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

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

Asymmetric Properties of Heart Rate Variability to Assess Operator Fatigue

The aim of this study is to evaluate the suitability of heart rate recordings for establishing a reliable connection to well-defined fatigue and performance measures in order to estimate fatigue in industrial and transportation applications. An overnight driving simulation scenario with partial sleep deprivation was utilized to induce strong fatigue. An experiment trial was divided into repeated sessions, each of which consisted of a driving performance and two vigilance tasks. Heart rate (HR) was recorded over the entire experiment; HRmeasures were derived and correlated against measures that were established from driving and vigilance task performance and that represent various aspects of operator fatigue. In a previous report (Hefner et al. 2009) we presented on the basis of the data of one volunteer that multiple fatigue measures correlate well with different expressions of heart rate variability (HRV), especially with longterm HRV derived from Poincaré plots. In this work, we intensify the Poincaré analysis by dividing the distribution of HR data in different accelerating and decelerating segments and by establishing properties of asymmetry between these segments. We also show that most of the properties of long-term HRV correlate well with specific fatigue measures for a group of 5 volunteers despite their large inter-individual differences in HR-to-fatigue correlations

PERCLOS: An Alertness Measure of the Past

The growing number of fatigue related accidents in recent years has become a serious concern. Accidents caused by fatigue, or more precisely impaired alertness, in transportation and in mining operations involving heavy equipment can lead to substantial damage and loss of life. Preventing such fatigue related accidents is universally desirable, but requires techniques for continuously estimating and predicting the operator’s alertness state. PERCLOS (percentage of eye closure) was introduced as an alertness measure. Some years later, it was claimed to be superior in fatigue detection to any other measure, including the general Eye-Tracking Signal (ETS) and even EEG recordings. This study will show that this is not the case. To put things into the prospective a fair and objective comparison between PERCLOS, the general ETS and EEG/EOG has to be established. To achieve this purpose, a protocol was established to investigate the fatigue detection capabilities of PERCLOS, ETS, and EEG/EOG in a simple two class discrimination analysis using an ensemble of Learning Vector Quantization (LVQ) networks as a classification tool. Karolinska Sleepiness Scale (KSS) and Variation of Lane Deviation (VLD) were used in order to obtain independent class labels, whereas KSS provided subjective alertness labels while VLD provided objective alertness labels. The general ETS and the fused EEG/EOG measures contain substantially greater amounts of fatigue information than the PERCLOS measures alone. These conclusions were found to be valid for all three commercially available infrared video camera systems that were utilized in the study. The data utilized in the discrimination analysis were obtained from 16 young volunteers who participated in overnight experiments in the real car driving simulation lab at the University of Schmalkalden

Technologies for the Monitoring and Prevention of Driver Fatigue

A series of driving simulation pilot studies on various technologies for alertness monitoring (head position sensor, eye-gaze system), fitness-for-duty testing (two pupil-based systems), and alertness promotion (in-seat vibration system) has been conducted in Circadian Technologies’ Alertness Testbed. The results indicate that, all tested technologies show promise for monitoring/testing or preventing driver fatigue, respectively. However, particularly for fatigue monitoring, no single measure alone may be sensitive and reliable enough to quantify driver fatigue. Since alertness is a complex phenomenon, a multi-parametric approach needs to be used. Such a multi-sensor approach imposes challenges for online data interpretation. We suggest using a neural-fuzzy hybrid system for the automatic assessment of complex data streams for driver fatigue. The final system output can then be used to trigger the activation of alertness countermeasures

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

Assessing Driver Fatigue as a Factor in Road Accidents

Fatigue is one of the most pervasive yet under-investigated causes of human-error-related driving accidents, incidents, and injuries. Several studies suggest that 25-30% of driving accidents are fatigue related (Horne et al., 1995). However, government reports estimate that only 1-4% of crashes may be attributable to the driver falling asleep or being drowsy, based largely on data derived from police reports recorded at these accidents (Cummings et al., 2001). The reason for this wide disparity is that there is no simple tool or objective way for investigators to collect the (right) data needed to correlate accidents with fatigue. To bridge this gap, a diagnostic survey instrument was developed, along with a weighted risk model based on Fuzzy Scalable Monotonic Chaining (FSMC), to help investigators readily determine (by standardized criteria and with high probability) the role of fatigue as a causal factor in driving accidents

Evaluation of Fatigue Management Technologies Using Weighted Feature Matrix Method

Operator fatigue is one of the most prevalent root causes of accidents,both on the highway and in workplaces where heavy equipment is used and 12-hour shifts are employed, such as in the mining industry. In response to thisconcern, a growing number of Fatigue Management Technologies (FMT) arebecoming available to help maintain operator alertness and performance levels bydetecting operator fatigue and interfacing with the operator and/or supervisor toprevent accidents and incidents (Williamson et al., 2005, Barr et al., 2005). Inlight of the numerous competing technologies, the research community, as well asindustry, could benefit from the flexible evaluation tool proposed here. It willassist industries as a whole, and corporations more specifically, in identifying thebest FMT solutions for different work and/or driving situations. This project wasspecifically focused on the needs of operators of heavy equipment in the miningindustry, but could also be of value to other like industries where shift work isnecessary and maintaining high levels of alertness are crucial for ensuringworkplace safety and productivity