(IJACSA) Iteratioal Joural of Advaced Computer Sciece ad Applicatios, Vol. 6, No. 9, 5 Electrooculogram Sigals Aalysis for Process Cotrol Operator Based o Fuzzy c-meas Jiagwe Sog Raofe Wag* Guaghua Zhag Chaoxig Xiog Leya Zhag Cubag Su Abstract Biomedical sigals of huma ca reflect the body's task load, fatigue ad other psychological iformatio. Compared with other biomedical sigals, electrooculogram (EOG) has higher amplitude, less iterferece, ad is easy to detect. I this paper, the EOG sigals of operator s were aalyzed. Wavelet trasform was used to remove the highfrequecy artifacts. The fuzzy c-meas was adopted to detect the eye blik peak poits of EOG. After that, eye blik iterval () of operator was calculated. Four EOG features (the average of, variace of, stadard deviatio of ad variatio coefficiet of ) were extracted. Fially, the relatioship betwee EOG features ad operator s fatigue, effort, axiety ad task load were aalyzed. The experimetal results illustrate that EOG features had some relatio to the operator s fatigue, effort, axiety ad task load respectively. Keywords electrooculogram; fuzzy c-meas; operator fuctioal state; fatigue I. INTRODUCTION Fatigue is a atural physiological pheomeo, which is related to the operator s labor itesity, work eviromet, health, mood ad so o. It is a kid of self-adjustmet ad protectio fuctio of huma body. I recet years, with the rapid developmet of ecoomy ad the faster pace of life, may of the operators cotiue to work i the state of fatigue [,]. Cosequetly, may accidets occurred because of operator fatigue. Ad also the axiety ad effort level of the operators ca affect their work performace. The operator s performace degradatio may be the reaso of some serious disasters, particularly i safety-critical applicatios. The assessmet of operator metal workload would help predict the operator s periods of high operatioal risk. Early warig based o predictio of the operator s metal workload is oe of the effective methods to prevet such accidets. Therefore, the accurate assessmet of operator metal workload is the key to provide early warig successfully. Curret scietific research foud that, the huma body cotais may types of biological sigals which ca reflect i parts the operator s fatigue, axiety, metal workload, etc. [-5] After comparig the characteristics of electroecephalogram (EEG), electrocardiogram (ECG) ad electrooculogram (EOG), the authors aalyzed EOG sigals of operator s. Features of EOG sigals cotai eye blik rate, eye blik amplitude, closig time, eye blik variability, etc. [6] The researchers thought that EOG is associated with operator s metal workload because eye blik is associated with facial erve ad the awakeig is associated with cetral erve. Whe task load icreases, the visual demad is likely to icrease which may lead to eye blik rate decrease. Simultaeously, the eye activity may icrease. Whe the operator focuses o the tasks, his/her eyes blik rate may decrease ad the eye blik iterval may icrease [7,8]. I [9,], the authors aalyzed the driver s eye blik iterval. It is illustrated that whe the driver is fatigue, his/her eyelid ptosis arises, eye blik amplitude decreases. I other words eye blik iterval decreases while closig time icreases. This paper is to ivestigate the EOG sigals associated with the operator fuctioal state (OFS), like fatigue, axiety, effort ad task load. The experimetal eviromet of process cotrol was desiged, ad the EOG sigals of operators uder multi-level of task loads were sampled. Fuzzy c-meas (FCM) was adopted to detect the eye blik peak poits of EOG. Ad the EOG features to assess the huma fatigue, axiety, effort ad task load were foud through the study of EOG. Thereby, these idices will be used to predict the fuctioal state of the operator. Early warig system ca be costructed to prevet accidets which are caused by huma factors. 8 P a g e
(IJACSA) Iteratioal Joural of Advaced Computer Sciece ad Applicatios, Vol. 6, No. 9, 5 The rest of the paper is arraged as follows. Sectio described the data acquisitio experimet i details. I sectio, the Fuzzy c-meas algorithm is preseted. I sectio, the EOG sigal aalyzig process ad results are illustrated, ad the discussios are preseted. Sectio 5 makes the coclusios ad gives the future works. II. EXPERIMENT A. Data Acquisitio The experimets were carried out o Automatio-ehaced cabi air maagemet system (AUTOCAMS), developed by Hockey ad his colleagues [] ad modified by Lorez []. AUTOCAMS simulates a life support system of a spacecraft. It requires the subject to maage a semi-automatic system which is to maitai comfortable atmospheric coditio of the cabi. There are four sub-systems correspodig to four critical parameters of atmospheric coditio (oxyge cocetratio, carbo dioxide cocetratio, temperature, pressure). The subject was istructed to cotrol some of the sub-systems maually accordig to the experimetal desig. Subjects were traied o the maual process cotrol tasks for over 7 hours, followed by a further 5 hour experiece of cotrollig the system uder a rage of coditios. After that, the subjects are regarded as the adept at operatig AUTOCAMS. Three subjects (SA, SB, ad SC) participated i this experimet. The task of the subject was defied to cotrol some of the four subsystems so as to maitai their relevat variables withi target rages. There are 8 cotrol coditios (C~C8) i the experimet. The first (C) ad last (C8) coditios are five miute baselie testig coditios i which the subjects task was to moitor the operatio of AUTOCAMS. The amout of maually cotrolled subsystems i other cotrol coditios (C~C7) is,,,,,, respectively ad each coditio lasts for 5 miutes. For a experimetal sessio, the cyclic loadig method resulted i a loadig phase (C~C) ad a uloadig phase (C5~C7). Every subject was required to do the experimet for three times (e.g. SA, SA, SA) at the same time o differet days to avoid circadia effect. B. EOG Data The subjects vertical EOG sigals were recorded by Niho-Kode EEG physiological recorder from sites, which are placed above ad uder the subject s right eye ad refereced to liked earlobes. The EOG data were sampled at 5 Hz. III. METHODS A. Fuzzy c-meas Fuzzy c-meas is a data clusterig method i which each data poit belogs to a cluster, with a degree specified by a membership grade [-5]. FCM partitios a collectio of vectors X x, x,, x, where xk xk, xk,, x ks is the kth vector of X. FCM is based o miimizig a objective fuctio, which respects to fuzzy membership U, ad set of cluster cetroids, V. The FCM objective fuctio ca be described as, c m Jm ( U, V ) ik dik k i () c s. t. ; [,]; ik ik ik i k where, c is the umber of clusters. ik is the membership fuctio of vector x k to the ith cluster. d ik xk v i ca be either Euclidea distace or oe of its geeralizatios such as Mahalaobis distace. Euclidea distace is used i this study. The steps of the FCM method are explaied i brief: Firstly, the ceters of each cluster V(), fuzzy coefficiet m, umber of cluster c ad termiatio threshold are radomly selected. Secodly, the membership matrix with the followig equatio: ik ik jk j () t U is computed ( ) c m t d ( t) d ( t ). () Thirdly, the cth cluster cetroid is calculated with the followig equatio: m i ik k ik k k m v ( t ) ( t) x ( t ). () The process is stopped if ( t) ( t) V V. I this paper, the FCM algorithm was used to separate EOG sigals ito subsets, i.e. the higher amplitude subset ad the lower amplitude subset. The the eye blik peak of EOG ca be idetified. IV. RESULTS AND DISCUSSION The process of EOG aalysis is as follows: ) Deoisig. Wavelet Trasform was used to remove the high frequecy artifacts of EOG sigal. The parameter settigs of wavelet deoisig method are: wavelet base: DB, decompositio level:. Soft threshold deoisig method ad ubiased threshold estimatio method was adopted. ) Fuzzy c-meas. The EOG sigal was divided ito two clusters. The eye blik peak was foud i the higher amplitude cluster. Figure shows the eye blik peak poit of EOG sigals. It ca be see that the proposed method ca fid the eye blik peak correctly. The eye blik peaks for all the EOG sigals were detected by usig the same method. ) Feature extractio. The eye blik iterval was calculated. Figure shows the mea eye blik iterval of miute of SC. The teds to decrease (icrease) whe task load icrease (decrease). For the purpose of aalyzig the relatio of EOG to OFS, four features of, i.e. average of (A), variace of (V), stadard deviatio of (SD) ad variatio coefficiet of (VC), were extracted. The value of each feature i each coditio was 9 P a g e
average of (s) variatio coefficiet of (s) stadard deviatio of (s) variace of (s) (IJACSA) Iteratioal Joural of Advaced Computer Sciece ad Applicatios, Vol. 6, No. 9, 5 calculated to observe its chages i the whole experimet. Figure -6 show the chages of these four features i the whole experimet of SC. Figure 7 shows the umber of subsystems uder maual cotrol ad subjective measuremets i eight cotrol coditios of SC, respectively. - -..6.8...6.8 x Fig.. The results of fuzzy c-meas: lie EOG sigal;* eye blik peak poit 8 6 7 6 5 coditio Fig.. Variace of 8 7 6 5 coditio Fig. 5. Stadard deviatio of 6 8 time (mi) Fig.. per miute 5 coditio Fig.. Average of The average of, variace of ad stadard deviatio of decrease whe the operator s task load icreases from C to C. Ad these features show the same chages whe the operator s task load decrease from C5-C8. It is clear that the average of, variace of ad stadard deviatio of has a sigificat relatio to operator s task load..8.6...8 coditio Fig. 6. Variatio coefficiet of The variatio coefficiet of icreases from C-C5, which may be iflueced by the operator s icreasig effort ad axiety. I C ad C8, AUTOCAMS rus automatically. The operator was required to moitor the system. Thus there was t ay task load for the operator. I C-C7, the operator eeds to cotrol some of the subsystems. From all the four features, it could be see that there are sigificat differeces betwee o-load coditios (C ad C8) ad uder load coditios (C-C7). The differece of features betwee C ad C8 may be caused by the subject s fatigue. P a g e
subjective measures (%) umber of subsystems uder maual cotrol (IJACSA) Iteratioal Joural of Advaced Computer Sciece ad Applicatios, Vol. 6, No. 9, 5 Fig. 7. The umber of subsystems uder maual cotrol ad subjective measuremets These fids idicate that the chages of task load ad metal workload ca be reflected by features of EOG. The correlative aalysis is used to quatitatively aalyze the relatioship betwee EOG features ad operator task load, axiety, effort ad fatigue. Table shows the results of correlative aalysis betwee EOG features ad operator fatigue, axiety, effort ad task load of SC. Operator s fatigue ca be reflected by variatio coefficiet of. All these four features ca be idices of operator s axiety. Average of, variace of ad stadard deviatio of have sigificat relatios with operator s effort ad task. TABLE I. Operator Fuctioal State codito (a) 8 6 CORRELATION ANALYSIS BETWEEN EOG FEATURES AND OPERATOR FUNCTIONAL STATE Stadard Deviatio of Average of Variace of Fatigue -. -.6 -.56 -.68 Axiety -.7 -.77 -.8 -.76 Effort -.97 -.85 -.9 -.69 Task -.95 -.78 -.89 -.6 TABLE II. fatigue axiety effort codito (b) Variatio Coefficiet of SIGNIFICANT EOG FEATURES RELATED TO OPERATOR FUNCTIONAL STATE No. Fatigue Axiety Effort Task load SA VC VC VC SA V, SD VC V, SD SA A, SD A, SD A, SD SB V, SD VC SB A,V A SB V, SD A,V SC V,VC V,VC V,VC V, SD SC V,VC V, SD A, SD A, SD SC V,VC A,VC A,VC A,VC The same aalysis was doe to the EOG sigals for all the subjects. Table shows the top sigificat EOG features (P<.5) for each subject. Variace of is a good idex of operator fatigue. Stadard deviatio of ca reflect the operator s axiety, effort ad task load. It could be see that the saliet features for differet subjects are differet owig to idividual differece. For subject SA, stadard deviatio of is a saliet feature of operator axiety, effort ad task load. For subject SB, variace of ca sigificatly reflect the chages of operator fatigue. For subject SC, variace of ad variatio coefficiet of are sigificat features of fatigue ad axiety. Average of ca reflect the chages of effort ad task load. V. CONCLUSION This paper aalyzed the EOG sigal of operator. Fuzzy c- meas was used to detect the eye blik peak of EOG. Average of, variace of, stadard deviatio of ad variatio coefficiet of were extracted. The relatio of EOG features ad operators fatigue, axiety, effort ad task load was aalyzed. Saliet EOG features for each subject are successfully extracted. The experimet results show that it s ecessary to assess the operator fuctioal state usig persoal saliet features. The results of this paper could be used to idetify operator fuctioal sate i future works. Future works will focus o two areas. First, the authors will do more experimets ad further aalysis o EOG, i order to evaluate the idividual differeces. Secod, models that describe the relatio betwee EOG features ad operator fuctioal state will be costructed. ACKNOWLEDGMENTS This work is partially supported by the Shaghai Uiversity Youg Teachers' Traiig Scheme Fuds ZZGJD, the Natioal Nature Sciece Foudatio uder Grat No. 69 ad No. 68. REFERENCES [] G. Borghii, L. Astolfi, G. Vecchiato, D. Mattia, F. Babiloi, Measurig europhysiological sigals i aircraft pilots ad car drivers for the assessmet of metal workload, fatigue ad drowsiess, Neurosci. Biobehav. R. Kidligto, vol., pp. 58-75, July. [] R. Wag, J. Zhag, Y. Zhag, X. Wag, Assessmet of huma operator fuctioal state usig a ovel differetial evolutio optimizatio based adaptive fuzzy model, Biomed. Sigal Process. Cotrol, Kidligto, vol. 7, pp. 9-98, September. [] K. L. Saroj, C. Ashley, Driver fatigue : electroecephalography ad psychological assessmet, Psychophysiol. Malde MA, vol. 9, pp. -, May. [] I. Kather, S. C. Wriessegger, G. R. Muller-Putz, A. Kubler, S. Halder, Effects of metal workload ad fatigue o the P, alpha ad theta bad power durig operatio of a ERP (P) brai computer iterface, Biol. Psychol. Amsterdam, vol., pp. 8-9, October. [5] S. Rodriguez-Ruiz, P. M. Guerra, S. Moreo, M. Carme Feradez, J. Vila, Heart rate variability modulates eye-blik startle i wome with bulimic symptoms, J. Psychophysiol. Gottige, vol. 6, pp. -9, October. [6] H. A. Slagter, K. Georgopoulou, M. J. Frak, Spotaeous eye blik rate predicts learig from egative, but ot positive, outcomes, Neuropsychologia, Kidligto, vol. 7, pp. 6-, May 5. [7] M. J. Doughty, Further assessmet of geder- ad blik patterrelated differeces i the spotaeous eyeblik activity i primary gaze i P a g e
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