Human Factors最新文献

Objective: This study explored the impact of in-vehicle messages relative to roadside messages to alert drivers to events within a simulated work zone, in order to determine if these messages can improve driving performance within the work zone.

Background: Safety risks in work zones are usually mitigated by design standards and clear signage to communicate work zone information to drivers. Due to distraction and other driving task demands, these signs are not always noticed by motorists, nor are they always followed when they are noticed.

Method: The driving simulation tested drivers in two different types of work zones, shoulder work, and lane closure. Participants drove through these work zones three times, each with different messaging interfaces to communicate hazardous events to the driver. The interfaces included a roadside, portable changeable message sign, a smartphone presenting only auditory messages, and a smartphone presenting audio-visual messages.

Results: There was significantly better driving performance on key metrics including lane deviation for the in-vehicle message conditions relative to the roadside signs. Furthermore, drivers directed visual attention toward the roadway for the in-vehicle message conditions relative to the roadside sign condition.

Conclusion: The results indicate that in-vehicle messaging could provide benefits to primary task performance in driving if the message content is appropriately designed.

Application: The findings provide support for a design framework to support in-vehicle communication to drivers approaching work zones and other environments to safely alert them to hazards.

Objective: To examine and evaluate ways in which an understanding of the quintessential element of Human Factors/Ergonomics can address the spectrum of existential threats that confront contemporary civilization.

Background: HF/E is dedicated to improving quality of life. Paradoxically, many processes which sustain contemporary civilization act to reduce that overall quality. Some technological developments themselves now even present existential threats to the fragile skein of civilization itself. Many disciplines address these diverse threats, and each may be advised and facilitated by HF/E knowledge and methods. It is a moral imperative of our science to contribute what we can to proposed resolutions.

Method: A primary conduit, by the established strengths of HF/E can contribute to potential solutions is identified. The present work advocates for specific, practical interventions using a direct-perception mediated, panopticon principle, that derives from the corpus of our science.

Result: Limitations upon a general, social understanding of imminent global concerns, which are largely ignorable when not actually present, are brought to immediate consciousness via an HF/E principle emphasizing the direct-perception of threat. It is argued that this, and allied HF/E insights can generate practical steps toward problem resolution at both macroscopic and localized levels of implementation.

Applications: The primary, practical application of the proposed panopticon principle is to use our science to save global civilization. It is postulated that this represents useful employment of the knowledge we have adduced and accumulated across our discipline's existence.

Objective: The objective of this study was to estimate trust from conversations using both lexical and acoustic data.

Background: As NASA moves to long-duration space exploration operations, the increasing need for cooperation between humans and virtual agents requires real-time trust estimation by virtual agents. Measuring trust through conversation is a novel and unintrusive approach.

Method: A 2 (reliability) × 2 (cycles) × 3 (events) within-subject study with habitat system maintenance was designed to elicit various levels of trust in a conversational agent. Participants had trust-related conversations with the conversational agent at the end of each decision-making task. To estimate trust, subjective trust ratings were predicted using machine learning models trained on three types of conversational features (i.e., lexical, acoustic, and combined). After training, model explanation was performed using variable importance and partial dependence plots.

Results: Results showed that a random forest algorithm, trained using the combined lexical and acoustic features, predicted trust in the conversational agent most accurately $\left(R_{adj}^2=0.71\right)$. The most important predictors were a combination of lexical and acoustic cues: average sentiment considering valence shifters, the mean of formants, and Mel-frequency cepstral coefficients (MFCC). These conversational features were identified as partial mediators predicting people's trust.

Conclusion: Precise trust estimation from conversation requires lexical cues and acoustic cues.

Application: These results showed the possibility of using conversational data to measure trust, and potentially other dynamic mental states, unobtrusively and dynamically.

Objective: The present study investigated if the same users are vulnerable to phishing emails, scam text messages, and fake news headlines and if there are universal predictors of susceptibility for all three tasks.

Background: Theoretical research provides support for the notion that the same users likely fall for multiple forms of online deception. However, no research has directly compared susceptibility for various online deceptions (eg phishing, disinformation, scam text messages) within the same group of users.

Method: Participants completed an online survey consisting of demographic questions, the Cognitive Reflection Test (ie impulsivity), and the Digital Literacy Scale, and classified 90 legitimate and deceptive emails, text messages, and news headlines.

Results: Results suggest that individuals who struggle to discriminate between deceptive and legitimate stimuli on one task experience similar difficulties on the other two tasks. Additionally, while lower levels of digital literacy and cognitive reflectiveness predicted poorer discrimination abilities across all three tasks, age did not predict performance. Interestingly, participants appeared to be the most susceptible to phishing emails.

Conclusion: Overall, individuals who fall for one form of online deception appear to be more likely to fall for other forms of deception, and digital literacy and cognitive reflectiveness can predict widespread vulnerability to online deception.

Application: Organizations may be able to identify potential vulnerabilities for a variety of online attacks by measuring digital literacy, cognitive reflectiveness, and performance in one online deception task. Additionally, training interventions may be the most needed for phishing emails.

Objective: We investigated secondary-task-based countermeasures to the vigilance decrement during a simulated partially automated driving (PAD) task, with the goal of understanding the underlying mechanism of the vigilance decrement and maintaining driver vigilance in PAD.

Background: Partial driving automation requires a human driver to monitor the roadway, but humans are notoriously bad at monitoring tasks over long periods of time, demonstrating the vigilance decrement in such tasks. The overload explanations of the vigilance decrement predict the decrement to be worse with added secondary tasks due to increased task demands and depleted attentional resources, whereas the underload explanations predict the vigilance decrement to be alleviated with secondary tasks due to increased task engagement.

Method: Participants watched a driving video simulating PAD and were required to identify hazardous vehicles throughout the 45-min drive. A total of 117 participants were assigned to three different vigilance-intervention conditions including a driving-related secondary task (DR) condition, a non-driving-related secondary task (NDR) condition, and a control condition with no secondary tasks.

Results: Overall, the vigilance decrement was shown over time, reflected in increased response times, reduced hazard detection rates, reduced response sensitivity, shifted response criterion, and subjective reports on task-induced stress. Compared to the DR and the control conditions, the NDR displayed a mitigated vigilance decrement.

Conclusion: This study provided convergent evidence for both resource depletion and disengagement as sources of the vigilance decrement.

Application: The practical implication is that infrequent and intermittent breaks using a non-driving related task may help alleviate the vigilance decrement in PAD systems.

Objective: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks.

Background: Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages.

Method: This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance.

Results: Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values.

Conclusion: HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs.

Application: The established models can be used in realistic driving scenarios.

Introduction: In the 1950s and 1960s, John Senders carried out a number of influential experiments on the monitoring of multidegree-of-freedom systems. In these experiments, participants were tasked with detecting events (threshold crossings) for multiple dials, each presenting a signal with different bandwidth. Senders' analyses showed a nearly linear relationship between signal bandwidth and the amount of attention paid to the dial, and he argued that humans sample according to bandwidth, in line with the Nyquist-Shannon sampling theorem.

Objective: The current study tested whether humans indeed sample the dials based on bandwidth alone or whether they also use salient peripheral cues.

Methods: A dial-monitoring task was performed by 33 participants. In half of the trials, a gaze-contingent window was used that blocked peripheral vision.

Results: The results showed that, without peripheral vision, humans do not effectively distribute their attention across the dials. The findings also suggest that, when given full view, humans can detect the speed of the dial using their peripheral vision.

Conclusion: It is concluded that salience and bandwidth are both drivers of distributed visual attention in a dial-monitoring task.

Application: The present findings indicate that salience plays a major role in guiding human attention. A subsequent recommendation for future human-machine interface design is that task-critical elements should be made salient.

Objective: The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether interacting with the robot influences the participant's anxiety and attitude towards robots.

Background: Human-Robot Collaboration (HRC) could be used to alleviate pollinator shortages and robot performance issues. However, the effects of HRC for this setting have not been investigated.

Methods: Sixteen participants were recruited. Four HRC modes, no cue, with cue, unreliable, and manual control were included. Three categories of dependent variables were measured: (1) spine kinematics (L5/S1, L1/T12, and T1/C7), (2) pupillary activation data, and (3) subjective measures such as perceived workload, robot-related anxiety, and negative attitudes towards robotics.

Results: HRC reduced anxiety towards the cobot, decreased joint angles and angular velocity for the L5/S1 and L1/T12 joints, and reduced pupil dilation, with the "with cue" mode producing the lowest values. However, unreliability was detrimental to these gains. In addition, HRC resulted in a higher flexion angle for the neck (i.e., T1/C7).

Conclusion: HRC reduced the physical and mental workload during the simulated pollination task. Benefits of the additional cue were minimal compared to no cues. The increased joint angle in the neck and unreliability affecting lower and mid back joint angles and workload requires further investigation.

Application: These findings could be used to inform design decisions for HRC frameworks for agricultural applications that are cognizant of the different effects induced by HRC.

Objective: To assess frontal plane motion of the pelvis and lumbar spine during 2 h of seated and standing office work and evaluate associations with transient low back pain.

Background: Although bending and twisting motions are cited as risk factors for low back injuries in occupational tasks, few studies have assessed frontal plane motion during sedentary exposures.

Methods: Twenty-one participants completed 2 h of seated and standing office work while pelvic obliquity, lumbar lateral bending angles, and ratings of perceived low back pain were recorded. Mean absolute angles were compared across 15-min blocks, amplitude probability distribution functions were calculated, and associations between lateral postures and low back pain were evaluated.

Results: Mean pelvic obliquity (sit = 4.0 ± 2.8°, stand = 3.5 ± 1.7°) and lumbar lateral bending (sit = 4.5 ± 2.5°, stand = 4.1 ± 1.6°) were consistently asymmetrical. Pelvic obliquity range of motion was 4.7° larger in standing (13.6 ± 7.5°) than sitting (8.9 ± 8.7°). In sitting, 52% (pelvis) and 71% (lumbar) of participants, and in standing, 71% (pelvis and lumbar) of participants, were considered asymmetric for >90% of the protocol. Lateral postures displayed weak to low correlations with peak low back pain (R ≤ 0.388).

Conclusion: The majority of participants displayed lateral asymmetries for the pelvis and lumbar spine within 5° of their upright standing posture.

Application: In short-term sedentary exposures, associations between lateral postures and pain indicated that as the range in lateral postures increases there may be an increased possibility of pain.

Objective: The current study examined whether differences in the branding and description or mode of training materials influence drivers' understanding and expectations of a partial driving automation system.

Background: How technology is described might influence consumers' understanding and expectations, even if all information is accurate.

Method: Ninety drivers received training about a real partial driving automation system with a fictitious name. Participants were randomly assigned to a branding condition (system named AutonoDrive, training emphasized capabilities; or system named DriveAssist, training emphasized limitations) and training mode (quick-start brochure; video; or in-person demonstration). No safety-critical information was withheld nor deliberately misleading information provided. After training, participants drove a vehicle equipped with the system. Associations of drivers' expectations with branding condition and training mode were assessed using between-subjects comparisons of questionnaire responses obtained pre- and post-drive.

Results: Immediately after training, those who received information emphasizing the system's capabilities had greater expectations of the system's function and crash avoidance capability in a variety of driving scenarios, including many in which the system would not work, as well as greater willingness to utilize the system's workload reduction benefits to take more risks. Most but not all differences persisted after driving the vehicle. Expectations about collision avoidance differed by training mode pre-drive but not post-drive.

Conclusion: Training that emphasizes a partial driving automation system's capabilities and downplays its limitations can foster overconfidence.

Application: Accuracy of technical information does not guarantee understanding; training should provide a balanced view of a system's limitations as well as capabilities.