Human Factors最新文献

Objective: To design and develop a Portable Auditory Localization Acclimation Training (PALAT) system capable of producing psychoacoustically accurate localization cues; evaluate the training effect against a proven full-scale, laboratory-grade system under three listening conditions; and determine if the PALAT system is sensitive to differences among electronic level-dependent hearing protection devices (HPDs).

Background: In-laboratory auditory localization training has demonstrated the ability to improve localization performance with the open (natural) ear, that is, unoccluded, and while wearing HPDs. The military requires a portable system capable of imparting similar training benefits as those demonstrated in laboratory experiments.

Method: In a full-factorial repeated measures design experiment, 12 audiometrically normal participants completed localization training and testing using an identical, optimized training protocol on two training systems under three listening conditions (open ear, TEP-100, and ComTac™ III). Statistical tests were performed on mean absolute accuracy score and front-back reversal errors.

Results: No statistical difference existed between the PALAT and laboratory-grade DRILCOM systems on two dependent localization accuracy measurements at all stages of training. In addition, the PALAT system detected the same localization performance differences among the three listening conditions.

Conclusion: The PALAT system imparted similar training benefits as the DRILCOM system and was sensitive to HPD localization performance differences.

Application: The user-operable PALAT system and optimized training protocol can be employed by the military, law enforcement, and various industries, to improve auditory localization performance in conditions where auditory situation awareness is critical to safety.

Objective: This study aimed to investigate the effects of a head/neck supporting exoskeleton (HNSE) on the electromyographic fatigue threshold (EMG_FT) of the neck and shoulder muscles during a simulated overhead work task.

Background: Overhead work is a well-known risk factor for neck and shoulder musculoskeletal disorders due to the excessive strain imposed on the muscles and joints in these regions.

Method: Fourteen healthy males performed a repetitive overhead nut fastening/unfastening task to exhaustion while wearing and not wearing the HNSE at two neck extension angles (40% and 80% of neck maximum range of motion). Electromyographic signals were continuously recorded from the right and left sternocleidomastoid (SCMR, SCML), splenius capitis (SCR, SCL), upper trapezius (UTR, UTL), and anterior deltoid (ADR, ADL) muscles. The normalized electromyographic amplitude (nEMG) data was time normalized, and a bisegmental linear regression was applied to determine the muscle fatigue break point.

Results: The results showed a significant increase in fatigue threshold time in the SCMR (p < .001), SCML (p = .002), and UTR (p = .037) muscles when the HNSE was used. However, the EMG_FT times for the right and left deltoid and left trapezius muscles showed a nonsignificant reduction due to the head/neck support exoskeleton use. In addition, the neck extension angle did not reveal a significant effect on muscles' EMG_FT time.

Conclusion: Overall, the findings confirmed a significant delay in fatigue onset in sternocleidomastoid muscles, as measured by the electromyographic fatigue threshold. This finding suggests that the HNSE can be an effective ergonomic intervention for reducing the risk of musculoskeletal disorders in overhead workers. However, further studies are needed to investigate the effect of the HNSE at other neck extension angles and more realistic tasks to ensure the generalizability of our results.

Application: The present findings emphasize the application of the fatigue onset time to evaluate the effectiveness of ergonomic interventions, including exoskeletons, which can subsequently be utilized to alleviate postural demands and reduce the risk of musculoskeletal disorders.

Objective: To determine whether typical road users appreciate the special optical properties of retroreflective materials.

Background: Retroreflective surfaces reflect light back towards the source of the illumination. All drivers benefit from retroreflective materials, as they are required on road signs, on large trailers, in lane delineation, and other traffic control devices. Retroreflective markings can also greatly enhance the conspicuity of pedestrians at night, but pedestrians typically underuse retroreflective markings. One possible reason is that pedestrians may not appreciate the special optical properties of retroreflective materials.

Method: Two experiments tested whether observers could correctly predict that retroreflective materials appear remarkably bright when illuminated by a source that is aligned with the observers' eyes. Observers used a magnitude estimation procedure to predict how bright retroreflective and non-retroreflective stimuli would appear during a demonstration designed to highlight retroreflectivity. They then judged the brightness again during the demonstration.

Results: In general, observers underestimated how bright retroreflective stimuli would be and overestimated how bright diffuse reflective and fluorescent stimuli would be. The underestimates for retroreflective stimuli were particularly striking when the observers had not closely examined the stimuli in advance.

Conclusion: The fact that road users do not appreciate retroreflectivity may help explain why pedestrians underuse retroreflective markings at night.

Application: Educational interventions could prove useful in this domain.

Objective: This study aimed to assess the plausibility of self-control depletion, or ego-depletion, as the underlying cognitive resource responsible for performance decrements on the sustained attention to response task.

Background: Researchers suggested that self-control is a limited cognitive resource used to complete a myriad of processes, including sustained attention. Past research showed that trait self-control affects some sustained attention tasks. However, little research has investigated the effect of self-control as a limited cognitive resource that varies over time (i.e., as a state-dependent variable).

Methods: This experiment investigated the effect of self-control (trait and state) on a sustained motor-inhibition task (e.g., sustained attention to response task; SART). State self-control was manipulated using a between-subjects design-participants in the experimental condition completed a task designed to deplete state self-control prior to performing the SART while the control condition completed a modified version that did not deplete self-control.

Results: Trait self-control predicted performance on the SART, but the depletion task (state self-control) had no detectable effect.

Conclusion: Given the evidence, it is unlikely that state self-control plays a causal role in performance decrements in the SART, but there appears to be some association between performance on the SART and trait self-control.

Application: Trait self-control ought to be considered in future work for personnel selection in real-world tasks that the SART models such as long-distance driving, air traffic control, and TSA operations.

Objective: This study aimed to explore the relationship between system interface elements' design features and interaction performance in simulated vehicle vibration environments.

Background: Touch screens have been widely used in vehicle information systems, but few studies have focused on the decline of touchscreen interaction performance and task load increase when driving on unpaved roads.

Method: The interaction performance (reaction time and task accuracy rate) with vibration frequencies below 3 Hz (1.5, 2.0, and 2.5 Hz) and different interface design elements was investigated employing a touch screen computer and E-prime software.

Results: The results indicate that vehicle vibration (below 3 Hz) can significantly reduce interaction performance with a vehicle information system interface.

Conclusion: An appropriate increase in the physical size of the interface design features (visual stimulus materials and touch buttons) can help to mitigate this negative effect of vibration.

Application: The results and findings of this study can be utilized for the design of information system interfaces as it relates to the vibration scenario of unpaved roads.

Objective: We aimed to develop advanced machine learning models using electroencephalogram (EEG) and eye-tracking data to predict the mental workload associated with engaging in various surgical tasks.

Background: Traditional methods of evaluating mental workload often involve self-report scales, which are subject to individual biases. Due to the multidimensional nature of mental workload, there is a pressing need to identify factors that contribute to mental workload across different surgical tasks.

Method: EEG and eye-tracking data from 26 participants performing Matchboard and Ring Walk tasks from the da Vinci simulator and the pattern cut and suturing tasks from the Fundamentals of Laparoscopic Surgery (FLS) program were used to develop an eXtreme Gradient Boosting (XGBoost) model for mental workload evaluation.

Results: The developed XGBoost models demonstrated strong predictive performance with R² values of 0.82, 0.81, 0.82, and 0.83 for the Matchboard, Ring Walk, pattern cut, and suturing tasks, respectively. Key features for predicting mental workload included task average pupil diameter, complexity level, average functional connectivity strength at the temporal lobe, and the total trajectory length of the nondominant eye's pupil. Integrating features from both EEG and eye-tracking data significantly enhanced the performance of mental workload evaluation models, as evidenced by repeated-measures t-tests yielding p-values less than 0.05. However, this enhancement was not observed in the Pattern Cut task (repeated-measures t-tests; p > 0.05).

Conclusion: The findings underscore the potential for machine learning and multidimensional feature integration to predict mental workload and thereby improve task design and surgical training.

Application: The advanced mental workload prediction models could serve as instrumental tools to enhance our understanding of surgeons' cognitive demands and significantly improve the effectiveness of surgical training programs.

Objective: This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems.

Background: Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven.

Method: Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods.

Results: Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found.

Conclusion: Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo.

Application: The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces.

With vehicle automation becoming more commonplace, the role of the human driver is shifting from that of system operator to that of system supervisor. With this shift comes the risk of drivers becoming more disengaged from the task of supervising the system functioning, thus increasing the need for technology to keep drivers alert. This special issue includes the most up-to-date research on how drivers use vehicle automation, and the safety risks it may pose. It also investigates the accuracy that driver monitoring systems have in detecting conditions like driver distraction and drowsiness, and explores ways future drivers may respond to the broader introduction of this technology on passenger vehicles.

Objective: Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving.

Background: Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers' states posits it could become a novel method for measuring risk perception.

Methods: Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports.

Results: Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario.

Conclusion: This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario.

Application: Little is known regarding the effects of drivers' risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology.

Objective: Varying driver distraction algorithms were developed using vehicle kinematics and driver gaze data obtained from a camera-based driver monitoring system (DMS).

Background: Distracted driving characteristics can be difficult to accurately detect due to wide variation in driver behavior across driving environments. The growing availability of information about drivers and their involvement in the driving task increases the opportunity for accurately recognizing attention state.

Method: A baseline for driver distraction levels was developed using a video feed of 24 separate drivers in varying naturalistic driving conditions. This initial assessment was used to develop four buffer-based algorithms that aimed to determine a driver's real-time attentiveness, via a variety of metrics and combinations thereof.

Results: Of those tested, the optimal algorithm included ungrouped glance locations and speed. Notably, as an algorithm's performance of detecting very distracted drivers improved, its accuracy for correctly identifying attentive drivers decreased.

Conclusion: At a minimum, drivers' gaze position and vehicle speed should be included when designing driver distraction algorithms to delineate between glance patterns observed at high and low speeds. Distraction algorithms should be designed with an understanding of their limitations, including instances in which they may fail to detect distracted drivers, or falsely notify attentive drivers.

Application: This research adds to the body of knowledge related to driver distraction and contributes to available methods to potentially address and reduce occurrences. Machine learning algorithms can build on the data elements discussed to increase distraction detection accuracy using robust artificial intelligence.