EEG–EMG coupling as a hybrid method for steering detection in car driving settings

Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Analyzing the relationship between distracted driving and eye movement using multimodal data collected during car driving

Human error is the leading cause of traffic accidents and originates from the distraction caused by various factors, such as the driver's physical condition and mental state. One of the significant factors causing driver distraction is the presence of stress. In a previous study, multiple stressors were used to examine distraction while driving. Multiple stressors were given to the driver and the corresponding driver biometric data were obtained, and a multimodal dataset was published thereafter. In this study, we reiterate the results of existing studies and investigated the relationship between gaze variability while driving and stressor intervention, which has not yet been examined. We also examined whether biometric and vehicle information can estimate the presence or absence of secondary tasks during driving.

Car Driving Status and Living Arrangement Associated with Sarcopenia among Rural Japanese Older Adults: A Cross-Sectional Study

Ensuring mobility after driving cessation is an important public health issue to prevent functional limitations, but this issue is still not fully understood in rural settings. The aim of this study was to test the hypothesis that being a non-driver and living alone is associated with a greater risk of sarcopenia among the community-dwelling elderly in rural Japanese areas. This study was conducted in 2018 and data from 738 participants were used. Sarcopenia was assessed by measuring walking speed, handgrip strength, and skeletal muscle mass. Car driving status and living arrangement were collected using self-reported questionnaires and face-to-face interviews. Four groups were set to determine combined conditions of car driving status and living arrangement. Logistic regression analysis was performed to estimate the odds ratio (OR) and a 95% confidence interval of sarcopenia after adjustment for confounding factors. Compared with the reference group (driver and living with others), the OR of sarcopenia was significantly higher in the non-driver and living alone group (OR = 2.21; 95% confidence interval, 1.02–4.80). Our findings suggest that the consideration of both driving status and living arrangement are important in the formulation of public health strategies to prevent sarcopenia in rural Japanese areas.

IMPROVING AUTOMOBILE INSURANCE CLAIMS FREQUENCY PREDICTION WITH TELEMATICS CAR DRIVING DATA

Novel navigation applications provide a driving behavior score for each finished trip to promote safe driving, which is mainly based on experts’ domain knowledge. In this paper, with automobile insurance claims data and associated telematics car driving data, we propose a supervised driving risk scoring neural network model. This one-dimensional convolutional neural network takes time series of individual car driving trips as input and returns a risk score in the unit range of (0,1). By incorporating credibility average risk score of each driver, the classical Poisson generalized linear model for automobile insurance claims frequency prediction can be improved significantly. Hence, compared with non-telematics-based insurers, telematics-based insurers can discover more heterogeneity in their portfolio and attract safer drivers with premiums discounts.

Sex Differences in the Amount and Patterns of Car-Driving Exposure in Spain, 2014 to 2017: An Application of a Quasi-Induced Exposure Approach

We designed a cross-sectional study in Spain, from 2014 to 2017. Our objective was to assess sex-related differences in the amount of driving exposure of car drivers, overall and stratified by the main environment-related driving conditions. We compared the sex distribution across three populations: (1) total number of person-years aged > 18 years; (2) total number of person-years aged > 18 years holding a valid car-driving license; and (3) total number of non-responsible car drivers involved in crashes with another offending driver, stratified by different environmental variables. The quasi-induced exposure approach was applied: the non-responsible drivers were considered as representative of the entire population of drivers on the road at the place and time at which the crash occurred. We calculated the female-to-male odds ratio (OR) by comparing population 2 versus 1, and population 3 versus 2. Finally, we performed separate regression models in population 3 for each environment-related variable as the dependent variable and driver’s age and sex as the independent variables. The female-to-male OR for the first comparison was 1.12, but values below 1 were found for extreme age groups. In the second comparison, an OR of 0.50 (0.49–0.51) was found, with progressively lower OR values as age increased. In population 3, women were found to drive less than men in environments known to be high risk (i.e., open roads, night-time, poor light conditions, and weekends). A significant gender gap exists in the amount and type of driving exposure. Although women obtain a driving license more frequently than men, they drive much less and tend to avoid high-risk environments. These results emphasize the need to incorporate a gender perspective in the development and implementation of road safety interventions.

Pupil dilation as indicative of cognitive workload while driving a car: effects of cell phone use and driver experience in young adults

BACKGROUND: Cognitive workload resulting from drivers’ engagement in concomitant tasks while driving, such as talking on a cell phone, affects the availability of attentional resources for the various stages of information processing, which can interfere with the selection of relevant traffic information, leading to poor performance and higher risk of accidents. AIM: The purpose of this study was to test the adaptation and application of the method of fixation-aligned pupillary response averaging to the car driving context, and, if successful, to determine effects of talking on a cell phone while driving, in both handheld and hands-free situations, and effects of driving experience on pupillary responses of young adult drivers, as indicative of cognitive workload. METHOD: Ten novice and ten experienced drivers had pupil diameter measured while driving in a car simulator under velocity of 80-120 km/h, daylight, linear trajectory and low traffic level. Data analysis was based on the method of fixation-aligned pupillary response averaging. RESULTS: Noise curves were around baseline (zero) values while pupil dilation curves clearly stood out from noise magnitude, in all conditions for both groups. Greater pupil dilation peak during talking on the cell phone (handheld and hands-free conditions) while driving occurred only for the novice group. CONCLUSION: Adaptation and application of the method of fixation-aligned pupillary response averaging to the car driving context succeed. Cognitive workload imposed by the dual task of talking on a cell phone increased pupil dilation for novice drivers, which may alter acquisition of visual information and impair driving behavior.

Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios

The complexity of concurrent cerebral processes underlying driving makes such human behavior one of the most studied real-world activities in neuroergonomics. Several attempts have been made to decode, both offline and online, cerebral activity during car driving with the ultimate goal to develop brain-based systems for assistive devices. Electroencephalography (EEG) is the cornerstone of these studies providing the highest temporal resolution to track those cerebral processes underlying overt behavior. Particularly when investigating real-world scenarios as driving, EEG is constrained by factors such as robustness, comfortability, and high data variability affecting the decoding performance. Hence, additional peripheral signals can be combined with EEG for increasing replicability and the overall performance of the brain-based action decoder. In this regard, hybrid systems have been proposed for the detection of braking and steering actions in driving scenarios to improve the predictive power of the single neurophysiological measurement. These recent results represent a proof of concept of the level of technological maturity. They may pave the way for increasing the predictive power of peripheral signals, such as electroculogram (EOG) and electromyography (EMG), collected in real-world scenarios when informed by EEG measurements, even if collected only offline in standard laboratory settings. The promising usability of such hybrid systems should be further investigated in other domains of neuroergonomics.

Questionnaire Survey on Driving among Patients with Age-Related Macular Degeneration in Japan

Purpose: This study aimed to assess driving capabilities in patients with exudative age-related macular degeneration (AMD) causing unilateral blindness or paracentral scotoma without vision deterioration. Methods: Of the 275 patients with AMD who responded to a questionnaire regarding car driving at Osaka University Hospital, we excluded 78 patients who answered that they had never driven. Finally, 197 patients were included (50 with bilateral and 142 with unilateral AMD). We investigated the relationship between the questionnaire findings and best-corrected visual acuity (BCVA). Results: The mean age was 74.8 ± 6.9 years, and the mean BCVA in the right and left eyes were 0.48 and 0.47, respectively. A negative correlation was observed between the proportion of patients who stopped driving due to AMD and the vision in the worse eye (p < 0.0001); however, 66% of participants were still driving. Regardless of the BCVA, 84% of them wished to continue driving. Concerning perceived dangerous situations, all patients reported an oversight of people or signals and night driving; further, patients with unilateral and bilateral vision deterioration reported vision narrowness and difficulty with discerning signal colours, respectively. Conclusion: Despite the associated danger, patients with AMD continued driving. Close attention should be paid to the driving activities among patients with AMD, even if they have passed the relevant driving tests.