Microsleep Episodes and Related Crashes During Overnight Driving Simulations

Microsleep (MS) episodes and related crashes were studied in an overnight driving simulation study. A new definition of MS proposed recently was applied and the mean number as well as the mean length of MS was calculated. MS occurred much more frequently than crashes. Within all pre-crash intervals (length 1 minute) the percentage of MS was calculated. Results showed that there are numerous MS episodes before every crash. The mean length of MS was between 5 and 9 seconds and did not change significantly during the night. The mean MS percentage was high within pre-crash intervals (60-80%) and is a predictor for crashes

Steering Wheel Behavior Based Estimation of Fatigue

This paper examined a steering behavior based fatigue monitoring system. The advantages of using steering behavior for detecting fatigue are that these systems measure continuously, cheaply, non-intrusively, and robustly even under extremely demanding environmental conditions. The expected fatigue induced changes in steering behavior are a pattern of slow drifting and fast corrective counter steering. Using advanced signal processing procedures for feature extraction, we computed 3 feature set in the time, frequency and state space domain (a total number of 1251 features) to capture fatigue impaired steering patterns. Each feature set was separately fed into 5 machine learning methods (e.g. Support Vector Machine, K-Nearest Neighbor). The outputs of each single classifier were combined to an ensemble classification value. Finally we combined the ensemble values of 3 feature subsets to a of meta-ensemble classification value. To validate the steering behavior analysis, driving samples are taken from a driving simulator during a sleep deprivation study (N=12). We yielded a recognition rate of 86.1% in classifying slight from strong fatigue

Estimating Fatigue from Predetermined Speech Samples Transmitted by Operator Communication Systems

We present an estimation of fatigue level within individual operators using voice analysis. One advantage of voice analysis is its utilization of already existing operator communications hardware (2-way radio). From the driver viewpoint it’s an unobtrusive, non-interfering, secondary task. The expected fatigue induced speech changes refer to the voice categories of intensity, rhythm, pause patterns, intonation, speech rate, articulation, and speech quality. Due to inter-individual differences in speech pattern we recorded speaker dependent baselines under alert conditions. Furthermore, sophisticated classification tools (e.g. Support Vector Machine, Multi-Layer Perceptron) were applied to distinguish these different fatigue clusters. To validate the voice analysis predetermined speech samples gained from a driving simulator based sleep deprivation study (N=12; 01.00-08.00 a.m.) are used. Using standard acoustic feature computation procedures we selected 1748 features and fed them into 8 machine learning methods. After each combining the output of each single classifier we yielded a recognition rate of 83.8% in classifying slight from strong fatigue

A Measure of Strong Driver Fatigue

Strong fatigue during sustained operations is difficult to quantify because of its complex nature and large inter-individual differences. The most evident and unambiguous sign is the occurrence of microsleep (MS) events. We aimed at detecting MS utilizing computational intelligence methods. Our analysis was based on biosignal and video recordings of 10 healthy young adults who completed 14 sessions over two nights in our real-car driving simulation lab. Visual scoring by trained raters led to 2,290 examples of MS. Only evident events accompanied by prolonged eyelid closures, roving eye movements, head noddings, major driving incidents, and drift-out-of-lane accidents were regarded as MS. All other cases with signs of fatigue were regarded as dubious. The same amount of counterexamples (Non-MS) where continued driving was still possible were picked out from the recordings. Non-MS and MS examples covered only 15% of the whole time. Support-Vector Machines were utilized as classifiers and were adapted to these two classes of examples. If such classifiers were applied consecutively, then 100% of time is covered. Validation analysis demonstrated that the classifier gained high selectivity and high specificity. Based on this complete coverage, the percentage of MS in a predefined time span can be calculated. This measure was highly correlated to deteriorations in driving performance and to subjective self-ratings of sleepiness. We conclude that reliable detection of MS is possible despite large intra- and inter-individual differences in behaviour and in biosignal characteristics. Therefore, the percentage of detected MS gives an objective measure of strong driver fatigue

Sustainable Reduction of Sleepiness through Salutogenic Self-Care Procedure in Lunch Breaks: A Pilot Study

The aim of the study was to elucidate the immediate, intermediate, and anticipatory sleepiness reducing effects of a salutogenic self-care procedure called progressive muscle relaxation (PMR), during lunch breaks. The second exploratory aim deals with determining the onset and long-term time course of sleepiness changes. In order to evaluate the intraday range and interday change of the proposed relaxation effects, 14 call center agents were assigned to either a daily 20-minute self-administered PMR or a small talk (ST) group during a period of seven months. Participants' levels of sleepiness were analyzed in a controlled trial using anticipatory, postlunchtime, and afternoon changes of sleepiness as indicated by continuously determined objective reaction time measures (16,464 measurements) and self-reports administered five times per day, once per month (490 measurements). Results indicate that, in comparison to ST, the PMR break (a) induces immediate, intermediate, and anticipatory reductions in sleepiness; (b) these significant effects remarkably show up after one month, and sleepiness continues to decrease for at least another five months. Although further research is required referring to the specific responsible mediating variables, our results suggest that relaxation based lunch breaks are both accepted by employees and provide a sustainable impact on sleepiness