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Suvalee Ngamlertwattanakul

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Uthai Kuhapong, Mullica jaroensutasinee, and Krisanadej jaroensutasinee

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An Implementation of Web Portal for 3D Rendering Service Deployment and Access On Linux Clusters 20

Ekasit Kijsipongse, Suriya U-ruekolan, Sirod Sirisup, and Somthep Vannarat

CAD Implementation of Analog Circuit Optimization Tool Based on Multi-Objective Geometric Programming 25

Theerachet Soorapanth

Cloud Forest Climatic Characteristics of Tropical Montane Cloud Forest at Mt. Nom, Thailand 30

Peerasak Sangarun, Wittaya Pheera, Mullica jaroensutasinee, and Krisanadej jaroensutasinee

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Pramote Srisuksant, Rachapom Kienprasit, Seksun Sartsatit, jatupom Chim'ungrueng, Charuwalee Huadmai,Witsarawat Chantaweesomboon, and Saowaluck Kaewkam[1erd

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Me!. Arif Hasan and Chumnam Punyasai

Energy Efficiency of a CN G Retrofi t Light T ruck 48

Thanud Luangnarutai, and Raksit Thitipatanapong

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jatupom Chinrungrueng, Ronachai Pongthomseri, Songphol Dumnil, Saowaluck Kaewkamnerd,and Teerapol Silawan

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jittiwut Suwatthikul

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Opas Trithaveesak, Awirut Srisuwan, jakrapong Supadech,Chamdet Hruanun, and Al11pom Poyai

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PatimakornJantaraprim, Pornchai Phukpattaranont, Chusak Limsakul and Booncharoen Wongkittisuksa

Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University

ABSTRACT - This paper presents a study of signals that trend to produce false alarm in falldetection using Autoregressive (AR) model and Discrete Wavelet Transform (DWT). In theexperiment, there was a tri-axial accelerometer mounted on the trunk. Ten young subjects performedfour types of falls including forward fall, backward fall, left and right side fall. Ten elderly subjectsperformed six types of Activity of Daily Live (ADL) including sit-stand, stand-sit, sit-lie, lie-sit, benddown and walking for 2m. For 14 sets of data, the results showed that falls and ADL could beclassifiedusing AR2cD2 and AR3cD3. Falls could be distinguished from ADL with 70.83% and 75%sensitivity for AR2cD2 and AR3cD3, respectively and up to 85% specificity for both parameters.Moredata from the experiment to verify this method is ongoing research.

KEY WORDS - fall detection, accelerometer, autoregressive, wavelet transform, elderly

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~ '1'1'1lJ~ tlmJ f) 'lJ~~do'l'i1'1'11'1111:'l~m 1lJI~d~ 1lJUf1~~ ~ff 'Itl1mftV'lJ 'k.I 'U'U q

U5'lJ~ 1'li'W~~'Itl1~1Jl'l'fllltl1lllltl'W'I'I'I10 '1'11tlU1'l'fllltlW!HIV

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lJlllld l-UlJM~~f11';j~';jdllIlU~~Vlm~ 28 'l'i1U'i~f1tl1J~,tJ ~l~lJ

lu.u1'l'l'l~'I 4 'l'i1 'l'i1~lJ~lll.u1'l'l'1W~ltJ 3 'l'i1 ti':lJln1J'lJtl~2111• I , I

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lu.u1'l'l'l~'I

~llffmJll{jf)'l~\,!'Utl'lf)lj\9lj11)<ijUf)l'Hl'lJ i1'lCJ1~Discrete Wavelet Transform hlJf1U Autoregressive Model

fftytylillfllllJl1 ~~'V'lTI~iJll'W11UlJ

I'11'Ntlf)l'imlil~uNfl'V'linfl

'j1.1~4 UNUfnWn'j':i:'JJJunnrnffWtlllfllfn7:IJd-ltlwtfifiJ• u u

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~a~'VllIilllJinfl1J illfl'W 'W11'1'W'1Jil~11'tytylill fllllJlHtI'V'l1ifl~flm1

QOUllUllfl'i l:;'Ii'Hl'V'll'iliillllil{~W'1 flil rhfflJU'i:;ff'Vli'1Jil~

Wavelet Decomposition llt1'1'l-hfflJU'i:;ff'Vli~II'1'IU'HlfflJU'i:;

ffl1~ Autoregressive flflflf~ i~iffflJU'i:;ff'Vli Autoregressive

OO'Hlillflffrurulill fllllJl1 ~~'V'lTIlfl tJlllH~ III Nl'Wfll'i'l1l DWT, u u

~1~Ht1'1~~'l1lfflJU'i:;ff'Vli Autoregressive ~lI'1'IU'Vlfl11'ilul'1'ltJ

lWH~ltJU'i:;1Yl'VlliitJlJ~illU l'1~llN'Wfll'V'lI'W1U~ 6

fftytylillfllllJ11 ~~'V'lTI~iJll'W11UlJ

I'11'Ntlf)l'im1il~uNfl'V'lmfl

'V'll'i liillllil{ ~W'1 ~ 11'1'illflfll'i 11fl1l:;'lifftytylill fllllJl 1 ~

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ffruru 1ill fllllJl 1 ~~ 'V'lTIIfl tJlll'i ~lltl:; ffrurulill fllllJl 1 ~~'V'lTI~Nl 'Wu u u u

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Daubechies order 3 ~'i:;I'1U~l~'l 6 'i:;I'11J lfltJfhlt'Wfl1'l1'~il'1Jil~

'V'll'iliillllil{iJ1UllUU ARxcAy lltl:; ARxcDy l~il AR ll'Vl'W~il

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Autoregressive ri1'W cA 11'Vl'WfflJU'i:;ff'Vli Approximation '1Jil~

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Wavelet Decomposition lltl:; y 1l'Vl'W'i:;I'1U'1Jil~ Wavelet

Decomposition ~~mru~ y I'l'1ltlU 0 1111'fl~llllJ'Wfftytylill

fllllJl'i~~'V'lTIlfltJm~lllNl'W DWT

1'W~ 'WIIIil 'Wfll 'i ~ III 'Wfll'1'1 tJ1 fl 'i ~ ~ 1 tJU 'i :; 11'1 'Vllii tJlJ

'V'll'iliillll il {'1Jil~fftyty 1ill fllllJl 1 ~~'V'lTImrilif il:;\j flll ti~ilil flllJ'W

• • w '" '0'1 ,I '" )2 11'1'Wl'Vllfl'W('1Jill;jtlf'lflf'l'W 14 'ltfl, '1Jill;jtl'Vlfl11'ilU 14 'ltfl lltl:;

m:;illtJ 1'I1'iJ~l'Wl'W'1Jil~'V'll'iliill'lil{ ill flll~ tI:; vi 11'W.r;ill;jtli~11'il~

'ltfl'V'lil'l tl'WrllltfugNflN'Wlltl:;'Vlfl11'ilU~illU lflH~ltJ

,I '" "'1 '"~I ~ "'1 '" oJju'i:;1Yl'VlI'VltJlJ'Vl 'IHu'W'li'Wfl Backpropagation lJ flH11''il~I1J'W

~ NECTEC Technical Journal, NECTEC-ACE2009 Special Edition

.lllJlJ Multilayer Feedforward Network ~l\.JTW 2 iwJitl\.J iJ

Transfer Function i\.J'5wJitl\.JI1J\.J Tansig 1l~:::J\.JW1~~~I1J\.J

Purelin i\.Jfl1'J~f)~\.J 1fl'J~·lhEJ'I~ll'l.hf11~1\.J1\.J 1'1'1\.J\91i\.JJ\.J

cJitl\.Jll~~:::J\.Ji~ll~ 1 ii~5 1'1'1lJ\9Ii\.Jm)mhf11~1\.J1\.J 1'1'1\.J\9Ill~

~:::f1f~1:Y1'1'1f1J'Vm1iJl~tl{'I'Irt~ 'iJ:::~f)~\.J1f1H~lEJ~Ym1iJl~tl{

,!\.JJ1 50 f1f~ 1l~:::U\.JYif)f11J1'1'1'1!f)l1l'l'l1::: 1f1H~lEJ~i~f11

Linear Regression :lJ1f)f)11 0.95 (f11 Linear Regression ~~iJf11

"9 " '<f """1.1 d' "9" <f.OJ d1'U1!f)~ 1 llff\9l~11W1~~~'VIHf)H\.J:lJfIll'U1 !f)~W1~~~11J1'1'1:1JlEJ'VI

fl1'l'l\.J\91);f~ifl'vitl M'I~f11J1'1'1'1! f)~m:lJl:::mJ1:Y1'1'1flJ 1i''VI\91fftllJ

~tl 'I,j '111i~'I~~l\.J 1\.Jflf~;f~'I'I:IJ\9I~'VI\91fftllJll~~:::'I'l1) 1iJl~tl{

I'VllnlJ 1250 f1f~ (50*5*5) oUtlJ,!~~f)~\.Jll'fl:::'VI\91fftl1J'iJ:::~l\.Jf)l).~)1'iJfftllJ 1\91EJlll2-Fold Cross Validation ~1EJ 1\91EJflf~mf)i~

oUtl:IJ'fl'li\9l~ 1 11J\.JoUtl:IJ'fl~f)~\.J 1l'fl:::oUtl:IJ'fl'li\9l~ 2 11J\.JoUtl:IJ~'U •• 'U 'U •• 'U

t .d 31~ .d ~ 'j) d 'j)

'VI\91fftllJf1H'VI 2 i'l'l'Utll;j'fl'l1\91'V12 IlJ\.J'Utll;j'fl~f)~\.JIl'fl:::'UtlJ,!'fl'l1\91

~ 111J\.JoUtll;j'fl'VI\9lfftllJ'I11f)l)'VI\91fftllJJ1 3 f1f~l\.Jll~'fl:::mtlll'vitl

'I'I1fl1111 ~ EJ'Utl~r:-t~1:l'l'li 1111111ff\91~,j ):::i1'V1~ fl1'1'l1 \.J~,j 'UtJ~

Sensitivity ll'fl::: Specificity ~tl 'I,j

-ffl)jl)j1Wfl11:1Jl'i~1:l'l'li~~1\.Jm:::1J'1\.Jf)l) DWT lh:::l'1lJ~l~ '1

llff\9l~1'1~1'11tlci1~1\.J~,j~ 7

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cA2 002

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oA3 003

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oM cD4

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cA5 cD5

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cd cv o<::l t:::::>"

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(i'/l:J1.:}tf1 pmn1'i~·J.yl'11iN rf1:J.:}'lh(Jl.h~iJmJrimJtfmilJ

v/l:J1iJJ(i'/iJ{ ARxcAy ri11fFh Linear Regression mnn-h 0.95

o cv .:::. d' 0 QI

ll'fl::: 2 ff1'1'1'JlJ'I'ln1:IJI~tl) ARxcAy ll'fl::: ARxcDy 19I1:IJ"lfllJ

riJ\.Jlfl~tl~'I'I:IJlEJ x llff\9l~111m~~lEJ'Iliff1:1Jl)tl~loUll»1il

I .d d \f)!V' t

Linear Regression :lJ1f)f)11 0.95 'iJ1f)m)HVl I 'iJ:::l'I'I'UIfl11m

-ff:IJ,j):::i1'V1f Autoregressive ;f~el\.Jl'1lJ~ 2 ll'fl::: 3 'lJtl~ffl)jqjllll

fI'11:IJ1'i ~1:l'I'li1mJ~) ~'Iliff1:1Jl) \1'1111~1 f1) ~~ 1EJ,j):::{yl'VII~VlJ~

loU11\91EJiJLinear Regression :IJlf)f)11 O.95'1~ ,r'Ull~fl~11

'I'll) 1iJl~ tl {if'lli I'I'I:IJ1:::{Y:IJ1:Y1'1'1f lJIi'~)1'iJI1lJf)l) 1I:IJ'lJW~~1il

~ '1"Approximation 'Utl~ Wavelet Decomposition 'VJm~\9I1J11m. ,Linear Regression :IJlf)f)11 0.95 m)liJl~tl{1l~~ijll\.J11UlJ~~~

H~n'iJl1lJf)l)lI:IJ'I~ ll~el\.Jl'1lJ~ 2 'Utl~-ff:lJ1Jgihli

Autoregressive 'Utl~-ff:IJ,j):::i1'V1f Approximation 'lJil~ Wavelet

Decomposition 'VJ m :::l'1lJ'Ilim:lJl:::{Y:lJ1:Y1'I'IflJ i i'l9Ij 1\J~1Jfll'j~lJ

Irttl~'iJ1f) i~fl1 Linear Regression :IJlf)f)11 0.95 l\.JoUill;j'iflmlu

IV;EJ~'l1\91I~EJ1

v" C! 91 '" 91 "I 191 , '"nm1'f1EllllJeJ\l\9]'U'UtJ\lf11':i\9]':i1'iJ'iJlJf11':i'fl:lJ~~HJ 'b' Discrete Wavelet Transform ':i1:lJfllJ Autoregressive Model

~lfl\l1l'i1~~ 2 1l~~{l'W'l~11 filifll1.h~iY'Vlf Autoregressive

lf~v'U~u~ 2 ll"~ 3 'lJfl~if:JJ'l.h~iY'Vlf Detail 'lJfl~ Wavelet

Decomposition 'l'Jfl'i~~ulrr~l Linear Regression lllf1f111 0.95

I1mnll9lfl{ii~~ijll'l{) lUll~1l~l~I9l'il1l~Uf1l'i~ll'l~~'li'Wf1'W lrlfl

~~mu1'1'1l'i 1iill9l fl {~vll 1rrl fl H ~1tJ~1 'lJ'1 '1~~ f1~ 11fl fl 1rr

~11.n'U1m ~~ 1tJ~ ~~'lJ'1\9ll:JJl~fl'W'1'IJllJ'W~1'WTU :JJlf1~rlfll'VitJu f1U

I1mnll9leJ{1.h~~f1'Vl~~£nf1'W 'WU11~lJ'W AR2cD2, AR2cD3,

AR3cD211"~ AR3cD3 ~~"fffl\PIfl~fl~f1u.ij'm;J,,~ml'\PI~l'W'l1.J'lJfl~

Scatter Plot 1'W'l1.J~ 8 ~~.ij'm;J"1l1f1f1l'i~ll (.) ~fl'W.ij'l~~Wf1flflf1

~1f)~m;J"1l1f1i11lfl'i'illI9l1ll1.Jf1~ (+)

.li11111.J'11~4 'W1'i1ii~l9lfl{ YfUl11m~'\iltJ"ff1:JJl'itl~I.ij'119l1ll, , .~ '191 91 0 Q.I 0 1 'd ..• 1 Q.I Q.I..:S I"

l~ll'U 'IJ'IJ1~19l'W"ff1'l'!'iU1l1'Wl'W 'I'!'W\PI'l',Jf1fll'Vlllu'i \PI~'W'W1l~'If

1IlH~ltJ~ij I l'1'!'W\PIi~"fffl~i'WGJifl'Wl-dfl~1l1f1l1J'Wlm~~ltJ~

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06

05 t*04 + ...-

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08

0.7 + /0.6 +

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01 •0-04 -02 02 04 06 08 1.2 14

0.7

06

05

04

c) 03

01

00.6

0.6

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0.2

d) 0 ..

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1

zv.yf 8 Scatter Plot 'Uf)-1ifiIV'jg;ffnt'Autoregressive 'Uf)-1

tYJJV'jg;ffnt'Detail 'Uf)-1Wavelet Decomposition

f917JNrt 3 l.hdrn£mwn7Jf9InfHJlJn7HfJJ'lJfN AR2cD2,

AR2cD3, AR3cD2 Uil~ AR3cD3

~1J'1i1LIMlf Sensitivity Specificity

AR2cD2 70.83 85.91

AR2cD3 58.33 100.00

AR3cD2 45.83 90.91

AR3cD3 75.00 86.36

1l1f1Ntlfl11"'I1~fftl'lJfhJ.ijmJtl 14 'li~ ff1m1"t1ff1"u"l~11 f11~. .ff:lJU1"~if'l1f Autoregressive ,f~tl"W~'lJ~ 2 Utl~ 3 'lJtl~fft)Jt)J1W

fl 1 1:lJL~ ~ i:lVl-fil ~ tJ\9l1"~"lJJ 111'U"W1 114':lJ~ ff 1:lJ11"t1~ 1U"Wf1f111"ll:lJ

tl tl f11l1f1 nil f111:lJ\9l1:lJU f1$i"l~ 1"W'IJW~~ f11ff:lJU 1"~ if '11f

Approximation Utl~ Detail 'lJtl.:l Wavelet Decomposition 1"W'l'jf1

1"~~'lJ 1,ru"W1 114':lJ~ff1m1" t1~lU"Wf1f1nll:lJtltlf11l1f1nll f111":lJ

\9l1:lJUf1$i"l~ l~tJ AR2cD2, AR2cD3, AR3cD2 Utl~ AR3cD3

1,rNtlf111"~1.ij1~~~ff~ Utl~Mf11 Specificity mf1f111 85% ,f~ 4~ .m1"liJl\9ltl1 ri1"Wf11 Sensitivity lJril 70.83% Utl~ 75% ~hl1f'lJ

AR2cD2 U,,~ AR3cD3 \9l1:lJ~h~'lJ ~~Uff~.:lU"W1l14':lJl1ff1:lJnt1

i~~u 1iJl\9ltl1d\9l1"11l1l'lJfl11"ll:lJ 'l~tlV1.:l"l1"~\9l1:lJ~1"W1"W.ijtll;!tl

'I1~fftl'lJrl1'l1f'lJfft)Jt)J1W~lJU"W 11 ,r:lJ\9l1"lllll'lJfl11"ll:lJ~~Vim~ i"W

f111"'I1~tltl.:ldlJlVitJ~ 14 'li~ rn1"'I1~fftl'lJf)'lJffWW1W~iiu"W11,r:lJ• u u . ,

Q.I 31 Co 0 I .::::1 .:!.i ~ Q,I

\9l1"11l1l'lJf111"tl:lJN~Vitl1~1l1"W1"W:lJ1f1f111"WlVitltJ"WtJ"W

U1"~if'l1n mVi'IJtl~1~ f111"dl~:lJI$i:lJ

3I"'=' Q,I d.' ~ gI 31.<::toflW~ ~llltJ'lJtl'IJtl 'lJfJW1fl1"tl'IJ1tJtr"WtJfl 11:lJ!11l Vl1~~ 1"W1ff1f11 1":lJ

~"W~ (NECTEC-PSU center of excellence for rehabilitation

engineering) Iltl~'I'j"WUW cVl\9lfff1£llff~'lJtl1"Wflj"W'I11~i,r f1n.ff'l1'lJff'4"Wf111"'11m"W illtJiJ

and C. Chan, "Development of a Fall Detecting System for

the Elderly Residents", Proceedings of the 2nd

[6] P. Jantaraprim, P. Phukpattaranont, C. Limsakul, and

B. Wongkittisuksa, "Evaluation of Fall Detection for the

Elderly on a Variety of Subject Groups", international

".tS ~ iJ Q.J CJ} "1" G) gJ , Q.J

nmmfjlIUtJ\I\9I'W61Je)\If)ll\9ll1'j)'j)'lJf)l~ii:UJ !\9ICJ!'Jf Discrete Wavelet Transform l1:IJfl'lJ Autoregressive Model

Physics: Conference Series, Vol. 34,2006, pp. 1059-1067.

[10] N. Noury, A. Fleury, P. Rumeau, A.K. Bourke, G. O.