vision based intelligence wheel chair

8/11/2019 vision based intelligence wheel chair

1/26


2/26

VISION BASED INTERFACE SYSTEM FOR HANDS FREE CONTROL OF AN INTELLIGENT WHEEL CHAIR

The user head may be out of the ima"e view, or only the profile face is in the captured

ima"eA

The face color is user-dependent, and may chan"e dramatically in varyin" illumination

conditionsA

The user may have different facial appearances, such as mustache and "lassesA and The bac+"round may be cluttered when Ws move in the real world&

n this report, a novel head "esture based interface (58) is developed for our Ws,

namely :obo#hair, based on the inte"ration of the daboost face detection al"orithm(6rads+i,

0119) and the #amshift ob*ect trac+in" al"orithm (.iola and Bones, 2')& n this process, head

"esture reco"nition is conducted by means of real time face detection and trac+in"&

This report presents an ntelli"ent Wheelchair(W) control system for the people with various

disabilities& To facilitate a wide variety of user abilities , his system involves the use of face-

inclination and mouth-shape information ,where the direction of an W is determined by the

inclination of the users face, while proceedin" and soppin" are determined by the shapes of the

users mouth& The system is composed of electric powered wheelchair, data acquisition board,

ultrasonicCinfrared sensors, a P# camera and vision system& Then the vision system o analy=e

users "estures is performed by three sta"es > detector, reco"ni=er and convertor&

n the detector, the facial re"ion of the intended user is first obtained usin" daboost A

thereafter the mouth re"ion is detected based on ed"e information&

The e?tracted features are sent to the reco"ni=er, which reco"ni=es the face inclination

and mouth shape usin" statistical analysis and @-means clusterin" , respectively

These reco"nition results are then delivered to the convertor to control the wheelchair

PVP Siddhartha Intit!t" #$ T"%hn#'() *an!r!) Vi+a(a,ada - Pa"e2


3/26


/. ROLE OF IW0S

/.1 WHAT IS THE VITTAL ROLE PLAYED BY IW0S

With the increase of elderly and disabled people, a wide ran"e support devices and care

equipment has been developed to help improve their quality of life (D3/) & n particular,

intelli"ent wheelchairs (Ws) have received considerable attention as mobility aids& Essentially,

Ws are electric powered wheelchairs (EPWs) with an embedded computer and sensors, "ivin"

them intelli"ence& i"ure 0 shows the various Ws

Fi'!r" 12 Int"&&i'"nt Wh""&%hair 3IW4 3a4 GRASP La5#rat#r( S6art Chair ) 354

Wh""&%hair #$ Y!ta7a "t. a&) 3%4 Na8 Chair.

Two basic techniques have been required to develop Ws> 0) auto navi"ation techniques

for automatic obstacle detection and avoidance, 2) convenient interfaces that allow handicapped

users to control the W themselves usin" their limited physical abilities& While it is important to

develop a system that enables the user to assist in the navi"ation, the system is useless if it

cannot be adapted to the abilities of the user& or e?ample, in the case a user cannot manipulate astandard *oystic+, other control options need to be provided&

/./ RELATED RESEARCH

%o far many access methods for Ws have been developed and then they can be classified

as intrusive and non-intrusive& They are summari=ed in Table 0& ntrusive methods use "lasses, a

http://www.jneuroengrehab.com/content/6/1/33/figure/F1http://www.jneuroengrehab.com/content/6/1/33/figure/F1


4/26


headband, or cap with infraredCultrasound emitters to measure the userFs intention based on

chan"es in the ultrasound waves or infrared reflect & n contrast, non-intrusive methods do not

require any additional devices attached to userFs face or head&

TABLE 12 IW CONTROLS IN LITERATURES

Intr!i8" Int"r$a%" 2

INTELLIGENT

WHEELCHAIR

FEATURE DEVICE SUPPORTING

COMMANDS

&/hen,et, al 5ead

3rientation

Tilt %ensors, icroprocessor 8o, 6ac+, /eft, :i"ht

%3 Pro*ect Eye 8a=e Electrode 8o, 6ac+, /eft, :i"ht

Wheelsley Eye 8a=e nfrared %ensors,


5/26


WHEELCHAIR COMMANDS

oshida ace

8o, /eft, :i"ht, :otate

i?in" #ommands>

8o-/eft, 8o-:i"ht

s shown in Table 0, voice-based and vision-based methods belon" to the nonintrusive

methods& .oice control is a natural and friendly access method, however, the e?istence of other

noises in a real environment can lead to command reco"nition failure, resultin" in safety

problems& ccordin"ly, a lot of research has been focused on vision-based interfaces, where

control is derived from reco"ni=in" the userFs "estures by processin" ima"es or videos obtained

via a camera& With such interfaces, face or head movements are most widely used to convey the

userFs intentions& When a user wishes to move in a certain direction, it is a natural action to loo+

in that direction, thus movement is initiated based on noddin" the head, while turnin" is

"enerated by the head direction& 5owever, such systems have a ma*or drawbac+, as they are

unable to discriminate between intentional behavior and unintentional behavior& or e?ample, it

is natural for a user to loo+ at an obstacle as it "ets close, however, the system will turn and "o

towards that obstacle&

/.9 THE TECHINAL IDEA

ccordin"ly, we develop a novel W interface usin" face and mouth reco"nition for the

severely disabled& The main "oal of the present study is to provide a more convenient and

http://www.jneuroengrehab.com/content/6/1/33/table/T1http://www.jneuroengrehab.com/content/6/1/33/table/T1


6/26


effective access method for people with various disabilities& or accurate reco"nition of the userFs

intention, the direction of the W is determined accordin" to the face inclination, while

proceedin" and stoppin" are determined by the shape of the mouth& This format was inspired

based on the operation of car, as the userFs face movements correspond to the steerin" wheel,

while the userFs mouth corresponds to the bra+e and "as pedal& The mechanisms prevent an

accident in the case the user instinctively turns their head to loo+ at an obstacle, thereby ma+in"

safer& oreover, the proposed control mechanisms require minimal user motion, ma+in" the

system more comfortable and more adaptable for the severely disabled when compared to

conventional methods&

The W system consists of acial eature !etector (!etector), acial eature :eco"ni=er

(:eco"ni=er), and #onverter & n our system, the facial re"ion is first obtained usin" daboost

al"orithm, which is robust to the time-varyin" illumination& Thereafter the mouth re"ions are

detected based on ed"e information& These detection results are delivered to the :eco"ni=er,

which reco"ni=es the face inclination and mouth shape& These reco"nition results are then

delivered to the #onverter, thereby the wheelchair are operated& To assess the effectiveness of the

proposed interface, it was tested with G' users and the results were compared with those of other

systems& Then, the results showed that the proposed system has the superior performance to

others in terms of accuracy and speed, and they also confirmed that the proposed system can

accurately reco"ni=e userFs "estures in real-time&

9. IW0S ARCHITECURE AND ITS OVERVIEW

9.1 SYSTEM3IW0S4 ARCHITECTURE



7/26


The proposed W is composed of electric powered wheelchair, data acquisition board,

and a P# camera and vision system& data acquisition board (!D-board) is used to process the

sensor information and control the wheelchair& The !D-board and a vision system are

connected via a serial port& n our system, a EPW-!E%E & care :ider



8/26


!D 6oard > #ompile Technolo"y %!D-!'EH

nput device > /o"itech (7' I '9)


9/26


i"ureGdescribes the process to reco"ni=e userFs "estures, where the reco"nition is

performed by three steps> !etector, :eco"ni=er, and #onverter& irst, the facial re"ion is obtained

usin" the daboost al"orithm, and the mouth re"ion is detected based on ed"e information&

These detection results are then delivered to the :eco"ni=er, which reco"ni=es the face

inclination and mouth shape usin"K-means clusterin" and a statistical analysis, respectively&

Thereafter, the reco"nition results are delivered to the #onverter, which operates the wheelchair&

oreover, to fully "uarantee user safety 0 ran"e sensors are used to detect obstacles in

environment and avoid them& n what follows, the details for the respective components are

shown&

FIGURE 92 Th" O8"ra&& Ar%hit"%t!r" #$ th" Pr#:#"d C#ntr#& S(t"6

;.1 FACIAL FEATURE DETECTOR2 DETECT USER


10/26


or each frame of an input streamin", this module locali=es the facial re"ion and mouth

re"ion, and sends them to the :eco"ni=er& The facial re"ion is obtained usin" the daboost

al"orithm for robust face detection, and the mouth re"ion is obtained usin" ed"e information

within the facial re"ion&

or application in a real situation, the face detection should satisfy the followin" two

requirements>

t should be robust to time-varyin" illumination and cluttered environments and

t should be fast enou"h to supply real-time processin"& Thus, the daboost al"orithm is

used to detect the facial re"ion&

This al"orithm was ori"inally proposed by .iola and has been used by many researchers& The

daboost learnin" method is an iterative procedure for selectin" features and combinin"

classifiers& or each iteration, the features with the minimum misclassification error are selected,

and wea+ classifiers are trained based on the selected features& The daboost learnin" method

+eeps combinin" wea+ classifiers into a stron"er one until it achieves a satisfyin" performance&

To improve the detection speed, a cascade structure is adopted in each of the face detectors, to

quic+ly discard the easy-to-classify non-faces& This process is illustrated in i"ure '&

i"ure$shows some face detection results& To demonstrate its robustness, the face

detection method was tested with several standard !6s such as .@ !6 & oreover, it was

tested on the data obtained from real environment& i"ures$(a)and $(b) show the results for

.@ !6s, respectively& nd i"ures $(c)is the results for online streamin" data& s seen in

i"ure$, the proposed method is robust to the time-varyin" illumination and the cluttered

environments& To reduce the comple?ity of the mouth detection, it is detected based on the

position of the facial re"ion usin" the followin" properties>

The mouth is located in the lower re"ion of the face and The mouth has a hi"h contrast compared to the surroundin"s&

Thus, the mouth re"ion is locali=ed usin" an ed"e detector within a search re"ion estimated

usin" several heuristic rules based on the facial re"ion& The details for the search re"ion are "iven

in our previous wor+ by the current authors&

http://www.jneuroengrehab.com/content/6/1/33/figure/F4http://www.jneuroengrehab.com/content/6/1/33/figure/F5http://www.jneuroengrehab.com/content/6/1/33/figure/F5http://www.jneuroengrehab.com/content/6/1/33/figure/F5http://www.jneuroengrehab.com/content/6/1/33/figure/F5http://www.jneuroengrehab.com/content/6/1/33/figure/F5http://www.jneuroengrehab.com/content/6/1/33/figure/F4http://www.jneuroengrehab.com/content/6/1/33/figure/F5http://www.jneuroengrehab.com/content/6/1/33/figure/F5http://www.jneuroengrehab.com/content/6/1/33/figure/F5http://www.jneuroengrehab.com/content/6/1/33/figure/F5http://www.jneuroengrehab.com/content/6/1/33/figure/F5


11/26


FIGURE ;2 O!t&in" #$ Fa%" D"t"%ti#n Uin' Ada5##t A&'#rith6

FIGURE =2 Fa%" D"t"%ti#n R"!&t 3a4 Th" R"!&t $#r MMI DB)354 Th" R"!&t $#r VA*DB) 3%4 Th" R"!&t $#r On&in" Str"a6in' Data

i"ure7shows mouth detection results& %ince the detection results include both narrow ed"es

and noise, the noise is eliminated usin" the post-processin"&



12/26


FIGURE >2 Th" M#!th D"t"%ti#n R"!&t 3a4 Ed'" D"t"%ti#n R"!&t 354 N#i" R"6#8"d

R"!&t

;./ FACIAL FEATURE RECOGNI?ER 2 RECOGNI?E FACE

INCLINATION AND MOUTH SHAPE OF THE INTENDED

USER

This module reco"ni=es the userFs face inclination and mouth shape, both of which are

continuously and accurately reco"ni=ed usin" a statistical analysis and template matchin"& s a

result, the proposed reco"ni=er enables the user to control the wheelchair directly by chan"in"

their face inclination and mouth shape& or e?ample, if the user wants the wheelchair to move

forward, the user *ust says L8o&L #onversely, if the user wants the wheelchair to stop, the user

*ust says L


13/26


To minimi=e the inertia, the derivative is ta+en with respect to& ccordin"ly, the

orientationcan then be obtained by equation (2)&

(2)

whererr,cc andrc are the second moments, the respective of which are defined

as and & f the value of M is less than , this

means that the user nods their head slantin" to the left& 3therwise, it means that the user nods

their head slantin" to the ri"ht& i"ure Jshows the reco"nition results for the face inclination&

FIGURE 2 Th" R"%#'niti#n R"!&t F#r Fa%" In%&inati#n. 3A4 Th" C#66and O$ T!rn-

L"$t) 3B4 Th" C#66and O$ T!rn-Ri'ht.

To reco"ni=e the mouth shape in the current frame, template matchin" is performed, where the

current mouth re"ion is compared with mouth-shape templates& These templates are obtained

byK-means clusterin" from 00' mouth ima"es&K-means clusterin" is a method of classifyin" a

"iven data set into a certain number of clusters fi?ed a priori& n this e?periment, multiple mouth-

shape templates were obtained, which consisted of 7 different shapes of L8oL and L


14/26


FIGURE @ 2 Th" 6#!th ha:" t"6:&at". 3a4 G# 6#!th ha:" t"6:&at" and) 354 Uh6

6#!th ha:" t"6:&at".

The results of the comparin" the templates with a candidate are represented by matchin" scores&

The matchin" score between a mouth-shape template and a candidate is calculated usin" the

5ammin" distance, where the 5ammin" distance between two binary strin"s is defined as the

number of di"its in which they differ& 5ere the matchin" scores for all the mouth-shape templates

and a mouth candidate are calculated, and the mouth-shape template with the best matchin"

score is selected&

;.9 CONVERTER2 TRANSLATE USER


15/26


FIGURE > Data A%!iiti#n 5#ard 3SD-DA;E4

The commands "iven from the user interface are passed to the control pro"ram runnin"

the wheelchair throu"h the serial port& The board pro"ram then controls the speed and direction

of wheelchair by modifyin" the volta"e passin" throu"h the wheelchair&

Table2shows command map between wheelchair movement and output volta"e& The

proposed system is able to control both the direction and the velocity of the wheelchair, as the

user can produce a different output volta"e by chan"in" their mouth shape or face orientation& n

addition to simple commands, such as "o-forward, "o-bac+ward, turn-left, or turn-ri"ht, the

proposed system can also "ive a mi?ture of two simple commands, similar to *oystic+ control&

or e?ample, the wheelchair can "o in a '$ de"ree direction by combinin" the "o-forward and

"o-ri"ht commands&

http://www.jneuroengrehab.com/content/6/1/33/table/T3http://www.jneuroengrehab.com/content/6/1/33/table/T3


16/26


TABLE /> OPERATION VOLTS OF INTELLIGENT WHEELCHAIR

COMMANDS OUTPUT 1 OUTPUT /

8o 2&$.NG&J. 2&'$.

6ac+ 0&2.N2&'$. 2&'$.

/eft 2&'$. 0&2.NG&J.

:i"ht 2&'$. 2&'$.NG&J.

%top 2&'$. 2&'$.

The interface system of W was developed in P# platform> the operation system is

Windows HP and #P< is Pentium 0&J 85=& The #amera is /o"itech, which was connected to the

computer usin" the TESTING GROUPS

STAGE TIME NUMBER

EPW USAGE

COMPUTER USAGE3434

ble-6oiled


17/26


The e?periments were performed by two steps& irst, the performance of the proposed

system is presented, which was tested in various environments& The effectiveness of the proposed

system is then discussed in comparison with other methods&

EPERIMENT I2 TO MEASURE THE ACCURACY OF OUR INTERFACE

or the proposed system to be practical in the real environments, it should be robust to

various illuminations and cluttered bac+"rounds& Thus, the proposed method was applied to

detect and reco"ni=e the userFs facial features in a comple? bac+"round& i"ure 0shows that the

facial feature detection results& The scenes had a cluttered stationary bac+"round with a varyin"

illumination& s seen in i"ure0, the results accurately detected the face and mouth, confirmin"

the robustness to time-varyin" illumination, and low sensitivity to a cluttered environment&

FIGURE 1 2 Fa%" and 6#!th d"t"%ti#n r"!&t&

With the proposed system the userFs intention is represented by the inclination of the face

and shape of the mouth, ma+in" the accurate reco"nition of these features crucial&

http://www.jneuroengrehab.com/content/6/1/33/figure/F10http://www.jneuroengrehab.com/content/6/1/33/figure/F10http://www.jneuroengrehab.com/content/6/1/33/figure/F10http://www.jneuroengrehab.com/content/6/1/33/figure/F10


18/26


i"ures 00(a)and00(b)show the reco"nition results for the face inclination and the mouth

shapes, respectively& s shown in these fi"ures, they are continuously and accurately reco"ni=ed&

FIGURE 11 2 Fa%" and 6#!th r"%#'niti#n r"!&t& 3a4 $a%" in%&inati#n r"%#'niti#n) 354

6#!th ha:" r"%#'niti#n.

To quantitatively evaluate the performance of the proposed system, we as+ed each user to

perform contain commands, such as "o strai"ht, stop, turn left or turn ri"ht and repeat the action

$ times& Table 'shows the avera"e time ta+en to detect the face and facial features, then

reco"ni=e them in an indoor and outdoor environment& s a result, the avera"e time ta+en to

process a frame was about 72 ms, allowin" the proposed system to process more than 0$

framesCsec on avera"e (07 framesCsec in indoor and 0' framesCsec in outdoor)& Table $ shows the

reco"nition rates of the proposed interface for the respective commands& The proposed systemshows the precision of 0O and the recall of 17&$O on avera"e& Thus, this e?periments proved

that the proposed system can accurately reco"ni=e userFs intentions in real-time&

TABLE ; 2 PROCESSING TIME 3.MS4

http://www.jneuroengrehab.com/content/6/1/33/figure/F11http://www.jneuroengrehab.com/content/6/1/33/figure/F11http://www.jneuroengrehab.com/content/6/1/33/table/T5http://www.jneuroengrehab.com/content/6/1/33/figure/F11http://www.jneuroengrehab.com/content/6/1/33/figure/F11http://www.jneuroengrehab.com/content/6/1/33/table/T5


19/26


STAGE INDOOR OUTDOOR

ace !etection G G2

outh !etection 0$ 09

ace nclination :eco"nition 2 2

outh %hape :eco"nition 0$ 07

Total 72 79

TABLE = 2 PERFORMANCEEVALUATION RESULTS

COMMANDS RECALL PRECISION

/eft Turn &19 0

:i"ht Turn &1' 0

8o %trai"ht &17 0

%top &19 0

i"ure02shows some snapshots for the proposed system to be applied on various

environments& 3utdoor environments have a time-varyin" illumination and more comple?

bac+"round, as shown in i"ures 02(b)and02(d)& 5owever, despite those comple?ities, the

proposed system wor+ed very well in both environments& n particular, in case of someone

comes to tal+ the user (in i"ure 02(c)), our system can accurately discriminate between

intentional and unintentional behaviors, thereby preventin" potential accidents, when the user

instinctively turns their head to loo+ at a person&

http://www.jneuroengrehab.com/content/6/1/33/figure/F12http://www.jneuroengrehab.com/content/6/1/33/figure/F12http://www.jneuroengrehab.com/content/6/1/33/figure/F12http://www.jneuroengrehab.com/content/6/1/33/figure/F12http://www.jneuroengrehab.com/content/6/1/33/figure/F12http://www.jneuroengrehab.com/content/6/1/33/figure/F12http://www.jneuroengrehab.com/content/6/1/33/figure/F12http://www.jneuroengrehab.com/content/6/1/33/figure/F12


20/26


FIGURE 1/ 2 IW C#ntr#& On R"a& En8ir#n6"nt&

=./ EPERIMENT II2 TO COMPARE WITH OTHER INTERFACES



21/26


To prove the efficiency and effectiveness of the proposed W interface, it was also

compared with other systems& 5ere, two methods were adopted, one is headband-based method

and the other is method usin" face trac+in" & Then, the former belon"s to the intrusive method

and the latter belon"s to the vision-based method& n the headband-based method, the "o-and-

stoppin" is controlled by noddin" userFs head to the front or to the rear, and the direction is

chan"ed by noddin" userFs head to the left side or to the ri"ht side& n such system, the head

motions are measured throu"h a headband that includes an accelerometer sensor& 3n the other

hand, a face-based interface detects userFs face in the first frame and trac+s it continuously, and

then userFs face are detected usin" s+in-color model&

i"ure0Gshows the control commands for respective methods& When visually inspected,

our system requires the smaller motions than others& This tells us our system is more comfortable

and suitable to the severely disabled&

FIGURE 19 2Int"&&i'"nt Wh""&%hair in:!t 6"th#d&

or the practical use by the severely disabled, such systems should be operable on both

indoor and outdoor environments& Thus, the three systems were evaluated across indoors and

outdoors, chan"es in time of day and weather conditions& %uch conditions are summari=ed in

Table7& nd some test maps in indoor and outdoor environments are shown in i"ure 0'&

TABLE >2 TEST ENVIRONMENTS

http://www.jneuroengrehab.com/content/6/1/33/figure/F13http://www.jneuroengrehab.com/content/6/1/33/table/T7http://www.jneuroengrehab.com/content/6/1/33/figure/F14http://www.jneuroengrehab.com/content/6/1/33/figure/F13http://www.jneuroengrehab.com/content/6/1/33/table/T7http://www.jneuroengrehab.com/content/6/1/33/figure/F14


22/26


PLACES TIME AND ILLUMINATION

Ind##r (!aytime, fi?ed illumination)

(4i"ht time, fi?ed illumination)

O!td##r (!aytime, time-varyin" illumination and a shadow)

(4i"httime, -)Ind##r t# #!td##r) #r 8i%" 8"ra (!aytime, time-varyin" illumination and shadow)

FIGURE 1; 2 S#6" EJa6:&" #$ T"t Ma:& 3A4 O!td##r T"t Ma:) 3B4 Ind##r T"t Ma:&

The participants to navi"ate each map 0 times usin" three interfaces& The performances

for three interfaces were then evaluated in terms of the accuracy and speed& n those e?periments,

the face-based method was tested in only the indoor environments, due to its sensitivity to the

time-varyin" illumination& s mentioned above, it used the s+in-color model to e?tract userFs

face, so it is very sensitive to illumination chan"es&

Tables J and9show the summari=ed performance comparisons for the three methods&

Table Jshows the avera"e times ta+en to reach the destination for three interfaces& mon" three

methods, the face-based method is the slowest, whereas there is no si"nificant difference

between our method and the head-band method& eanwhile, Table9shows the reco"nition

accuracies of three methods, where the proposed-based method produced the best performance

with an avera"e accuracy of 17&$O, while the face-based method had an accuracy of 9J&$O and

http://www.jneuroengrehab.com/content/6/1/33/table/T8http://www.jneuroengrehab.com/content/6/1/33/table/T9http://www.jneuroengrehab.com/content/6/1/33/table/T9http://www.jneuroengrehab.com/content/6/1/33/table/T8http://www.jneuroengrehab.com/content/6/1/33/table/T9http://www.jneuroengrehab.com/content/6/1/33/table/T9


23/26


24/26


inimal user motion, ma+in" the proposed system more adaptable to the severely

disabled, than conventional methods&

:obustness to a cluttered bac+"round and the time-varyin" illumination and

ccurate reco"nition of user intention based on discriminatin" between intentional and

unintentional behavior&

To prove these advanta"es, the proposed system was tested with G' users on the indoor

outdoor environments& 5owever, to "uarantee full user safety, the proposed system also needs to

be able to detect and avoid obstacles automatically thus further research in this area is currently

underway&

. CONCLUSION

nthis report, a few interestin" views were discussed re"ardin" an W system , which

is adaptable, efficient androbust for disabled people with physical abilities& n this system , the



25/26


direction of W is determined by users face inclination, while "oin" and stoppin" is determined

by users mouth shape& This report describes the desi"n and implementation of a novel hands

free control system for Ws& This W system provides enhanced mobility for the elderly and

disabled people who have very restricted limb movements or severe handicaps&

robust 58 is desi"ned for vision-based head "esture reco"nition of the :obo#hair

user& The reco"ni=ed "estures are used to "enerate motion control commands so that the

:obo#hair can be controlled accordin" to the users intention& To avoid unnecessary movements

caused by user loo+in" around randomly, the 58 is focused on the central position of the

wheelchair to identify useful head "estures&

The proposed system was tested with G' users in indoor and outdoor environments

and the results were compared with those of other systems, then the results showed that the

proposed system has superior performance to other systems in terms of speed and accuracy&

Therefore, it is proved that proposed system provided a friendly and convenient interface to the

severely disabled people&

The future research will be focused on some e?tensive e?periments and evaluation of

58 in both indoor and outdoor environments where cluttered bac+"rounds, chan"in" li"htin"

conditions, sunshine and shadows may brin" complications to head "esture reco"nition&

@. REFERENCES

1. Bournal of 4euroEn"ineerin" and :ehabilitation2009-Bin %un Bu, unhee %hin, and Eun

i @im



26/26

vision based intelligence wheel chair

Documents

Transcript of vision based intelligence wheel chair