Sleepiness Pattern of Indonesian Professional Driver Based on Subjective Scale and Eye Closure Activity

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1 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: Sleepiness Pattern of Indonesian Professional Driver Based on Subjective Scale and Eye Closure Activity Manik Mahachandra, Yassierli, Iftikar Z. Sutalaksana, Kadarsah Suryadi Faculty of Industrial Technology Institut Teknologi Bandung, Indonesia Abstract Studies have concluded that sleepiness while driving is considered as one main cause of traffic accidents. This naturalistic study aimed to investigate sleepiness characteristics (pattern and changes) based on subjective sleepiness scale and eye closure activity (eye-blink frequency and microsleep frequency per minute). Sixteen professional drivers agreed to participate in this study. They drove a round trip of Bandung-Jakarta-Bandung for a shuttle service company for about seven hours with mostly highway. A set of surveillance system were installed at the dashboard to record video data continuously during driving for 4-6 consecutive days. Subset of video for 30 minutes of sleepiness incident were collected, together with the value of Karolinska Sleepiness Scale (KSS) as subjective sleepiness scale in every 5- minutes interval within the subset. Frequency of eye-blink and microsleep (as an overlong eyelid closure in certain duration of time) of each video subset were then computed and analyzed. Fifty-four video subsets considered as high sleepiness incidents were obtained. Results showed that sleepiness pattern were dominated by bilinear model (48%). The sleepiness changes became faster when drivers experience transition from alert to sleepy state (KSS level 5). This physiological state could be applied as the point of time when sleepiness detection needed. Sleepiness detection method showed high sensitivity in the 20% increment of eye-blink frequency per minute and the 0.5 sec duration of microsleep. These parameters, together with the other results were valuable in the development of sleepiness detection system based on eye closure activity in the future study. Index Terms driver, eye-blink, microsleep, naturalistic study, sleepiness Manuscript received November 10, This study was fully supported by Grant for Applied Research from the Ministry of Research and Technology of Indonesia in M. Mahachandra, Yassierli, I. Z. Sutalaksana, and K. Suryadi are with the Industrial Management Research Group, Faculty of Industrial Engineering, Institut Teknologi Bandung, West Java Indonesia. Corresponding phone: , fax: , I. INTRODUCTION Sleepiness while driving is considered as one main cause of traffic accidents, specifically on highways. Fatalities caused by sleepiness on wheel were also inferred by many studies. Investigation reported by [1] estimated that sleep related accidents accounted for about 15 20% of traffic accidents on urban roads and motorways in the UK. While data from the USA showed that sleepiness is responsible for 1 3% of highway crashes and about 96% of crashes involving passenger vehicles, as stated by [2]. In Indonesia, [3] report demonstrated that 41 cases out of 194 traffic accident cases on the highways were related to sleepiness while driving during Sleepiness during driving finally resulted in substantial losses, including car damage and number of died or injured individual. Moreover, some researchers showed that sleepiness caused in traffic accident resulted in higher morbidity and mortality than accidents caused by any other factors. Drivers sleepiness has been documented along with other dangerous behaviors leading to traffic accident while driving such as drinking, speeding, using a cell phone while driving, using illegal drugs while driving, and novice drivers, as concluded by [4] and [5]. However, unlike the other factors, sleepiness tends difficult to be avoided due to its natural characteristic in human daily life. As concluded in [6] study, human are considered to have lack of ability in predicting their sleepiness condition based on prior physiological and cognitive indicators. Therefore, there should be an extensive effort to minimize sleepiness related accidents. This condition will also be suitable for Indonesia, as a developed country with a significant number of accident cases. Of course, the effort can only be started by understanding the Indonesian driver sleepiness characteristics. Sleepiness is a common biological conditions experienced by every human being. Sleepiness occurs when a person feels there is a tendency to sleep or have a probability of falling

2 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: asleep at a time. Sleepiness is the transition from awake to sleepy phase and phase of sleep stage 1, regardless of whether this condition continues into next phase, as stated by [7]. Sleepiness is a natural condition that occurs in each individual, showing a physical status between awake and fell asleep. Human generally experience sleepiness before enter the sleep phases. Furthermore, [8] concluded that usually sleep does not happen suddenly, but through step changes. So that, sleepiness as the changes condition of human state has several attribute to be observed, such as its pattern and changes velocity. Sleepiness level can be simply quantified through a subjective sleepiness scale. Karolinska Sleepiness Scale (KSS) is among the scales that has widely been used, as concluded by [9]. This subjective indicator has been proved to be highly correlated with another sleepiness indicator, includes the objective one, such as and electroencephalograph (EEG), psychomotor vigilance task (PVT), Visual Analogue Scale (VAS), Alpha Attenuation Test (AAT), and Karolinska Drowsiness Test (KDT), as concluded on various studies, such as [9], [10], [11], and [12]. In addition, drivers sleepiness can also be observed through objective indicators. One reliable indicator is assessed as eyelid closure pattern. As explained by [13], eye-blink and overlong eye closure (microsleep) are among the well-proved parameters that accurate in assessing driver sleepiness condition. To observe sleepiness characteristic, eye-blink frequency per minute and microsleep frequency per minute can be applied as the parameter. The main purpose of this study was to investigate sleepiness characteristic of Indonesian drivers. The characteristics were examined as subjective sleepiness pattern, sleepiness changes, as well as eye-blink frequency and microsleep frequency pattern over sleepiness level increment. In order to represent the original situation of driving, this study was conducted in a naturalistic method, where intervention to the participants was minimized. The sleepiness characteristics as study results will be valuable to establish the sleepiness detection system for Indonesian drivers in the further studies. A. Participants II. METHOD Research collaboration with a shuttle service company in Bandung, Indonesia was established. Regular drivers for Bandung-Jakarta s airport route were invited as participant. This route was selected for presenting Indonesian highway situation and because the travelling period was longer than other available routes. Sixteen male professional drivers with more than 1.5 years experience were joined this study. However, only twelve of them (age ± 3.11 years) were able to complete the whole study. B. Instruments A set of surveillance system was installed at each car dashboard, as shown by Fig. 1. The system consisted of one camera in front of the driver, one camera facing outside to the road, and a digital video recorder under the passenger seat. The recorded video was saved in a hard disk of its Digital Video Recording (DVR) box with a recording speed of five frames per second. Fig. 1. Naturalistic study using a set of surveillance system. Karolinska Sleepiness Scale (KSS) was applied to quantify the subjective sleepiness level. The 9-point of KSS, originally published by [14], was used: 1 = very alert, 3 = alert, 5 = neither alert nor sleepy, 7 = sleepy (but not fighting sleep), 9 = very sleepy (fighting sleep). However, to adjust with Indonesian drivers language ability, the original KSS was translated into Bahasa Indonesia (see Appendix A). The translation was done by experts, and was verified via a back translation procedure. Prior to this study, training on KSS was given privately to each participant. C. Procedure Participants were monitored for 4-6 consecutive days. Most of them had one round-trip per day with about seven-hours driving for each round-trip. They were asked to drive as usual. Video data were recorded continuously during the trip of Bandung-Jakarta -Bandung. Sleepiness Incident Once the participant had returned to the car pool in Bandung after a round trip driving, an interview was taken. Participant was asked to determine any driving location(s) with relatively high sleepiness level (level 5 to 9 of KSS). The associated, recorded video was shown for confirmation. Subsequently, subsets of the video for 30 minutes prior to the time with high sleepiness level were discussed. The subset was divided into events in every 5 minute interval (named 0, 5 th, 10 th, 15 th, 20 th, 25 th and 30 th minute). The video events

3 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: were shown randomly to participants, and then the participant was requested to quantify their KSS level. It was also possible that the similar video sub-subset was repeatedly shown. In this case, the final KSS value was taken as average. Data from the first day of experiment were omitted to minimize any bias since the camera might affect the driver behaviors. Each video subset was presented as a sleepiness incident. Sleepiness Pattern Pattern of sleepiness level of each sleepiness incident was plotted based on subjective sleepiness scale changes over time. Three trend line models (linear, exponential, and bilinear) were fitted to each sleepiness incident data. Coefficient of determination (R 2 ) was computed to determine the best trend line model. Sleepiness Changes The sleepiness changes were then computed as the velocity of sleepiness level increment per observation time. The computation was carried out for two classes of sleepiness level, e.g. KSS level of 1 to 5 and 5 to 9. Changes in the subjective sleepiness level per minute were calculated for every video subset, based on linear assumption. Differential analyses were then conducted for comparing velocity data between the two classes. Eye-blink Frequency Eye-blink identification was carried out by investigating eyelid closure status of each video frame. Every one second video was represented by approximately five frame events. The following rules apply to determine the condition of participant s eye closure status: a. Participants eye is stated close if the eyelid closure is more than 80%, and b. If an image was not clear, the participants eye is judged to be open. Eye-blink frequency per minute was calculated for each 30- min video subset. To minimize subjective bias from participant, only three-minute data of five-minute time interval were considered in computing eye-blink frequency. The threeminute data were started from one minute prior to one minute after the time participant stated his subjective sleepiness level. This time interval represented the participant state at certain subjective sleepiness level. Three minutes interval was chosen rather than five minutes in order to minimize any bias on sleepiness level which might be occurred since participant sleepiness level may vary across the time interval. To evaluate the eye-blink frequency pattern within subjective sleepiness level, alteration trend of eye-blink frequency was analyzed visually. Later on, correlation test was conducted within certain range of sleepiness level which showed increasing trend of eye-blink frequency, e.g. from KSS level 1 to KSS level 5. The test result was then compared to correlation test within all subjective sleepiness level (from KSS level 1 to KSS level 9). The more significant the correlation test result, the more valid the subjective sleepiness range to be a standard for sleepiness detection based on eyeblink frequency increment. Microsleep Frequency Microsleep identification was also carried out by eyelid closure status investigation. Microsleep, as an overlong eyelid closure, were analyzed in four groups of eyelid closure duration, i.e. 0.5 s; 1.0 s; 1.5 s; and 2.0 s. These four types of duration were chosen among several studies that conclude various duration of microsleep, such as [13] and [14]. Frequency of microsleep event was computed for each video subset. Mean frequency of microsleep for each group were calculated for every three-minute data, based on similar argumentation in the previous section. Average number of microsleep frequency was then calculated for each subjective sleepiness level (KSS 1-9) and were grouped per participant. Similar with the previous analysis, each duration of microsleep frequency was also inspected visually for its alteration pattern. Correlation tests were also applied and compared to find any subjective sleepiness range standard for sleepiness detection. Sleepiness Detection Sleepiness detection was analyzed using eye-blink frequency and microsleep frequency as the proposed parameters. Two methods of parameter s calculation were applied into eye-blink frequency data, which were eye-blink mean frequency value at certain subjective sleepiness level and percentage of eye-blink frequency increment. Whilst detection analysis using microsleep frequency data were computed as microsleep mean frequency value at certain level of subjective sleepiness. To analyze the ability of each parameter effectiveness on sleepiness detection, signal detection concept as stated in [15] and [16] was applied. The analysis was carried out by computing the number of participant that show each of hit (true positive), miss (false negative), false alarm (false positive), and correct rejection (true negative) condition for each parameter. Hit rate and false alarm rate were then calculated to determine the sensitivity (d ) to represent the effectiveness of each condition in detecting sleepiness. A hit is the condition when the parameter success in detecting sleepy state while participant feeling sleepy, computed as the proportion of true positive occurrence. False alarm is a condition when the parameter falsely detecting sleepy state while participant not feeling sleepy, computed as the proportion of false positive (type II error) occurrence. Sensitivity, so called d, is one important parameter in

4 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: detection theory which reflects the strength of the sleepiness condition in what level it can be distinguished with alert condition. human physiological state. Therefore, sleepiness characteristics, such as its pattern and changes, were then analyzed and standardized, based on the sleepiness incidents data collection. III. RESULTS AND DISCUSSIONS Naturalistic observation in this study, which was carried out for several months, can reveal that drivers were indeed experiencing sleepiness during driving on the highway routes that were observed. This fact strengthens researcher notion that technology intervention in detecting driver sleepiness is absolutely required. This current study is considering the pilot study of sleepiness detection system, especially for Indonesian drivers. In this study, the first day of observation, where participants still learnt to be watched by the surveillance instruments, sleepiness occurrence is rarely appear. Almost all participants perform well in keeping their alertness, which were totally different from observation on the following days. This considered to be caused by the learning period, where participant usually performs not normally condition. Therefore, the first day observations were excluded in the further analysis to minimize bias. B. Sleepiness Pattern Three models were fitted into each sleepiness incident data to investigate which model best describes the sleepiness pattern. Most of the sleepiness incident performs a bilinear pattern (48% of all sleepiness incident data), where sleepiness level where gradually increase in two different linear trend. Fig. 3 shows an example of bilinear sleepiness pattern. Most of the bilinear data shows an increment in the slope (77% of all bilinear pattern of sleepiness incident). The average midtime when the first linear trend switches to another one was after 15 minute, or in the middle of the sleepiness incident, and the median KSS was on the level 4 at this condition. A. Sleepiness Incident Fifty-four video data of sleepiness incident were obtained in this study. Each video data presents a 30-minute sleepiness incident, from awake to sleepy state, represented by the subjective sleepiness value using KSS. Fig. 2 shows an example of sleepiness incident. Fig. 3. Example of bilinear pattern of sleepiness. The second dominant pattern found in this study was the linear pattern, where sleepiness linearly increases over the time period (39% of all sleepiness incident data). Fig. 4 shows an example of this pattern. Fig. 2. Example of sleepiness incident. Each sleepiness incident data collected indicates that sleepiness can happen at any time of driving, for various time period. For each sleepiness incident, there is a unique pattern where subjective sleepiness tends to increase over the observation time. To explore the detection system in the future, sleepiness has to be observed as a standard condition of Fig. 4. Example of linear pattern of sleepiness. Furthermore, the remaining data formed an exponential pattern (13% of all sleepiness incident data). Fig. 5 shows an

5 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: example of this sleepiness pattern. to 4 is considered to be faster than KSS 4 to 9. On average, the changes are 0.20 scales per minute and 0.15 scales per minute, respectively for KSS 1 to 4 and KSS 4 to 9. However, the Mann-Whitney U test shows that the two changes are not significantly differ (U = , sig. level = 0.19). Fig. 5. Example of exponential pattern of sleepiness. The average value of coefficient of determination (R 2 ) were for bilinear pattern, 0.56 for linear pattern, and 0.69 for exponential pattern. The numbers show that subjective sleepiness data could fit the three proposed models quite well. The coefficient of determination of bilinear sleepiness pattern was calculated as the average between the two linear lines. About 85% of all sleepiness pattern (linear, bilinear, and exponential) were fit on R , while the number become less if R were used (only 65% of the data), but still indicate a significant condition. The best fitted model is bilinear, since most of the sleepiness incidents in this study shows highest coefficient determination value in bilinear model. Most of this bilinear sleepiness pattern performs an increment slope magnitude of the gradient in the second linear pattern is higher than the first pattern. However, drivers sleepiness level can be slightly decrease in the middle of incident, because of the countermeasure act that performed in the middle of driving activity or because of the nature of driving activity itself. A sleepiness detection system, which will be studied in the future, have to be operated based on its sleepiness changes pattern. The system has to detect drivers sleepiness before the sleepiness level increases significantly. The detection must perform in certain point of the sleepiness increment. Because of the bilinear pattern, hence detection must work before the sleepiness slope changes into a steeper one. To observe the possibility of this characteristic, sleepiness changes was then explored in this study. Fig. 6. Two classes of sleepiness changes with KSS 4 as the mid-point. Insignificant test result of two classes of sleepiness changes using KSS level 4 as the mid-point made authors curious to test another mid-point of KSS as the sleepiness changes point. The objective was to find unique sleepiness changes pattern to use in further sleepiness study. Fig. 7 shows the other sleepiness changes using KSS 5 as the mid-point, considering that KSS level 5 is also the middle point of the KSS subjective sleepiness scale itself. From its description, the KSS level 5 is similar with changes condition from alert to sleepy, stated as "neither alert nor sleep in the original scale by [17]. The graph shows that sleepiness changes faster from KSS level 5 to 9 (0.23 scales per minute), rather than from KSS level 1 to 5 (0.17 scales per minute). However, similarly with previous analysis, the Mann-Whitney U test also shows that the two changes are not significantly differ (U = , sig. level = 0.13). C. Sleepiness Changes Sleepiness changes was analyzed by plotted the subjective sleepiness scale changes over the time period. Bilinear model was used as the most fitted model, concluded in previous analysis. The first sleepiness changes analysis result is shown in Fig. 6. The KSS level 4 was used as the mid-point of the changes, generated from the previous bilinear subjective sleepiness pattern analysis. The alteration of sleepiness level from KSS 1 Fig. 7. Two classes of sleepiness changes with KSS 5 as the mid-point. Graphs resulted show that drivers sleepiness increases from time to time. The increment becomes faster when subjective

6 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: sleepiness reach a certain point of condition. This condition stated as neither alert nor sleep in KSS subjective sleepiness scale. This KSS level 5 is the changes point of human physiological state from alert to sleepy. This conclusion similar with the previous preliminary study conducted by authors, reported in [18]. Another study conducted by [2] concluded that level 7 of KSS is correlated with a decrease in driver performance and attention level. Therefore, sleepiness have to be detected and even predicted several minutes before drivers experiencing this state of subjective sleepiness. In conclusion, subjective sleepiness level 5 can be valuable for detecting sleepiness. Drivers sleepiness have to be detected in this subjective sleepiness state to prevent more severe condition of sleepiness that possible to happen in several minutes later. D. Eye-blink Frequency Eye-blink frequencies per minute of each participant were observed for each level of KSS subjective sleepiness. Fig. 8 shows the average number of eye-blink frequency of all participants. Visually, there is a unique behavior of eye-blink frequency per minute which gradually increases from subjective sleepiness KSS level 1 until KSS 5 (mid-point of KSS) then changes with high volatility when subjective sleepiness higher than KSS level 5. This curiosity seems in line with findings in previous analysis, where KSS level 5 was concluded as the sleepiness detection point. Previous studies, for example one that conducted by [19], also conclude similar result that eyeblink frequency becomes higher when someone getting sleepy. nor sleep (KSS level 5). After experiencing KSS level 5, eyeblink frequency becomes unpredicted, although the subjective sleepiness scale increase gradually. Therefore, another result can be conclude regarding the performance of subjective sleepiness level 5. When sleepiness is detected in this physiological state, eye-blink frequency can be one of the valuable biosignal parameter. Sleepiness can be detected from the magnitude of eye-blink frequency per minute, or the percentage of increment of eye-blink frequency per minute. E. Microsleep Frequency Another parameter of eye-closure indicator in this study is microsleep frequency. Four histograms below show the microsleep frequency data of all participants. Fig. 9, Fig. 10, Fig. 11, and Fig. 12 are respectively for microsleep frequency of more than 0.5s, 1.0s, 1.5s, and 2.0s of eye closure duration. Each zero bar in the graph means that participants had experienced the sleepiness level, but during driving none of them showed any particular microsleep frequency. Fig. 9. Mean value of microsleep frequency 0.5s of each sleepiness level. (Note: The line above each bar indicates the standard deviation value.) Fig. 8. Mean value of eye-blink frequency of each sleepiness level. (Note: The line above each bar indicates the standard deviation value.) Further correlation analysis using Spearman s rho shows that eye-blink frequency more significantly correlates with subjective sleepiness level on KSS level 1 to 5 (r = 0.800, sig. level = 0.10) rather than on KSS level 1 to 9 (r = 0.03, sig. level = 0.93). It means that drivers eye-blink frequency is linearly increase from alert (KSS level 1) to neither alert Fig. 10. Mean value of microsleep frequency 1.0s of each sleepiness level. (Note: The line above each bar indicates the standard deviation value.)

7 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: Fig. 11. Mean value of microsleep frequency 1.5s of each sleepiness level. (Note: The line above each bar indicates the standard deviation value.) Coefficient correlation in the test results indicate that microsleep frequency increases when drivers physiological state changes from alert (KSS level 1) to neither alert nor sleep (KSS level 5). The increment is significant correlated with the subjective sleepiness state changes. After experiencing the subjective sleepiness level 5, microsleep frequency of all duration tend to be unpredicted, due to their volatility. Considering sleepiness level 5 as the point of sleepiness detection, another conclusion can be made. Microsleep frequency can be considered as a valuable parameter in sleepiness detection. The presence of microsleep frequency per minute in four types of duration of eye-closure become another proposed parameter of sleepiness detection in this study. Fig. 12. Mean value of microsleep frequency 2.0s of each sleepiness level. (Note: The line above each bar indicates the standard deviation value.) Similar with the eye-blink frequency analysis, each of the graphs above shows that there is a trend of microsleep frequency increment from subjective sleepiness level 1 to level 5. Microsleep frequency was gradually increase from subjective sleepiness level 1 to level 5, and then fluctuate afterward. Spearman s rho correlation tests were applied to test this condition. Results in TABLE I show that correlation of microsleep frequency and subjective sleepiness is high within low subjective sleepiness level only (KSS level 1 to 5). TABLE I CORRELATION TEST OF MICROSLEEP FREQUENCY INCREMENT Microsleep KSS 1 to 5 KSS 1 to 9 MS 0.5s r = 1.00 sig. level = 0.00 MS 1.0s r = 1.00 sig. level = 0.00 MS 1.5s r = 0.50 sig. level = 0.67 r = 0.00 sig. level = 1.00 r = sig. level = 0.33 r = sig. level = 0.13 F. Sleepiness Detection Early conclusions have been made from the previous eyeblink and microsleep frequency analyses. Eye-closure shows unique activity at subjective sleepiness level 5. Both of eyeblink frequency and microsleep frequency are gradually increase during the subjective sleepiness increment from KSS level 1 to KSS level 5, and show highly significant correlation of this pattern. After the KSS level 5, both indicators become unpredictable, as the alteration is very fluctuating. Therefore, subjective sleepiness level 5 can be applied as standard condition for detecting sleepiness. Any proposed sleepiness detection parameter have to be performed well when driver experience this physiological state. Eye-blink Frequency Mean value of eye-blink frequency was used as the first proposed sleepiness detection parameter. Four types of detection possibility (hit, miss, false alarm, and correct rejection) were calculated using subjective sleepiness level 5 and eye-blink frequency mean value at this level as the cut-off point. The mean value of eye-blink frequency was per minute. Sensitivity calculation is presented as the lowermost bar chart on Fig. 13. Furthermore, the second parameter is percentage of eyeblink frequency increment. An example of increment status computation of eye-blink frequency is shown by Table II, performed by one of the participant. This participant only experienced subjective sleepiness level 1 to 7 during the observation. The increment is calculated by alteration made of his eye-blink frequency in certain sleepiness subjective level from the average number of eye-blink frequency on the previous sleepiness subjective level(s). MS 2.0s r = 0.50 sig. level = 0.67 r = sig. level = 0.30

8 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: TABLE II EXAMPLE OF INCREMENT STATUS COMPUTATION OF FOUR GROUPS OF INCREMENT OF EYE-BLINK FREQUENCY KSS Freq per min Increment Increment status 5% 10% 15% 20% % % % % % % (Note: Data were taken from one participant. Symbol + indicates that the parameter able to show sleepiness since the increment greater than the group limit, while means the opposite. Blank data in KSS level 8 and 9 indicates that participant never experience these subjective sleepiness states during observation.) After similar computation was carried out for all participant of every group of eye-blink frequency increment percentage, sensitivity was calculated from the four types of detection possibility. Finally, sensitivity analysis of eye-blink frequency in detecting sleepiness is concluded in Fig. 13. sleepiness detection. Whereas the presence of microsleep was applied as the cut-off point. Sensitivity calculation was then compared in Fig. 14. Fig. 14. Sensitivity comparison of microsleep parameter in sleepiness detection. The most sensitive parameter of microsleep activity is the microsleep with 0.5 sec of eye-closure duration. In this parameter, sleepiness is detected by monitoring microsleep activity while driving. Any eye-closure within 0.5 sec or more means that driver is experiencing microsleep at that time. Therefore, sleepiness detection apparatus have to perform to warn the driver. Fig. 13. Sensitivity comparison of eye-blink frequency parameter in sleepiness detection. The highest sensitivity value in sleepiness detection performed by the 20% increment of eye-blink frequency per minute. In this parameter, sleepiness detection can be conducted by monitoring the changes of eye-blink frequency per minute. Any increment of eye-blink frequency more than 20% means that driver is starting the physiological condition of neither alert nor sleep (KSS level 5). Consequently, a sleepiness detection apparatus have to give warning at this point of time. Microsleep Next proposed parameter were four types of duration of the microsleep. Similar with previous analysis, number of hit, miss, false alarm, and correct rejection were computed using subjective sleepiness scale level 5 as the standard condition of G. Scope and Limitation Eye-blink frequency and microsleep have been applied in various sleepiness researches, such as studies from [19], [21], or [22]. However, eye-blink frequency and in these studies was commonly used to examine sleepiness level, compared to other parameters. None of the driver sleepiness researches considers this biosignal as a detection parameter. Therefore, this current study brings a novelty in the sleepiness detection process. On the other hand, some limitations were discovered during the study. Sleepiness incidents were only collected in the driving observation period. Any sleepiness or other sleepinessrelated-activity that might happen before observation was not controlled. However, to minimize any bias to the sleepiness incident data, random condition was assumed. In addition, the actual driving activity where proposed sleepiness detection system can be applied will be also in a random condition. Therefore, the experiment conducted in this study suits the actual driving activity well. Naturalistic observations in one type of road only (highway) becomes the other limitation of this study. Although observation with surveillance system also run through driving activity in non-toll road, obstacle from motorcycle, bicycle, or pedestrian was minimum. However, highway with low traffic density is considered very monotonous, which easily leads

9 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: drivers to sleepiness condition, as concluded by [11]. This study, therefore, has been performed in worst traffic condition, regarding the possibility of sleepiness occurrence. IV. CONCLUSION This research has investigated Indonesian sleepiness pattern through its pattern and changes, as well as the sensitivity of eye-blink frequency and microsleep frequency in sleepiness detection. Driver sleepiness, in this case is quantified by subjective sleepiness scale, increases bi-linearly. The sleepiness changes becomes faster when drivers reach certain level of sleepiness, i.e. transition from alert to sleepy state which can be expressed as KSS level 5. This physiological state can be applied as the point of time when sleepiness detection is needed. Preliminary sleepiness detection method using eye-blink activity shows high sensitivity in the parameter of 20% increment of eye-blink frequency per minute. Whereas microsleep activity analysis results in high sensitivity while using microsleep with 0.5 sec duration as the parameter. Further study can be focused in sleepiness detection apparatus development using these two types of eye-closure activity parameters. Surveillance system as used in this current study can be applied for monitoring the eye-closure activity. Moreover, research have to begin with comparing any combination of these parameters in detecting sleepiness. Validation in actual driving condition then have to be conducted for the proposed sleepiness detection system. APPENDIX The Indonesian version of Karolinska Sleepiness Scale which was applied in this study: 1 Sangat awas dan terjaga 2 3 Awas dan terjaga 4 5 Tidak awas dan terjaga, tapi juga tidak mengantuk 6 7 Mengantuk, tapi tidak susah untuk tetap terjaga 8 9 Sangat mengantuk, harus melawan kantuk, susah untuk tetap terjaga ACKNOWLEDGMENT Authors thank to X-Trans Travel for the cooperation in data collection. Also thank to students who were members of the related research grant for helping the data collection and processing. REFERENCES [1] Horne, J.A. and Reyner, L.A. (1995) Sleep related vehicle accidents, British Medical Journal, 310, [2] Di Milia, L. (2006) Shift work, sleepiness and long distance driving, Transportation Research Part F, 9, [3] Jasa Marga (2008) Mengantuk dan Kurang Antisipasi Penyebab Kecelakaan Tol, n/12/tgl/03/time/150510/idnews/861157/idkanal/10. Access date: 26 August [4] Evans, L. (1991) Traffic Safety, Van Nostrand Reinhold, New York. [5] Vanlaar, W., Simpson, H., and Robertson, R. (2008) A perceptual map for understanding concern about unsafe driving behaviours, Accident Analysis and Prevention, 40(5), [6] Kaplan, K. A., Itoi, A., and Dement, W. C. (2007) Awareness of sleepiness and ability to predict sleep onset: Can drivers avoid falling asleep at the wheel? Sleep Medicine, 9, [7] Johns, M. W. (2000) A sleep physiologist's view of drowsy driver, Transportation Research Part F, 3, [8] Rajaratnam, S. M. W. and Jones, C. B. (2004) Lessons about sleepiness and driving from the Selby Rail disaster case: R v Gary Neil Hart, Chronobiology International, 21(6), [9] Kaida, K., Takahashi, M., Åkerstedt, T., Nakata, A., Otsuka, Y., Haratani, T., and Fukasawa, K. (2006) Validation of the Karolinska Sleepiness Scale against performance and EEG variables, Clinical Neurophysiology, 117, [10] Kaida, K., Åkerstedt, T., Kecklund, G., Nilsson, J. P., and Axelsson, J. (2007) Use of subjective and physiological indicators of sleepiness to predict performance during a vigilance task, Industrial Health, 45, [11] Otmani, S., Pebayle, T., Roge, J., and Muzet, A. (2005) Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers, Physiology & Behavior, 84, [12] Åkerstedt, T., Connor, J., Gray, A., and Kecklund, G. (2008) Predicting road crashes from a mathematical model of alertness regulation The Sleep/Wake Predictor, Accident Analysis and Prevention, 40, [13] Schleicher, R., Galley, N., Briest, S., and Galley, L. (2008). Eye-blinks and saccades as indicators of fatigue in sleepiness warnings: Looking tired? Ergonomics, 51(7), [14] Galley, N. and Schleicher, R. (2004) Subjective and optomotoric indicators of driver drowsiness, Proceeding of the 3rd International Conference on Traffic and Transport Psychology, Nottingham. [15] Abdi, H. (2007) Signal Detection Theory (SDT), Encyclopedia of Measurement and Statistics, Sage, Thousand Oaks. [16] Stanislaw, H. and Todorov, N. (1999) Calculation of signal detection theory measures, Behavior Research Methods, Instruments, & Computers, 31(1),

10 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: [17] Åkerstedt, T. and Gillberg, M. (1990) Subjective and objective sleepiness in the active individual, International Journal of Neuroscience, 52, [18] Mahachandra, M., Yassierli, Sutalaksana, I. Z., and Ridwan, A. S. (2009) Changes in Drowsiness Level while Driving on Highway: Results of a Naturalistic Study in Indonesia, The 10th Asia Pacific Industrial Engineering & Management Systems Conference (APIEMS), Desember 2009, Kitakyushu, Fukuoka. [19] Wu, J. D. and Chen, T. R. (2008) Development of a drowsiness warning system based on the fuzzy logic images analysis, Expert Systems with Applications, 34, [20] Summala, H., Hakkanen, H., Mikkola, T., and Sinkkonen, J. (1999) Task effects on fatigue symptoms in overnight driving, Ergonomics, 42, [21] Papadelis, C. (2007) Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents, Clinical Neurophysiology, 118, [22] Wijesuriya, N., Tran, Y., and Craig, A. (2007) The psychophysiological determinants of fatigue, International Journal of Psychophysiology, 63,

N.Galley, R.Schleicher and L.Galley Blink parameter for sleepiness detection 1

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