This study examines the effectiveness of game-based learning in enhancing hazard identification skills. The research employed a mixed-methods approach, combining quantitative analysis of participants’ hazard identification performance and eye-tracking data with qualitative insights from participant feedback. In a simulated study, participants were asked to identify hazards following the game-based training intervention. The Hazard Identification Index (HII) scores increased significantly (p < 0.0001) in the post-training trials, indicating enhanced hazard recognition abilities. The analysis of eye-tracking data revealed changes in participants’ attention allocation patterns in the post-training trials. Furthermore, the study highlights the importance of minimizing task interruptions during hazard identification activities, as interruptions negatively impacted participants’ performance. The findings of this study have significant implications for training programs focused on workplace safety. Integrating game-based learning in safety training programs can help employers enhance hazard identification skills among their employees, ultimately contributing to safer work environments and reducing incidents of accidents and injuries.

In recent decades, automation has become increasingly integrated into knowledge work environments, i.e., jobs that require specialized skills for task completion. Some research has shown that automation may reduce cognitive workload. However, other work reports that increasing automation does not always lower cognitive workload. These conflicting results suggest that further investigation is needed to identify factors that influence the relationship between automation use and cognitive workload. Therefore, this study aimed to investigate how two potential moderators, task complexity and age, influence that relationship. A total of 24 younger and 24 middle-aged adults performed an object identification task that resembled a baggage scanning procedure conducted by airport security personnel. The task was manipulated by varying the level of automation (4 increasing levels) that provided support in identifying objects of interest as well as the complexity of task (two types: component and coordinative). In addition, participants completed a pattern identification and sorting memory task, which helped emulate a knowledge work environment. Performance (i.e., object identification accuracy, average completion time, pattern identification accuracy, and sorting memory task accuracy) and subjective workload (NASA-RTLX score) measures were recorded. Participants also rated the usability of the automation. Overall, results showed that while the use of automation was associated with reduced cognitive workload, both types of task complexity negatively affected this relationship such that increased complexity was associated with a decrease in accuracy. Age did not moderate the relationship between automation and cognitive workload, but there were qualitative differences in terms of how the two age groups perceived the utility and usability of the automated systems. Knowledge generated from this research has implications for the design of future human-automation systems and can be used to inform interface design that is tailored to the needs and preferences of different users and use cases.

Partially autonomous driving systems may require the human driver to take control at any moment, yet by their design, they often cause difficulty with attention management. In this preliminary study, we propose a data- and dynamics-driven approach to characterize driving performance in a partially autonomous vehicle during a manual braking event, under attentive or mind wandering states. A 10-participant experiment was completed in an advanced driving simulator. We employ a non-parametric learning technique, conditional distribution embeddings, to the driving simulator data, to evaluate likelihood of successfully completing the braking maneuver, under both attentive and mind wandering states. Our approach shows a statistically significant difference in braking profiles during mind wandering and non-mind wandering episodes for each participant. Our results reveal that heterogeneity in driving performance may have important implications for the design of autonomy that is responsive to attentional states. Data-driven tools, such as the one proposed here, may be useful in designing participant-specific alerts and warnings for control handovers and other safety-critical maneuvers, because of their potential to accommodate heterogeneous response.

Smart speech systems are becoming increasingly pervasive in society. At the same time, the number of older adults is rapidly growing. These simultaneous trends make it likely for older individuals to encounter and, in some cases, benefit from speech systems throughout later stages of life. To date, most research studies have examined older adult non-users’ opinions of speech systems, but not the sentiments of older users. To address this research gap, four focus groups were conducted to compare the perceptions and attitudes of seniors who voluntarily use and do not use speech systems across various devices. Findings suggest that older users and non-users are similar in their perception of the advantages provided this technology, factors that (could) motivate their use, common challenges faced while using these systems, and barriers to using particular features or speech systems altogether. The two groups differed in their preferences for learning how to use these systems, perception of system cost, and global perception of technology. In addition, older adult users exclusively believed speech systems to be easy to use, but also expressed concerns about information transparency and privacy. Older non-users explained that the absence of age-related declines was a barrier to use. These results may guide designers and researchers in developing, evaluating, and refining smart technologies to be used by various senior populations.

Automated vehicles may occasionally require drivers to take over. The complexity of the takeover process warrants the design of effective human–machine interfaces that assist drivers in regaining control, especially when the visual and auditory sensory modalities are occupied. Vibrotactile displays, which can represent information about the status, direction, and position of driving environment elements, have been suggested as one promising approach, but their effectiveness to aid in takeover transitions has not been fully evaluated. This study investigated the effects of meaningful tactile signal patterns, used as takeover requests, on automated vehicle takeover performance. Forty participants rode in a simulated SAE Level 3 automated vehicle and completed a series of takeover tasks with two tactile pattern formats, i.e., informative (which displayed status information of surrounding vehicles) and instructional (that displayed the appropriate takeover maneuver), and three in-vehicle locations (seat back, seat pan, and a seat back and seat pan combination). Takeover response options included lane changes only or brake applications followed by changing lanes, depending on the locations of surrounding vehicles. Results indicate that only meaningful instructional tactile signals, in either the seat back or seat pan, were associated with worse takeover response time and maximum resulting acceleration compared to signals without any patterns. Additionally, tactile information presented on the seat back was perceived as the most useful and satisfying. Findings from this study can inform the development of next-generation human–machine interfaces that utilize tactile stimulation in a wide range of environments with automation.

Maintaining situation awareness (SA) is essential for drivers to deal with the situations that Society of Automotive Engineers (SAE) Level 3 automated vehicle systems are not designed to handle. Although advanced physiological sensors can enable continuous SA assessments, previous single-modality approaches may not be sufficient to capture SA. To address this limitation, the current study demonstrates a multimodal sensing approach for objective SA monitoring. Physiological sensor data from electroencephalogram and eye-tracking were recorded for 30 participants as they performed three secondary tasks during automated driving scenarios that consisted of a pre-takeover (pre-TOR) request segment and a post-TOR segment. The tasks varied in terms of how visual attention was allocated in the pre-TOR segment. In the post-TOR segment, drivers were expected to gather information from the driving environment in preparation for a vehicle-to-driver transition. Participants' ground-truth SA level was measured using the Situation Awareness Global Assessment Techniques (SAGAT) after the post-TOR segment. A total of 23 physiological features were extracted from the post-TOR segment to train computational intelligence models. Results compared the performance of five different classifiers, the ground-truth labeling strategies, and the features included in the model. Overall, the proposed neural network model outperformed other machine learning models and achieved the best classification accuracy (90.6%). A model with 11 features was optimal. In addition, the multi-physiological sensor-model outperformed the single sensing model by comparing prediction performance. Our results suggest that multimodal sensing model can objectively predict SA. The results of this study provide new insight into how physiological features contribute to the SA assessment.

Research on multimodal information processing has seen a surge in recent years. Most of this research suffers from a significant shortcoming: the failure to perform crossmodal matching. Crossmodal matching refers to equating perceived intensities of stimuli across two sensory modalities. This step is critical for avoiding that modality is confounded with other signal properties. Very few studies include this step, and there is no agreed-upon matching technique. An experiment comparing two different approaches shows that even minor variations can lead to significant differences in match values and intraindividual match variability. Our findings highlight the need for developing and employing a valid crossmodal matching procedure for future studies.