Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. Methods: Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. Result: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. Conclusion: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. Significance: The proposed method can contribute to preventing accidents caused by drowsy driving.

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As a result of well-organized co-operation in controlled nuclear fusion research, each day sees the rapid spread of knowledge and results gained in this field throughout the world. Thus, the knowledge is steadily accumulating. However, perhaps for psychological reasons, views on the status of the problem change rather abruptly. With the same corpus of knowledge, we are now beginning to recognize clearly the nature of a problem which was shrouded in mist only yesterday, and yet tomorrow we may be unable to believe that the mist ever existed. One eminent English writer, I believe, neatly expressed this thought in an aphorism to the effect that every truth enjoys only a brief moment of triumph between one period during which it is rejected as a paradox and another during which it is accepted as trivial. To experience these brief moments, we gather together at IAEA Conferences which afford us so much joy.

: Drowsy driving accidents can be prevented if predicted in advance. The present work aims to develop a new method for detecting driver drowsiness based on the fact that the autonomic nervous function affects heart rate variability (HRV), which is a fluctuation of the RR interval (RRI) obtained from an electrocardiogram (ECG). The proposed method uses eight HRV features derived through HRV analysis as input variables of multivariate statistical process control (MSPC), which is a well-known anomaly detection method in the field of process control. In the proposed method, only one principal component was adopted in MSPC and driver drowsiness was detected through monitoring the T 2 statistic. Driving simulator experiments demonstrated that driver drowsiness was successfully detected in seven out of eight cases before accidents occurred. In addition, the proposed method was implemented in a smartphone app for on-vehicle use.