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KATAPENGAI{TAR
Assalamu'alaikr.rrn Wr. Wb
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Walbillahi Taufiq Walhidayah
Wassalamu'alaikum Wr. Wb,
Wassalam
Redaksi
Buletin IJtanrn Tahun 2004, Volunte I No. 3 : 177- /,83
Artificial Neural Networks for Peak Static Strength prediction
Nazaruddinl , Zu1rnjit, Adi Setiawan3
A bstralt
Peratnolun kekuatan statis ,sangallah penting untuk menyelesaikan ntasalah-masalah ergonomik terutama dal,"'nten?urangi teladinya musculoskeletal disrtrders ( MSD'S) dan cumulative trauma jisorder (CTD'S) da,'mmelakukan pekerjaan secara manual. Pada penelitian ini digunakan suatu leknik baru untuk peramalan Lekrs*slalis nranusia dalant kasus pengangkatan secara ntanual Sebelumnya, pendekotan statistik seperti regresi liruo-berganda telah digunakan. Di samping nrengusulkan suatu teknik baru, penelitian ini juga menguji kimampuzrneural nefwork's dalam perantalan kekuatan statis manu,sia dengan menggunakan ukuran-ukuro, tubrh. hieweneht'ork's clilatih oleh weight yang dibentttk dari data yapg diperoleh nelolui suatu survei. Didapati bahwa neuranelv'ctrks dopol dengan akurqt membqca datq dqn mqmpu dengan qkurol meramalkan dati tersebut. Deng*ntenggunakun pairutise t-test, didapati bqhwa perbedaan qntqra deta sebenarnya don data hasil ramalan aa;ottidak signi/ikan ( p-t,alue>0.05) clengan tingkat kepercayaan (CI) 9S%.
Kata kunci : Kekutan Statis, Peramalan, Artificial Neural Network, Musculoskeletal Disorders (MSD'sgPenanganan Manual
Abstract
S/utic"strength predictirtn i:s cruciql .fbr ergononric'.s.solution especially in reducing muscu[oskeletal disorden(MSD's) crnd ctrntulative trauma disorcler CfD',9 in manuel handling. ln this pqper propose a new techniquefwpretlictittg hunran stctlic strength for manual li/iing, Previously, slalistical approach such as multiple lineoregre'ssion.s has been used. Be,sides proposing a neu, technique, this paper is also examining the appticability qfartificial neural networks to predict u,orkers slatic strenglh. The neural networks was lrained by *"igl'ttt that ree;ecrealed./rom the data collected through o survey in Malaysia. Il u,as found that neural networks ian accuratehreading lhe data crnd /hus able to accurately predict the dala predicted. Using pairwise t-test, therewas insignificamtlifference betv'een predicting results and actuql data (p-value>0.05) with level of confident interval 95ok.
Keyword(s) : Static Strength, Prediction, Artificial Neural Network. Musculoskeletal Disorders (MSD,s), ManualHandlino
1. IntroductionErgonourics today is growing and changing.Developrnent stelns frorn increasing and irnprovingl<nowledge about the hurnan, and is driven by newapplications and new technological developments. Theword of ergononrics was derived from the Greek termsergon, indicating work and effort, and nornos, law orrules (Murrel, 1994). From basic sciences such associoloey. psyclrology, physiology, medicine,nrathernatics. etc., a group of more applied disciplinesdeveloped into the core of ergonornics such asanthropornetry, biomechanics, industrial hygiene, etc.This paper is corrcerned about biomechanics especiallyin using artificial neural networks as new model to solvein predicting the workers static strengths.The strength prediction is an important consideration.'ri hen solving ergononric problems. Many of researches
use regression method (Potvin et.al, 1992 and K.M.Bolte et.al, 1998) and others use polynomial method(McGill SM et.al, 1996) for predicting the humanstrength. As we know, regression method does not giveaccurate prediction for non linear modeling or complexmodeling. Previously, most of lifting strength values iscalculated based on the NIOSH lifting equation. TheNIOSH lifting equation is still being revised until rodaybecause it does not accurately calculate or predicthuman strength.Now days, artificial neural networks is very popular forpredicting. This is mainly due to their successes inrnodeling complex domain tasks as opposed totechniques such as linear regression and Kalman filters.Besides, artificial neural network modeling techniqueare very flbxible to apply, there are g"nerally no needsto make presumptions abbut the characteristic of static
|J : (-nrnntelce Dcpartmtnt, State Polytechrric l,hohsettmawe'' \lcchanical Dcpartrnent, Faculty of Enginccring, l-lNlNlA' Lhokseumawe
Artificial I\ieural Nelworks for Peak Static Strengtlr Prediction
strengtlr. The tlse of artificial neural network for
strength prediction has not been used recently'
Artiliciai neural networks have been seen involve in
various applicarions dotrlain. such as tnotion analysis
(Taha et,al, 1996), tl-ie prediction of stock trrarket
trading volrttrte (Weigencl, A.S. and B' Lebaron'1994),
currency exchange (f{el-enes et al'' 1993)' ellergy
consuuiption lMaiKay, I993). For ergonotnics solution'
an artificial neural network hits been used in reach
posture prediction (.lurrg ancl Park, 1994) and humatr
nrotion analysis such as liand grasping, walking'
lirnning (l-aha et.al. 1995) tsksioglu, et al (1996) used
rotir aitiircial neural netrvorl<s and statistical model for
ersonomic solution about prediction of pealt pinch
str-ength from a variety of factors, such as elbow and
shoulder flexion, age, weight' grip strength, and various
:r-m and hand dimensions. They found that the ANN
r.odel better at predicting peal< pinch strength than
.:itistical rnoclel. In this paper we study a type of static
r::ength whiclt has different flavors fi'onr those in the
.:rVe appl ications"-r: data come fi'orn the direct lreasurement through the
,,:re1,' of indr"rstrial workers static strength' The data
:,: set of anthropometric, age, and static strength which
r:re collectecl from industrial workers Static strerrgtlt
:-:liction is very usefitl il1 the design rvork task for the
::iels especially irr rrranual rlaterial handling'
l. Back Propagntion 'fechnique
s(net,):I + d-ntt'
In our study the number of inputs into the network are
variety, we try tel find the best combination of physical
dirnensions to fit the static strength. The output is
standing lifting strength for position with two-handed
pull 100 crn height level of the handle.
(5)
N+
Figure l. Topology of a
network
N+
fully connected neural
(
The network weights W;, ar€ updated using the back
propagation rnethod by minimising square error by the
trairring set:
d A tt 1,,\.E= ZE(k) = III/z)(Y -Y)' (6)
k-t k--l t=l
d"
ttrrtr€€{
jt3ry'
.r[b.
--: ar-tificial rreural networks are typically organized in
:.::s. Layers are rnade up 'bf a nurnber of K is the number of training sets. In this study the
r-.:::onnected ,nodes' rvhich contain an 'activation training sets are the inputs and outputs at the
..-::iou', These data will be used to find a correlation anthropometrics data and static strength data from
:,:-.i,3er.t the irrputs and outputs r.rsing a fully connected measurements. Yi are outputs of training sets
". , -. net.,vork. corresponding to lifting strength and grasp.-". ',Lllr' cotrnected network irsed to sitnr-rlate the data
-r- :: desct'ibed by the follorving set of equations:
=\ l<i<m, (l)3. Proposed Approach
The proposed approach is based on survey that
measurements of human static strength for example
using sorne of equiprnent for measure static strength do
direct measurements of the workers static strength.
Besides, the anthropometrics data also was collected to
know whether any correlation between anthropometrics
and static strengths. The anthropometrics data can be as
input and the output is only static strength data for
different size of anthropometrics data. Supposing the
output parameters are defined by the static strength
-i,'' '-tt"l
- :tet,)
,r,o1<N-l-r?. (2)
m<i <Nrn, (3)
l<i<n, (4)T[r!m
m
4rra
imG
', r:-= \ is the input and Y; is the out put' rr is tlre*!iT-r:=- of inputs. ir is ntltnber of outputs and N is
r .i-: integer less than rrr. The value of N I n decides
' :r '- -rr3r-o?n.uront in the netr.vork. The function 's in
r:' -: 1,1) is the signroid ftrnction:
Input
Nuzorudtlitr, Zuarni,,4tli Setiowan
oredicted (Yi) and the input as anthropometry and static
str.ngtl' (Xi) to find a function:Yi:.fiXi) 0)
The fr.rnctiorr / is rnapping the outputs and the inputs'
'iir. o,'tp",o 'can be static strengtlr fronr different
nositio'r such as knee bend, knee straight' sit etc'
il";; .ut., *. only take one output that is static
;;;.;il' oit*o-t'und"i pull 100-ln'rf:u'l of handle on
standfng lifting posture (Figure 3)' The inputs data X;
will consists of:
X'= Ittutut., lveight, artn reach, circunrference' hand
erc) (8)
The input components are not linrited to those
pr."l"r'iy describe bttt can be extended to irrcltrde other
pal'ameters such as age and other physical dimension
data as need arises. The inputs parameter will be created
for different nurnber of inputs and different types of
physical dlmension. Even though these data can be
predicte{ using linear regression method' but in the case
of predicted problem are more reliably using artificial
neural networks. The approach used here is illustrpte/ in
Figure 2.
4. Case StudY
Data was taken from 146 industrial workers across
Malaysia. The sample consists of 104 male (32 heavy
indusiries and 72 light industries) and 42 female (light
industries) workers. Subject was chosen randomly that
was involved in manual material handling'
Figure 2. Basic back-propagation
Age. weight. grasp strength and 9-different types
of arrthrJporretrics meaiLrrelnents frorn 52 types arrd 2
,fi'oui l2 type's lneasurelnents of static strength are
taken.
Table l. Physical dimensions measllrement
The anthropometry measurements are based on
literature by Kroemer (1999) and White (1964), whilst
the static sirengths based on literature by Chaffin and
Anderson (1991) and the guidelines by FAA. (Section
14, 1996). There are S [ina of physical dimension
measurement were taken, namely stature, knuckle
height, arm reach, knee height, wrist c^ircumference'
l,pi.t'un" circumference, crotch circumference' ankle
lir.u,rlf.r.n.., and lower thigh circumference (see
iable l). The other hand the strength data. were taken
onfV t,unaing strength for position with two-handed pull
100 cm height.levelof the handle
. Statltre
. l(nuckle height(NH)
. Arrn reach (ARF)
. Knee height (KH)o Ankle (AC)
o Wrist (WC)o Upper arm (UAC). Crotclr (CTC)o Lower Thiglr
(Lrc)
4rtificinl Neurd Networhs.for Penk Stnlic Slrenglh.Predicliott
Figure 3. Illustration of the power for 100 crn high level of the handle with twohanded pLrll in standing posture.
Tt-
_3
I
fr 5. Learning from Dnta and Simulating
ihe first step in this approaeh is retrieve physical
irmensiou data to be sirnulated, Whiehever methods are
:sed. the output data of training value for ANN is in:::rns of static strength data for standing lifting with
:.,.o-handed pr"rll on 100 cm height level of the handle
. ruure 3). Training is learning process by which inputs
.-.J oLrtpLrt data are repeatedly presented to tlre network'
.:rs process is rrsed to deterrnine the best set of weights
' : the network. r,vhich allerw the ANN to classify the
input vectors with a satisfactory level of accuracy. Inthis study we want to see wh€ther any correlationbetween types of physical dirnensions and staticstrength for standing posture with two-handed pull on100 crn height levelof the handle.From the result (Table 2) shows the static strength errorof male workers (heavy industry) for different number
of inputs of different physical dimension. It also showwhich part of the physical dimension have related to the
static strength,
T'able 2. Root Mean Square Error (RMSE) for difference cornbination of parameters input.ilr
r-i5.d-L
::.rililI
rrn.ici*-ref;[s,.
ag-e
:fNsrd pnl
\ I easured
\\ eielrt
S tatu le
\RFl .\(l
r lt
\t. lra
* tl'. Selectetl l'ttriable,s
': rnportant restrlt from this study is the artificial:--:. netwol'k cart trairl the physical dinrension data
. : -:;nlan static strength data altrost no significarrtly
different compare to the output. Besides, ANN also can
prove there are any relationship between physical
dirnension and human static strength.
Male (Lieht lndustriesFernale (Lieht Industries)
N azsru ddirr, Zu utti, Atli S et iaw an
M ale - Heavy lndustries
60
50
40
1 3 5 7 I l',l 13 ls 17 19 2'l 21 25 27 29 31
Num bcr ol gubjsct
+Msar'ur'd +-- Predicte d
o'-6
gF;Jo ttt
6Oot6E:
30
2C
10
0
Fieure 4. Cornparisons between measured and predicted for standing posture on l00cm
height level of the handle (Male in Heavy lndustries)
The neural networks also able to produced model from
human static strength in different position and different
dimension of arrthropometrics data' The tnodel is stored
as a set of neural tretwork weights generating during the
learning phase.
fn ng;re 3 above, shows that for male in heavy
indusiries, the input parameters with the smallest RMSE
(4.002) were age, weight, KH, UAC, -CTC,,and
AC'
Wittl ti. pairwise t-test with 95% confident level was
applied, tirere was no significant difference, between
pr.ai.t.a values and lneasurelnent values whereas p-'valtte
calculated was 0'456 (p<0'05)' Figure 4 shows
that results was approxitnately no difference cornpared
to actual measurements' As a result that the ANNs
model has capability with higher accuracy for predicting
the standing strength with l00cm height level of male in
light indusries.Figure 5 shows that the number of passes also affects to
thi result, There is any certain number of passes that
can nrake the smallest error' For the human static
strength of two-handed pull 100 cm levels in standing
postwe for heavy industries workers have the smallest
error when the number of passes is 20000.
6r
,l
Standing Strongth on 100cm Helght Favol
1 0o0o 1 6oOo 20000 25000 50000 1 00000 200000 500000
Number of Passes
fio6
goGit
=oot
5000
+Female(Lightlndustries) s--Msl6(Lightlndustri6s) Msl€(H€aWlndustries)
Figure 5. RMSE with Difference Nurnber of Passes (standing Lifting on l00cm)
The preceding nutnbers and types of. parameters
assurned for stirrding strength on 100cm height level for
male in light industiies was iderrtical with 50crn height
.level, however, tlre combination of parameters were not
exactly same. For male in light industries, the input
parameters with the smallest RMSE (3'848) were
Ailirtciol Nearsl Networks for Peok Stotic Strength prediction
exactly same with female in light industries such as
Grasp. By using the pairwise t-test with 9570 corrfidentlevel, it was found that there was no significantdifference between predicted valrtes and tneasurementvalues rvhereas p- value calculated was 0.508 Qt<0.05).
weight, stature, KH, NH, UAC, WC, CTC, AC, andThe Figure 6 shows that the accuracy of ANNs modelfor predicting wap high, and slightly differencecompared to actual measurements for male in Iightindustries.
M ale " Lig ht lnd ustries
- 50c45U';g|40
! JJ
5E eog6 zs
i*'oa x 15
I ro
r015 13 17 21 25 29 33 37 41 45 49 53 57
Num ber ot SubJ6c!
-+- M ea 6 ured --+.-... P redicted
FigLrre 6. Comparisons between measured and predicted for standing posture on l00cm heightlevel of the handle (Male in Light tndustries).
The earlier nurnbers and types of parameters assutned
ior standing strength on l00cm height level was age,,'reight, stature. KH, NH,, ARF, UAC, WC, AC, LTC,CTC, and Grasp, hor.vever, the combination offararneters were not exactly same, For fernale in lightrndustries. the input parameters with the smallest errorRMSE : 2.108) were weight, stature, KH, NH, UAC,
rI'C, CTC, AC, and Grasp.
The pairwise t-test with 95o/o confident level wereapplied, and found that there was significant differencebetween predicted values and measurement valueswhereas p-valtre calculated was 0.024 (p<0.05). Eventlrough it was significant difference using pairwise t-test, but in the Figure 7 shows the predicted resultsapproximately no difference compared to actualmeasurements.
Female - Light Industries
7 9 11 13 15 17 t92t 23 2527 2931 33 35 37 39 41
Numbe. of Subiccl
' +Measured , e-Predlcled,
Figure 7. Comparisott between Actual Measuretnent and Prediction for StandingLifting on Fleight l00cm (Female in Light Industries)
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^ tco Y ,^
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rurl
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N azr rw tldin, Z u orn i, A tli Sritinwn
6. Discussions
The artificial nettral network can be used to predict
lrurnan static strength for different subject's physical
dirnensions. fhese can be proved on the above graph
and table that there are not significantly different
between data tneasurement and ANN.At tlre human static strengtlr prediction for both male
and fetnale workers in light industries, age can affect
the human static strength. J'lre other hand the human
static stretrsth for rnale workers in heavy industries in
the sanre position, age is affect to the hurnan static
st rett gtlr.
From the results was shown that for the human static
strength prediction of rnale workers in heavy industries
need only 6 irrputs paranleter, but for trrale and female
workers in light indtrstries only 9 input to get the
snrallest error. Even between light and heavy industries
had difference rrtttrber of input, but there wgre no
significant different especially in the shape of graph'
Hswever, tlrere was still having any differences between
data outputs frorn ANN and data lneasurement' These
may caused there was not strong correlatiorr between
part of plr,vsical ditnension and hutnan static strength oftu'o-handed pull 100 clr level.
Generally, the simulation results using ANN have at
least two satrre values rvith the l'lleastlrelrent data for
each oLrtput paralneter. All the inputs have been passed
iteration 20000 times.
z. Conclusions
We conclLrde tlrat artificial neural network can form the
basis of physical ditnensiorr and weight for modeling
lrunran static strength and predict tlre human static
stren gtlr fbr d iff'erent phys ical d ilnerrsion.
The strength predicting rnodel that is developed willcreate into software, so that the users are easy to predict
the strength. Next research, we will test the data and the
resLrlt statistically and develop for predigting the other
positiorr of lil'ting strength and also dynarnic strength
nrodel of'u'orkers.
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Occupational Biomephanics, ,lohn lVilley & Son
I tt,'
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