Physiological data processing applied to active safety system s.

Physiological data processing applied to active safety system s. M a u rin M., K h ard i S. IN R E T S -L E N, case 24, 69 675 B ron cedex, France A B S T R A C T Eye closure rate (R EC) is to day considered as a convenient index in order to make the assessm ent o f driver s vigilance degree at the w hell. W e have recorded REC time variation for 12 subjects during a 2 hour driving periods in real conditions, and carried out specific processes. They allow us to observe differences betw een subjects with descriptive and non parametric statistic processing. D ifferences are mainly due to the driving task and the performance failure. W e have also observed an unexpected kind o f classification for drivers. IN T R O D U C T IO N The principal reason w hy hum an factors are the cause o f more than 90 % o f Road accidents lies in the lim ited reliability o f hum an capacities. T his high percentage show s up the interest o f studying driver physiological states, his behaviour and his car actions during an abnormal drive. Although crashes are relatively rare events, more than 50,000 individuals are killed in motor vehicle crashes annually in E urope against 4 0,0 0 0 in the United States (Leasure and Burgett). A nother 1.5 m illion are injured in E urope against 5 million in the United States, and the social costs exceed $ 95 billion annually against $ 137 billion. Accident studies carried out in France by B oussuge have revealed the effects o f drow siness and fatigue in the case o f trips undertaken on the highw ays during the period from 1979 to 1994. Thus, for a yearly sample o f about 300 killed in 1993, a loss o f vigilance w as the prim e cause o f lethal accidents (28 %). This can be com pared with the num ber o f deaths due to i) bursting o f tyres (6 %), ii) a failure to make proper allow ance fo r the w eather conditions (8 %), iii) a failure to keep a safe distance from the vehicle ahead com bined with dangerous overtaking (14 %), iv) exceeded speed limit (13 % ), v) attendance o f pedestrians (15 % ), vi) traffic in opposite directions (5 %), vii) varied obstacles (5 %), viii) others (5 %).Consequently our experimental investigations were performed to clarify the role o f the eyelid closure and driver s vigilance studies during m onotonous and prolonged driving ; here w e are dealing with eye closure measurement for diagnosing the d river s state, experim ental setup and statistics. -503-

In order to identify the characteristics that typify driving behaviour under a state o f reduced alertness, it is necessary to have reliable m ethods and param eters describing driver actions. Special attention w as focused on eyelid closure that is know n to vary according to the driv er's level o f alertness. M any authors treated eye closures and show ed that they could reveal inform ation about drivers im pairm ent s levels. Seko and al. assessed alertness levels and classified five levels as follow s : 1) continuous and rapid blinking, 2) sudden increase in blinking, 3) increase in slow blinking, 4) eyes close at tim es, and 5) eyes close for long intervals. Stem and al. have review ed blink characteristics linked to im pairm ent. A uthors concluded that fo r the periodic, spontaneous blink, the rate o f blinking is closely related to 'the m ental ten sio n ' o f the driver at the tim e, and that in all probability the m ovem ents constitute a kind o f relief m echanism w hereby nervous energy, otherw ise not utilised, passes into a highly facilitated path. This possibility is one that, with m inor m odifications, has been suggested by a num ber o f authors. They suggested that blinking involves efferent neural interactions between brain m echanism s responsible for the m uscles controlling the eyelids and other m uscle groups. W e agree w ith Stem and al., K nipling and W ierw ille and with a num ber o f investigators w ho have com e to the conclusion that blinks are a m eaningful reflector o f the effects o f the tim e o f the task. T he basic idea behind drow sy-driver detection device is to m onitor the driver unobtrusively by m eans o f an on-board system that can detect when the driver is im paired. D R IV E R D E S IG N Tw elve healthy drivers from 25 to 45 years old, with no neurophysiological problem s, voluntarily participated in o u r experim ents. All selected drivers had norm al driving habits, w ere not on any m edication, and had normal or corrected-to-norm al vision. They can be typically considered as a p a rt o f the driving population, having experience o f m ore than five years in driving and have been driving constantly until now. D rivers had to drive norm ally on IN R E TS circuit w ithout exceeding the "speed-lim it" o f 100 kph. T he driving task is diurnal and lasted tw o hour periods. D uring experim ents, observation on driver behaviour has been noticed and reported. Investigations w ere perform ed with rem unerated drivers. E X P E R IM E N T A L S E T U P T he experim ental vehicle used w as a left-hand-drive car. Experim ents on the IN R E TS circuit lasted tw o hour periods, during w hich E E G, EOG and vehicle speed signals w ere recorded. A fter electrode's fixing, electrophysiological m easures w ere carried out w ith open/close eyes in order to evaluate the basic level o f physiological param eters and the reference level o f each driver. There w ere 5 recorded signals : 2 EEG (Jasper sites), 2 EO G horizontal derivations and vehicle speed. F or the w hole signals, taking into account the spectral broadening o f their bands, the sam pling frequency has been fixed at 100 H z. F o r EEG signals, the upper frequency limit -504-

w as 45 Hz. T he recorded data w ere analysed w ith the help o f the digital computation facilities and by spectral analysis (A ntoniou). P rior to EO G analysis, signals were visually edited to remove all portions assigned to artefacts. To remove them from signals that diagnosis impairment at the w heel, w e have used Sem ilitsch s algorithm (Semilitsch and al., Kenemans and al.) associated w ith K h ard i s m etdhod (K hardi and al.)). Signals were analysed over 15 second periods as a function o f the frequency concerned. W e have assessed the total energy o f alpha and theta bands, and the rate o f eyelid closure. T he rate o f eyelid closure (REC) is defined as the follow ing ratio (1- - J L ) w here d0 is the degree o f opening o f the eye that is measurable a rcf by EOG signal versus the reference value drcf corresponding to a complete opening o f the eye when subjects are fully alerted during reference s recording. This reference is alw ays recorded before the running o f experim ents and lasted tw o five m inute periods. D E S C R IP T IV E S T A T IS T IC S Here subjects are nam ed in d l to ind 12, and w ith a resulting REC data every 15 seconds this yields 480 values for a full successful experim ent (two o f them have only 390 convenient values). For instance w e show respective first descriptive statistics in table 1. T a b le 1 : c r u d e d e s c rip tiv e s ta tis tic s indl ind2 ind3 ind4 ind5 in d 6 ind7 ind8 ind9 indlo indl 1 ind 12 n 390 480 390 480 480 480 480 480 480 480 4 8 0 480 mean 0.298 0.351 0.433 0.395 0.217 0.335 0.383 0.405 0.559 0.586 0.506 0.491 st. dev. 0.093 0.151 0.155 0. 1 2 1 0.079 0.136 0.135 0.105 0.328 0.305 0.288 0.307 min 0. 1 0 2 0.107 0.141 0.103 0.066 0.005 0.096 0.172 0. 0 0 2 0.001 0.003 0.001 med. 0.294 0.323 0.405 0.381 0.207 0.317 0.360 0.396 0.657 0.661 0.507 0.485 max 0.563 1.000 1.000 1.000 0.542 1.000 0.874 0.841 0.999 0.999 0.998 0.996 Tritely there are tw o m ajor clusters o f records, ind9 to 12 have the highest means and standard deviations, while am ong the rem aining subjects, ind5 and 1 show the low est means and standard deviations and constitute a m inor subset (figure 1). - 505 -

Figure 1 : a crude mapping of data. S O M E IN F E R E N T IA L A S P E C T S 1 - The first step concerns random ness o f successive R EC values, in order to apply classical statistic non param etric processings. They are different ways in order to test the null hypothesis o f randomness (and independence o f successive values), for instance runs test applied to data above or below sam ple m edian value, or runs test applied to data between successive critical points (local m ax and m in), (B row nlee, Lecoutre Tassi, M othes, Siegel). Independence cannot be accepted for data com ing from ind2, and for data related to ind6, 7 and 8, independence is not accepted in accordance to runs test related to m edian but accepted for runs test related to critical points. So we have to be careful and reserved about next statistical conclusions... In the sam e tim e w e observe som e glaring visual differences between these times series and so w e may m erely infer individual differences. Here again there are several non parametric comparison s tests betw een sam ples as K olm ogorov Sm irnov, Mann W hitney (and W ilcoxon), Wald W olfowitz ; unfortunatly (but as usually) statistical conclusions are not in perfect agreement. When applying K olm ogorov Sm irnov test, subject s differences are all statistically significant except couples (ind9, 10), and find 11, 12) ; applying Mann W hitney test the supplementary couples {ind2, 6 ), and ( ind3, 8) are not considered as different, and applying W ald W olfow itz the supplementary couples (ind2, 6 ), [ind4, 8 ), (ind4, 7 ), (ind7, 8) are not considered as differen t... So, som e vagueness again in conclusions (because som e w eakness and differences without uniform ity in respective pow er o f test) ; but m eanwhile with a som e relative good coherency with raw cluster findings. - 506-

F igure 2 : some contrasted empirical cdf. 2 - The originating purpose is to com pare som e quantile values o f respective individuals sample x i,k i = l - " n anc' k su b je t s index, and reciprocally to observe frequencies with w hich some reference xrcf values are exceeded (x for o u r R E C variable). W e recall the general asymptotic and free properties o f such statistics distributions : when the cum ulated distribution function F is continuous (and derivable) and for every probability 0 < a < 1 one m ay define the quantile xa such as F(xa) = a ; and with the x ; sample the em pirical cum ulated distribution function Fn, the order statistics x ^ (say x ^ ) < x@) <... < X(nj) and the em pirical quantile x( n0, +1). The classical results say XQna]+1) follow s (asymptotically) the normal law :A(x(I, al *~ra) ), and F (xk) follow s 5V(a, '?!), (Lecoutre Tassi, W ilks) ; nf (x )2 n one notes the advantage o f dealing with frequencies and Fn(xa) instead o f quantiles because F (xa) is not needed. This enables us to get som e confidence intervals for probabilities = F" (xref),and testing null h ypothesis H n : {ocrcrki = ocrc(-kj } for indk; and k j, here the statistic F ^ fx,.^ ) - F ^ f x ^ ) fo llo w s W (0, cxre,-( 1 - a rcr) (_ L + _ L ) } under H 0, where otrcf is estimated from the sample o f n ki ''k j indk; and kj together (because ocrcriki = otrcf kj, H 0). In every next calculations level o f significance (for confidence intervals or for hypothesis testing) is 0.95. 3 - Exem ples : 1) w e consider xrcr = 0.6, that is to say we deal with percentage o f time during w hich our REC variable is inferior to 0.6, - 507 -

as the m ax is low er than 0.6 for indl and 5 there are no data processing for them, and [ a 1; a 2] is a confidence interval. T a b le 2 : co n fid e n c e in le r v a ls for P ro b a b ility fr E C < 0.6} ind2 inc!3 im!4 indfi ind7 ind8 ind9 indlo in d ll indl2 ctjef 0.9250 0.8735 0.9520 0.9650 0.9320 0.9560 0.4320 0.4070 0.5710 0.5810 (Xj 0.9247 0.8730 0.9518 0.9649 0.9317 0.9558 0.4310 0.4060 0.5700 0.5800 0t2 0.9253 0.8741 0.9522 0.9651 0.9323 0.9562 0.4330 0.4080 0.5720 0.5820 T he range a 2 - at] is rather weak because for a 0.95 level it is given by 2*1.96* VorefO -Oref) /Vn and n = 480. N ull hypothesis { a rcf i i = «rof 12) and ( 0 ^ 9 = a ref 10) are accepted, as expected from previous results ; am ong all others null hypothesis (txref,4 = <^ref,8) and ( tew = oq.gf.7} are accepted, ( a r c r,3 = Ohof v) is refused. 2) now w e consider xrcf = 0.7, that is to say w e deal w ith percentage o f tim e during w hich our variable is inferior to 0.7. T a b le 3 : co n fid e n c e in le r v a ls for P ro b a b ility fr E C < 0.7} ind2 ind3 ind4 ind6 ind7 ind8 ind9 indlo in d ll indl2 otjef 0.9737 0.9333 0.9777 0.9789 0.9788 0.9846 0.5434 0.5310 0.6955 0.6825 «! 0.9736 0.9330 0.9776 0.9788 0.9787 0.9846 0.5423 0.5300 0.6946 0.6816 a 2 0.9738 0.9336 0.9778 0.9790 0.9789 0.9847 0.5444 0.5320 0.6964 0.6834 The situation is again very different betw een ind2 to 8 and ind9 to 12 ; fo r the first quite all the recorded values tire below 0.7 w hile for the last there is a non null probability fo r higher values. N ull hypothesis a rcf n = ttrcr,i2 ) and { a rcf,9 = ocrcf l0 } are accepted as before ; am ong all others null hypothesis { a rcf 2 = cxrcr.85 ar d ( 0 7 ^, 2 = a rcf,3) are refused. A s another non param etric sam ple tests we m ay observe R EC distribution s differences during the first and the hist hour, except again for ind9 to 12 w hich show m ore spread tim e variations. - 508 -

CONCLUSION These specific carried out signals (and tim e digitalization every 15 sec) allow us to engage som e descriptive and inferential statistical processings. O f course data are (as many times) intricate and subm itted to noise, but even so we observe differences ; for instance in relation to percentage o f tim e (probabilities a ) during w hich REC is below specific values (60%, 70% ). Differences between the first and the second hour o f driving have been observed ; subjects showed fatigue effects during the second hour o f the drive due to more pronounced impairment states that are associated to the m onotonous task. W e have also noted a kind of three driver s classes which describe three behaviours discriminating drivers. T o be reliable, statistical behaviours have to be extended to a large sample. This clustering m ethod would he benefit for classifying behavioural features for each group. It would be also useful in studies involving drivers that have absorbed drugs and alcohol. B esides the used m ethods, if em ployed for a large sam ple, w ould be compared to PERCLOS model o f Knipling and W ierw ille and tested with the existing and the coming active safety devices. This comparison should confirm the reliability o f the threshold value o f 80% o f eye closure indicating the drow sy states o f drivers. This dem arche m ay constitute a starting point in the analysis o f eye closure distributions in order to diagnosis early levels o f drowsiness, rather than the advanced states synonym ous to the falling asleep tit the wheel. R E F E R E N C E S Antoniou A., Digital filters : A nalysis and design, M C G raw -H ill B ook Company N -Y 1979. B oussuge J, Quinze ans de securitc sur autoroute. Bilan et perspectives. Revue Generate des routes et des Aerodrom es N 726, Fevrier 1995 B row nlee K.A., Statistical theory and m ethodology, J. W iley, 1965. Kenem ans J.L., M olcnaar P.C.M., Verbaten, M.N. and Slangen J.L., Removal o f the Ocular Artifact from the EEG : A com parison o f T im e and Frequency Domain M ethods with Simulated and Real D ata, Psychophysiology, 28, 114-121, 1991. Khardi S., O livier D. and Vallet M., Analyse electro-oculographique et baisses de vigilance des conducteurs autom obiles, Ann. Med. Trafic, N 45, 36-41, 1995. - 509 -

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