Quantitative estimation of a driver's vigilance level has a great value for improving driving safety and preventing accidents. Previous studies have identified correlations between electroencephalogram (EEG) spectrum power and a driver's mental states such as vigilance and alertness. Studies have also built classification models that can estimate vigilance state changes based on data collected from drivers. In the present study, we propose a system to detect vigilance level using not only a driver's EEG signals but also driving contexts as inputs. We combined a support vector machine with particle swarm optimization methods to improve classification accuracy. A simulated driving task was conducted to demonstrate the reliability of the proposed system. Twenty participants were assigned a 2-h sustained-attention driving task to identify a lead car's brake events. Our system was able to account for 84.1% of experimental reaction times with 162-ms prediction errors. A newly introduced driving context factor, road curves, improved the prediction accuracy by 2-5% with 30-80 ms smaller errors. These findings demonstrated the potential value of the proposed system for estimating driver vigilance level on a time scale of seconds.

Navigation systems have been widely used in outdoor environments, but indoor navigation systems are still in early development stages. In this paper, we introduced an augmented-reality-based indoor navigation application to assist people navigate in indoor environments. The application can be implemented on electronic devices such as a smartphone or a head-mounted device. In particular, we examined Google Glass as a wearable head-mounted device in comparison with handheld navigation aids including a smartphone and a paper map. We conducted both a technical assessment study and a human factors study. The technical assessment established the feasibility and reliability of the system. The human factors study evaluated human-machine system performance measures including perceived accuracy, navigation time, subjective comfort, subjective workload, and route memory retention. The results showed that the wearable device was perceived to be more accurate, but other performance and workload results indicated that the wearable device was not significantly different from the handheld smartphone. We also found that both digital navigation aids were better than the paper map in terms of shorter navigation time and lower workload, but digital navigation aids resulted in worse route retention. These results could provide empirical evidence supporting future designs of indoor navigation systems. Implications and future research were also discussed.

Background: Advancements in supporting personalized health care and well-being using virtual reality (VR) have created opportunities to use immersive games to support a healthy lifestyle for persons living with dementia and mild cognitive impairment (MCI). Collaboratively designing exercise video games (exergames) as a multistakeholder team is fundamental to creating games that are attractive, effective, and accessible. Objective: This research extensively explores the use of human-centered design methods that involve persons living with dementia in long-term care facilitates, exercise professionals, content developers, game designers, and researchers in the creation of VR exergames targeting physical activity promotion for persons living with dementia/MCI. Methods: Conceptualization, collaborative design, and playtesting activities were carried out to design VR exergames to engage persons living with dementia in exercises to promote upper limb flexibility, strength, and aerobic endurance. We involved a total of 7 persons living with dementia/MCI, 5 exercise professionals, 5 community-dwelling older adults, a VR company for content creation, and a multidisciplinary research team with game designers, engineers, and kinesiology experts. Results: An immersive VR exergame called Seas the Day was jointly designed and developed and it is freely available to be played in state-of-the-art VR headsets (Oculus Quest 1, 2). A model for the triadic interaction (health care institution, industry partner, academia) is also presented to illustrate how different stakeholders contribute to the design of VR exergames that consider/complement complex needs, preferences, and motivators of an underrepresented group of end users. Conclusions: This study provides evidence that a collaborative multistakeholder design results in more tailored and context-aware VR games for persons living with dementia. The insights and lessons learned from this research can be used by others to co-design games, including remote engagement techniques that were used during the COVID-19 pandemic.

Hazard perception (HP) is the process of detecting and identifying hazards. Drivers’ HP abilities are critical for driving safety. This paper presents a systematic literature review of driver HP, including scientific measures of HP, major human factors affecting HP, and training methods for improving HP skills. Sixty-nine peer-reviewed studies were identified and reviewed. The results showed that common measures of HP include hazard scenario questionnaires, HP reaction time, hazard hit rate, and eye fixation measures such as fixation probability, fixation reaction time, fixation duration, and fixation variance. Major human factors that affect HP include experience, aging, fatigue, distraction, and the use of alcohol and drugs. Various training methods have been developed to train and improve drivers’ HP skills. In general, there is evidence in the literature showing the effectiveness of HP training. A combination of complementary training approaches such as instruction, expert demonstration, and active practice with feedback and attention support the use of picture-, video-, computer-, and simulator-based training methods to improve HP performance in shorter HP reaction time, higher hazard hit rate, and better eye scan patterns (more spread scan, more anticipatory scan). These findings could guide future work developing and designing HP training programs. Three future research areas are identified and discussed: the need for standardized HP tests, long-term testing of HP training programs, and new HP questions and challenges brought by partially automated vehicles.

Driver distraction is one of the primary causes of crashes. As a result, there is a great need to continuously observe driver state and provide appropriate interventions to distracted drivers. Cognitive distraction refers to the “look but not see” situations when the drivers’ eyes are focused on the forward roadway, but their mind is not. Typically, cognitive distractions can result from fatigue, conversation with a co-passenger, listening to the radio, or other similarly loading secondary tasks that do not necessarily take a driver’s eyes off the roadway. This makes it one of the hardest distractions to detect as there are no visible clues of driver distraction. In this study, we have identified features from different sources including eye-tracking, physiological, and vehicle kinematics data that are relevant towards the classification of distracted and non-distracted drivers via the analysis of data collected from a driving simulator study involving 40 drivers across multiple driving scenarios. The key classification algorithms implemented include Random Forest, Decision Trees and Support Vector Machines. A reduced feature set including pupil area, pupil vertical and horizontal motion was found to be predictive of driver distraction while maintaining an average accuracy of 90% across various road types. Additionally, the impact of road types on driver behaviour was also identified. The findings of the study has practical application towards the design of driver distraction monitoring systems.