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.

The proposed method can be used in daily life, because the heart rate can be measured easily by using a wearable sensor.

Development of efficient methods for finding chemical reaction pathways has been one of the central subjects of theoretical chemistry. Recently, the artificial force induced reaction (AFIR) method enabled automated search for associative reaction pathways between multiple reactant molecules and has been applied to reactions involving a few tens of atoms. To expand its applicability to large systems, we combined it with the geometrical microiteration technique. With this extension, full optimization of transition state structures of enzymatic reactions in the protein became possible within the QM/MM framework. Performance of the microiteration-AFIR method was tested for a single water catalyzed Aldol reaction in (H2O)299 cluster and for an enzymatic reaction of the isopenicillin N synthase, where the potential energy surfaces were calculated by the ONIOM(QM/MM) method. These numerical tests demonstrated that the present method is promising in predicting reaction pathways that take place within an active site (consisting of tens of atoms) in a very large environment such as protein and solution.

: 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.

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