Human Factors最新文献

Objective: To explore and validate effective eye movement features related to motion sickness (MS) through closed-track experiments and to provide valuable insights for practical applications.

Background: With the development of autonomous vehicles (AVs), MS has attracted more and more attention. Eye movements have great potential to evaluate the severity of MS as an objective quantitative indicator of vestibular function. Eye movement signals can be easily and noninvasively collected using a camera, which will not cause discomfort or disturbance to passengers, thus making it highly applicable.

Method: Eye movement data were collected from 72 participants susceptible to MS in closed-track driving environments. We extracted features including blink rate (BR), total number of fixations (TNF), total duration of fixations (TDF), mean duration of fixations (MDF), saccade amplitude (SA), saccade duration (SD), and number of nystagmus (NN). The statistical method and multivariate long short-term memory fully convolutional network (MLSTM-FCN) were used to validate the effectiveness of eye movement features.

Results: Significant differences were shown in the extracted eye movement features across different levels of MS through statistical analysis. The MLSTM-FCN model achieved an accuracy of 91.37% for MS detection and 88.51% for prediction in binary classification. For ternary classification, it achieved an accuracy of 80.54% for MS detection and 80.11% for prediction.

Conclusion: Evaluating MS through eye movements is effective. The MLSTM-FCN model based on eye movements can efficiently detect and predict MS.

Application: This work can be used to provide a possible indication and early warning for MS.

Objective: This study aims to analyze the effect of correlated color temperature from LED glare sources on driving performance. The evaluation includes assessing the effect of disability glare on visual reaction time and rating discomfort glare using a standardized scale.

Background: LED technology is widely incorporated into various lighting systems; however, the impact of glare from oncoming car headlamps on driving performance at night-time is crucial for road safety.

Method: Twenty-three healthy young subjects participated in a laboratory-based experiment simulating night driving using a two-channel Maxwellian view optical system. Two LED lamps with correlated color temperature of 2800 K and 6500 K were used to generate a glare of 52 lx. Disability glare was quantified in terms of foveal reaction time and discomfort glare was rated using the de Boer scale.

Results: The results show that glare-induced effect is mitigated by an increase in background luminance. The correlated color temperature of the LED lamp does not affect either reaction time or discomfort glare rating.

Conclusion: The greater short-wavelength emission of 6500 K lamp does not intensify the effect of disability or discomfort glare, probably due to the macular pigment absorption on foveal vision and the transparency of ocular media, coupled with the involvement of other contributing factors. The correlated color temperature of the lamp is not the best descriptive parameter to identify its effect on glare.

Application: It is important to consider the impact of LED technology on visual performance to enhance road safety in critical glare situations during night driving.

Objective: To gather the existing evidence on the impact of sit-stand desk-based interventions on working-time and full-day sedentary behavior and compare their impact across different intervention lengths.

Background: Reducing sedentary behavior is vital for improving office workers' health. Sit-stand desks promote sitting and standing alternation, but understanding their effects outside the workplace is essential for success.

Methods: Studies published between January 2008 and January 2024 were searched through electronic databases (PubMed, Google Scholar, and Cochrane Library). The quality of the studies was assessed using the Quality Assessment Tool for Quantitative Studies of the Effective Public Health Practice Project.

Results: Twelve included studies showed that the intervention group experienced average reductions in full-day sedentary behavior of 68.7 min/day at 3 months, 77.7 min/day at 6 months, and 62.1 min/day at 12 months compared to the control group. For working hours sedentary behavior, reductions were observed in the intervention group at 9 weeks (73.0 min/day), 3 months (88.0 min/day), 6 months (80.8 min/day), and 12 months (48.0 min/day) relative to the control group.

Conclusions: Sit-stand desk interventions can be effective in helping office workers reduce sedentary behavior in the short, medium, and long-term both at work and throughout the full-day.

Application: Active workstation interventions, including sit-stand desks, educational sessions, and alert software, aim to reduce sedentary behavior among office workers. While sit-stand desks show promise in decreasing sitting time during working hours, their long-term effectiveness and impact beyond the workplace remain uncertain. This review evaluates their effectiveness across different durations, addressing both workplace and full-day impact.

Objective: This study investigated the effects of nondriving-related task (NDRT) touchscreen location and NDRT difficulty level on the driver task performance, eye gaze behavior, and workload during SAE Level 3 conditionally automated driving. Two driver tasks were considered: a visuomanual NDRT and a take-over task.

Background: Touchscreens are expected to play important roles inside automated vehicles. However, few studies have investigated the driver-touchscreen interaction during automated driving.

Method: A driving simulator experiment was conducted. The experimental task consisted of two successive subtasks: an NDRT followed by a take-over task. NDRT touchscreen location (Upper Left, Upper Right, and Lower Right) and NDRT difficulty level (Easy and Hard) were the independent variables. A set of driver task performance, eye gaze behavior, and perceived workload measures were employed for each subtask as the dependent variables.

Results: NDRT touchscreen location significantly affected both the NDRT and the take-over task performance. Lower Right was superior to Upper Right in the NDRT performance but was inferior in the take-over task performance. NDRT touchscreen location affected the perceived physical workload of the NDRT. NDRT difficulty level affected the perceived workload of the take-over task.

Conclusion: The research findings enhance our understanding of how NDRT touchscreen location and NDRT difficulty level impact the driver task performance during conditionally automated driving, and, further provide useful design implications and knowledge.

Application: The study results would inform the NDRT touchscreen interface design and the NDRT design for conditionally automated vehicles.

Objective: The primary purpose was to determine how trust changes over time when automation reliability increases or decreases. A secondary purpose was to determine how task-specific self-confidence is associated with trust and reliability level.

Background: Both overtrust and undertrust can be detrimental to system performance; therefore, the temporal dynamics of trust with changing reliability level need to be explored.

Method: Two experiments used a dominant-color identification task, where automation provided a recommendation to users, with the reliability of the recommendation changing over 300 trials. In Experiment 1, two groups of participants interacted with the system: one group started with a 50% reliable system which increased to 100%, while the other used a system that decreased from 100% to 50%. Experiment 2 included a group where automation reliability increased from 70% to 100%.

Results: Trust was initially high in the decreasing group and then declined as reliability level decreased; however, trust also declined in the 50% increasing reliability group. Furthermore, when user self-confidence increased, automation reliability had a greater influence on trust. In Experiment 2, the 70% increasing reliability group showed increased trust in the system.

Conclusion: Trust does not always track the reliability of automated systems; in particular, it is difficult for trust to recover once the user has interacted with a low reliability system.

Applications: This study provides initial evidence into the dynamics of trust for automation that gets better over time suggesting that users should only start interacting with automation when it is sufficiently reliable.

Objective: With the rapid improvements in drone technology, there is an increasing interest in distal pointing to diffuse drones. This study investigated the effect of depth on distal pointing when the hand does not traverse the entire distance from start to target so that the most suitable mathematical model can be assessed.

Background: Starting from the Fitts paradigm, researchers have proposed different models to predict movement time when the distance to the target is variable. They do consider distance, but they are based on statistical modeling rather than the underlying control mechanisms.

Methods: Twenty-four participants volunteered for an experiment in a full-factorial Fitts' paradigm task (3 levels of movement amplitude *7 levels of target width *3 levels of distance from participant to screen). Movement time and the number of errors were the dependent variables.

Results: Depth has a significant effect when the target width is small, but depth has no effect when the target width is large. The angular version of the two-part model is superior to the one-part Fitts' model at larger distances. Besides, Index of difficulty for distal pointing, ${ID}_{\text{DP}}$ with adjustable k achieves the best fit even though the model is very sensitive to the value of k and the complexity of the model could be resulting in an overfitting. The result implies that the effects of movement amplitude and target width are not comparable and grouping them to form a dependent index of difficulty can be misleading especially when distance is an added variable.

Conclusion: The angular version of the two-part model is a viable and meaningful description for distal pointing. Even though the ${ID}_{\text{DP}}$ with adjustable k is the best predictor for movement time when depth is an added variable, there is no physical interpretation for it.

Application: A reasonable predictive model for performance assessments and predictions in distal pointing.

Objective: This study investigated the effects of different approach directions, movement speeds, and trajectories of a co-robot's end-effector on workers' mental stress during handover tasks.

Background: Human-robot collaboration (HRC) is gaining attention in industry and academia. Understanding robot-related factors causing mental stress is crucial for designing collaborative tasks that minimize workers' stress.

Methods: Mental stress in HRC tasks was measured subjectively through self-reports and objectively through galvanic skin response (GSR) and electromyography (EMG). Robot-related factors including approach direction, movement speed, and trajectory were analyzed.

Results: Movement speed and approach direction had significant effects on subjective ratings, EMG, and GSR. High-speed and approaching from one side consistently resulted in higher fear, lower comfort, and predictability, as well as increased EMG and GSR signals, indicating higher mental stress. Movement trajectory affected GSR, with the sudden stop condition eliciting a stronger response compared to the constrained trajectory. Interaction effects between speed and approach direction were observed for "surprise" and "predictability" subjective ratings. At high speed, approach direction did not significantly differ, but at low speeds, approaching from the side was found to be more surprising and unpredictable compared to approaching from the front.

Conclusion: The mental stress of workers during HRC is lower when the robot's end effector (1) approaches a worker within the worker's field of view, (2) approaches at a lower speed, or (3) follows a constrained trajectory.

Application: The outcome of this study can serve as a guide to design HRC tasks with a low level of workers' mental stress.

Objective: The study aimed to enhance transparency in autonomous systems by automatically generating and visualizing confidence and explanations and assessing their impacts on performance, trust, preference, and eye-tracking behaviors in human-automation interaction.

Background: System transparency is vital to maintaining appropriate levels of trust and mission success. Previous studies presented mixed results regarding the impact of displaying likelihood information and explanations, and often relied on hand-created information, limiting scalability and failing to address real-world dynamics.

Method: We conducted a dual-task experiment involving 42 university students who operated a simulated surveillance testbed with assistance from intelligent detectors. The study used a 2 (confidence visualization: yes vs. no) × 3 (visual explanations: none, bounding boxes, bounding boxes and keypoints) mixed design. Task performance, human trust, preference for intelligent detectors, and eye-tracking behaviors were evaluated.

Results: Visual explanations using bounding boxes and keypoints improved detection task performance when confidence was not displayed. Meanwhile, visual explanations enhanced trust and preference for the intelligent detector, regardless of the explanation type. Confidence visualization did not influence human trust in and preference for the intelligent detector. Moreover, both visual information slowed saccade velocities.

Conclusion: The study demonstrated that visual explanations could improve performance, trust, and preference in human-automation interaction without confidence visualization partially by changing the search strategies. However, excessive information might cause adverse effects.

Application: These findings provide guidance for the design of transparent automation, emphasizing the importance of context-appropriate and user-centered explanations to foster effective human-machine collaboration.

Objectives: This study aimed to investigate drivers' disengagement from nondriving related tasks (NDRT) during scheduled takeovers and to evaluate its impact on takeover performance.

Background: During scheduled takeovers, drivers typically have sufficient time to prepare. However, inadequate disengagement from NDRTs can introduce safety risks.

Method: Participants experienced scheduled takeovers using a driving simulator, undergoing two conditions, with and without an NDRT. We assessed their takeover performance and monitored their NDRT disengagement from visual, cognitive, and physical perspectives.

Results: The study examined three NDRT disengagement timings (DTs): DT1 (disengaged before the takeover request), DT2 (disengaged after the request but before taking over), and DT3 (not disengaged). The impact of NDRT on takeover performance varied depending on DTs. Specifically, DT1 demonstrated no adverse effects; DT2 impaired takeover time, while DT3 impaired both takeover time and quality. Additionally, participants who displayed DT1 exhibited longer eye-off-NDRT duration and a higher eye-off-NDRT count during the prewarning stage compared to those with DT2 and DT3.

Conclusion: Drivers can benefit from earlier disengagement from NDRTs, demonstrating resilience to the adverse effects of NDRTs on takeover performance. The disengagement of cognition is often delayed compared to that of eyes and hands, potentially leading to DT3. Moreover, visual disengagement from NDRTs during the prewarning stage could distinguish DT1 from the other two.

Application: Our study emphasizes considering NDRT disengagement in designing systems for scheduled takeovers. Measures should be taken to promote early disengagement, facilitate cognitive disengagement, and employ visual disengagement during the prewarning period as predictive indicators of DTs.

Objective: To determine whether (i) low back loads and/or (ii) kinematic coordination patterns differed across theoretical expert, contextual expert and novice groups when completing both generic and occupation-specific lifts.

Background: Experience has been proposed as a factor that could reduce biomechanical exposures in lifting, but the literature reports mixed effects. The inconsistent relationship between experience and exposures may be partially attributable to the broad classification of experience and experimental lifting protocols not replicating the environment where experience was gained.

Methods: Purposive sampling was used to recruit 72 participants including theoretical experts (formal training on lifting mechanics), contextual experts (paramedics), and novices. Participants performed 10 barbell and crate (generic) lifts, as well as backboard and stretcher (occupation-specific) lifts while whole-body kinematics and ground reaction forces were collected. Peak low back compression and anteroposterior shear loads normalized to body mass, as well as kinematic coordination patterns, were calculated as dependent variables.

Results: No significant differences in low back loads were observed across expertise groups. However, significant differences were seen in kinematic coordination patterns across expertise groups in occupation-specific lifts, but not in generic lifts.

Conclusion: Increasing expertise is unlikely to minimize low back loads in lifting. However, contextual expertise did influence lifting kinematics, but only when performing occupationally specific lifts.

Application: Contextual expertise may help lifters adopt lifting kinematics that enhance the tolerance of their musculoskeletal system to withstand applied loads, but does not seem to reduce the applied low back loads relative to noncontextual expert groups.