
s
based on i
n
f
o
rm
at
i
on t
h
e conce
n
t
r
at
i
o
n l
e
vel
t
h
ro
u
gh
EEG si
g
n
al
.
The
dat
a
o
f
co
ncent
r
at
i
on l
e
v
e
l
i
s
obt
ai
ne
d f
r
om
t
h
e devi
ce
m
i
ndwa
v
e t
h
a
t
sho
w
i
n
g t
h
e l
e
vel
co
nce
n
t
r
at
i
on
o
f
user. T
h
e conc
entration level
is represe
n
ted by a val
u
e
b
e
tween
0
-
10
0.
0
is th
e cu
rrent co
nd
itio
n
s
do
no
t
co
n
c
en
trate
while 1
0
0
is t
h
e cu
rren
t
state o
f
b
e
ing
fu
ll co
n
c
en
tration
.
Test
i
ng i
s
d
o
n
e
repeat
edl
y
f
o
r
one
wee
k
. B
a
s
e
d o
n
t
h
e test data can be anal
yzed that the precision
of
t
h
e use
of f
u
zz
y
l
ogi
c. Tr
ue r
e
sp
ons m
eans whe
n
user i
n
t
e
nt
ed t
o
t
u
r
n
l
e
f
t
, t
u
rn
ri
g
h
t
,
st
r
a
i
ght
f
o
r
w
ar
d
or st
o
p
t
h
en si
st
em
answer as
re
ques
t
and t
e
st
ed
da
t
a
m
eans from
al
l
t
e
st
i
ng or
expe
ri
m
e
nt
t
h
at
was d
one
. Te
st
i
ng
dat
a
eq
ual
s
t
o
t
r
ue
res
p
ons
pl
u
s
fal
s
e
res
p
o
n
s.
The precisi
on of
the use of
fuzzy
logic
∑
∑
100%
8
5
.
71%
C
once
n
t
r
at
i
o
n
l
e
vel
i
s
o
b
t
a
i
n
ed
fr
om
EEG
si
gnal
t
h
at
i
s
c
a
pt
u
r
ed
f
r
om
Neu
r
osky
b
r
ai
nwa
v
e m
o
d
u
l
an
d th
en
it is
tran
sm
it
ted
to
ap
p
lication
o
n
co
m
p
u
t
er to
b
e
represen
tated
as its lev
e
l
.
Fro
m
th
e Tab
l
e
2,
Table
3, a
n
d
Table
4 ca
n
be seen that as
the
great
er
valu
e
o
f
co
m
b
in
atio
n
o
f
lev
e
l co
ncen
tration
and
brai
nwa
v
e si
g
n
a
l
so i
t
can be
m
a
de di
ffe
re
nc
e easl
y
bet
w
een the action wheelchait to
m
ove
fo
r
w
ar
d, t
u
r
n
l
e
ft
,
and
tu
rn
rig
h
t.
Based
on the
expe
rim
e
ntal results on a
wheelchai
r, t
h
e
wheelchair has
a va
lu
e of
K
P
an
d KD
ar
e
di
ffe
re
nt
at
ea
ch set
poi
nt
.
A
val
u
e
pe
r sec
o
n
d
of
t
h
e
r
o
t
a
ry
p
u
l
s
e e
n
co
der i
s
use
d
a
s
feed
bac
k
f
o
r t
h
e P
I
D
co
n
t
ro
l.
Fro
m
th
e Tab
l
e
5
sh
owed
th
at
for g
e
tting
PWM v
a
lu
e
o
f
bo
th
righ
t wh
eel an
d
left wh
eel u
s
ed
di
ffe
re
nt
val
u
e
o
f
KP
(P
r
o
p
o
r
t
i
onal
C
o
nst
a
nt
)
a
n
d K
D
(D
eri
p
at
i
v
e
C
o
ns
t
a
nt
) fo
r every
set
poi
nt
(SP)
t
o
be
stab
le. Th
e
v
a
l
u
e
o
f
KP an
d
KD was ob
tained
b
y
trial and
erro
r un
til stable co
nd
ition
.
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:
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08
IJECE Vol. 6, No. 6, D
ecem
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: 2878 – 2886
2
884
Table 2.
Res
ponse of Wheelc
h
air when
Forward
C
o
mmand
No
L
e
vel concentr
ation
Brainwave
Speed Wheel Righ
t
(m
/s)
SpeedWheel
L
e
ft(m
/s)
Respons
1
10
35
0
0
Stop
2
18
30
0
0
Stop
3
37
32
3.
4
3.
4
Str
a
ight
4
20
29
0
0
Stop
5
48
22
5.
6
5.
6
Str
a
ight
6
55
35
7
7
Str
a
ight
7
68
38
9.
6
9.
6
Str
a
ight
8
71
40
10
10
Str
a
ight
9
85
15
12
12
Str
a
ight
10
98
26
12
12
str
a
ight
11
100
20
12
12
str
a
ight
12
100
18
12
12
str
a
ight
13
90
29
12
12
str
a
ight
14
95
33
12
12
str
a
ight
15
51
34
6.
2
6.
2
str
a
ight
16
45
38
5
5
str
a
ight
17
40
40
4
4
str
a
ight
18
30
29
2
2
str
a
ight
19
85
30
12
12
str
a
ight
20
23
36
0.
6
0.
6
stop
21
38
37
3.
6
3.
6
stop
Tabl
e 3.
R
e
s
p
o
n
se of
Wheel
c
h
air
when Turn Right Command
No
Level concentrati
o
n
Brainwave
Wheel
Right
Wheel
Lef
t
Respons
1
24
55
0.
6
0.
8
Right
2
65
68
5
`9
Right
3
85
78
8
12
Right
4
92
125
8
12
Right
5
87
130
8
12
Right
6
33
99
0
2.
6
Right
7
45
62
1
5
Right
8
67
78
5.
4
9.
4
Right
9
87
56
8.
8
12
Right
10
45
145
3.
7
2.
3
Lef
t
11
55
38
7
7
Str
a
ight
12
53
64
2.
6
6.
6
Right
13
50
95
2
6
Right
14
84
71
8
12
Right
15
67
155
9.
4
5.
4
Lef
t
16
66
47
7.
5
9.
2
Right
17
32
52
1.
3
2.
5
Right
18
21
55
0.
2
0.
2
Str
a
ight
19
33
68
0
2.
6
Right
20
34
65
0
2.
8
Right
21
45
89
1
5
Right
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8-8
7
0
8
Desi
g
n
a
n
d
I
m
pl
eme
n
t
a
t
i
o
n
o
f
Wheel
ch
ai
r C
ont
r
o
l
l
e
r B
a
se
d El
ect
roe
n
ce
p
hal
og
ra
m …
(
M
.I.
Arz
a
k)
2
885
Tabl
e 4.
R
e
s
p
o
n
se of
Wheel
c
h
air
when Turn Le
ft Comm
a
n
d
No
Level concentrati
o
n
Brainwave
Wheel Right
Wheel Lef
t
Respons
1
100
250
12
8
Lef
t
2
89
159
12
8
Lef
t
3
55
157
7
3
Lef
t
4
48
110
1.
6
5.
6
Right
5
78
132
8.
2
11
Right
6
45
157
5
1
Lef
t
7
64
168
8.
8
4.
8
Lef
t
8
57
755
7.
4
3.
4
Lef
t
9
59
498
7.
8
3.
8
Lef
t
10
66
569
9.
2
5.
2
Lef
t
11
75
357
11
7
Lef
t
12
98
299
12
8
Lef
t
13
59
200
7.
8
3.
8
Lef
t
14
48
268
5.
6
1.
6
Lef
t
15
49
127
1.
8
5.
8
Right
16
59
100
3.
8
7.
8
Right
17
67
99
5.
4
9.
4
Right
18
88
357
12
8
Lef
t
19
90
458
12
8
Lef
t
20
100
257
12
8
Lef
t
21
89
225
12
8
Lef
t
Tabl
e
5. R
e
s
u
l
t
o
f
P
D
C
ont
rol
l
er
SET
POIN
T
Right W
h
eel
Lef
t
Wheel
Value P
W
M
KP
KD
Value P
W
M
KP
KD
SP 1
100
1
30
100
1
30
SP 2
200
1
30
200
1
30
SP 3
325
2
40
325
2
40
SP 4
450
3
100
450
3
100
SP 5
600
5
50
600
5
50
SP 6
800
6
70
800
6
70
5.
CO
NCL
USI
O
N
Based
on
testin
g, th
e wh
eelch
air ab
le to
mo
v
e
well.
If
we in
crease our lev
e
l o
f
con
centratio
n
of th
e
wh
eelch
air sp
eed
will also
i
n
crease.
For th
e m
o
m
e
n
t
we ar
e co
mm
an
d
e
d
to
tu
rn
righ
t or tu
rn
left
wh
eelch
a
ir
can the
n
e
x
ecute the comm
an
d prope
rly. Fuzzy logic use
d
to
wo
rk
well. Fu
zzy log
i
c that co
n
s
ists
o
f
21
ru
l
e
base
d data input conce
n
tration a
n
d brain
waves
from
m
i
n
d
wave
that produces
output
on each DC
m
o
tor
sp
eed
h
a
s
85
.71
% accu
r
acy.
Using
of filter
sh
ou
l
d
b
e
im
p
l
e
m
en
ted
;
it is i
n
tend
ed
t
o
m
i
n
i
m
i
ze n
o
i
se o
n
th
e
b
r
ai
n
wav
e
data. To
m
i
n
i
m
i
ze
no
ise
d
a
ta from
th
e EE
G
wav
e
s
will b
e
m
o
re accu
r
at
e and sim
p
lify th
e pro
cess
of
dat
a
a
n
al
y
s
i
s
.
Using EE
G
se
nsors t
h
at ha
ve m
o
re than
one c
h
annel
ele
c
trode, t
h
is ca
n
be
used as a
standa
rd for
data obtained
m
o
re accurately. The us
e
of
m
o
re than one channel m
a
kes it
possible to
detect the im
a
g
ination
o
f
th
e
hu
m
a
n
br
ain
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Print.
BIOGRAP
HI
ES OF
AUTH
ORS
Mochamad Ilman Ar
z
a
k
receiv
e
d the Bach
elor
Degree of Ele
c
tr
ica
l
Engine
ering
from
Telkom
University
, Ban
dung, Indonesia in November 2014.
He joined Electroni
cs and Intelligence
Robotic Research Group (EIRRG) from 2011 -2
014 in
Telkom Univesity
.
His r
e
search
inter
e
sts
are
embedded
s
y
stem andRobotics.
Unang Sunar
y
a
rece
ived th
e M
a
s
t
er Degree of
E
l
ec
tric
al T
e
l
eco
m
m
unication En
gineer
ing from
Telkom Univers
i
ty
, B
a
ndung, I
ndonesia in
Februa
r
y
2012. He join
ed
as a
Lecturer
in th
e
department of Electronics and Communicatio
n
Engineering
of Telkom University
, in 2010
.
Supervisor of E
l
ectron
i
cs and
Int
e
llig
ence
Robot
i
c
Research
Group (EIRRG) from
2010 -2015
in
Te
lkom Unive
s
ity
.
His re
sea
r
ch inte
re
sts a
r
e wireless sensor netw
ork, embedded
s
y
s
t
em,
Robotics, and
Signal Processing.
Sugon
do Hadiy
o
so
re
ce
ived t
h
e
M
a
s
t
er
.in El
ectr
i
ca
l-T
e
le
com
m
unication Eng
i
neer
ing
from
Telkom Univer
sity
, Bandung,
Indonesia in Marc
h 2012. He joined as a Lectur
er in the
department of Electronics and Communicatio
n
Engineering
of Telkom University
, in 2010
.
W
h
ere he is
cu
rrentl
y
m
e
m
b
er
s
of Biom
edica
l
Instrumentatio
n Research Gro
up in Telkom
Unives
it
y. His
r
e
s
earch
int
e
res
t
s
are wir
e
les
s
sen
s
or network, em
bedded s
y
s
t
em, logic design
on
FPGA and biomedical
engin
eering.
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