Human Factors最新文献

Objective: This study investigated the effects of mass and vertical center-of-mass position of combat items attached to a tactical vest, as well as marksmanship posture on rifle marksmanship performance, postural sway, and perceived workload during a simulated rifle shooting task.

Background: A tactical vest serves as a load carriage system in addition to providing body protection. Its design, particularly the mass and vertical position of attached combat items, may impact postural control during rifle shooting and thus marksmanship performance.

Method: Thirty-two participants performed a simulated rifle shooting task on a force plate with a tactical vest on. Three independent variables were considered: load mass (4 levels), vertical load center-of-mass position (4 levels), and marksmanship posture (2 levels). The dependent variables were: 6 rifle marksmanship performance measures, 7 postural sway measures, and a perceived workload measure.

Results: Heavier load mass significantly degraded rifle marksmanship performance, and increased postural sway and perceived workload. Marksmanship posture significantly affected rifle marksmanship performance and postural sway. The kneeling posture resulted in less postural sway and better marksmanship performance than the standing posture. Vertical load center-of-mass position affected only part of the marksmanship performance measures and did not affect the measures of postural sway and perceived workload.

Conclusion: Reducing combat item mass on tactical vests and enhancing soldier postural control ability would improve rifle marksmanship and soldier lethality.

Application: The study findings inform the development of future military tactical vests and rifle marksmanship training, highlighting the need for lightweight gear design and postural control training.

Objective: An up-to-date and thorough literature review is needed to identify factors that influence driver situation awareness (SA) during control transitions in conditionally automated vehicles (AV). This review also aims to ascertain SA components required for takeovers, aiding in the design and evaluation of human-vehicle interfaces (HVIs) and the selection of SA assessment methodologies.

Background: Conditionally AVs alleviate the need for continuous road monitoring by drivers yet necessitate their reengagement during control transitions. In these instances, driver SA is crucial for effective takeover decisions and subsequent actions. A comprehensive review of influential SA factors, SA components, and SA assessment methods will facilitate driving safety in conditionally AVs but is still lacking.

Method: A systematic literature review was conducted. Thirty-four empirical research articles were screened out to meet the criteria for inclusion and exclusion.

Results: A conceptual framework was developed, categorizing 23 influential SA factors into four clusters: task/system, situational, individual, and nondriving-related task factors. The analysis also encompasses an examination of pertinent SA components and corresponding HVI designs for specific takeover events, alongside an overview of SA assessment methods for conditionally AV takeovers.

Conclusion: The development of a conceptual framework outlining influential SA factors, the examination of SA components and their suitable design of presentation, and the review of SA assessment methods collectively contribute to enhancing driving safety in conditionally AVs.

Application: This review serves as a valuable resource, equipping researchers and practitioners with insights to guide their efforts in evaluating and enhancing driver SA during conditionally AV takeovers.

Objective: We investigated how people used cues to make Judgments of Difficulty (JODs) while observing automation perform a task and when performing this task themselves.

Background: Task difficulty is a factor affecting trust in automation; however, no research has explored how individuals make JODs when watching automation or whether these judgments are similar to or different from those made while watching humans. Lastly, it is unclear how cue use when observing automation differs as a function of experience.

Method: The study involved a visual search task. Some participants performed the task first, then watched automation complete it. Others watched and then performed, and a third group alternated between performing and watching. After each trial, participants made a JOD by indicating if the task was easier or harder than before. Task difficulty randomly changed every five trials.

Results: A Bayesian regression suggested that cue use is similar to and different from cue use while observing humans. For central cues, support for the UAH was bounded by experience: those who performed the task first underweighted central cues when making JODs, relative to their counterparts in a previous study involving humans. For peripheral cues, support for the MEH was unequivocal and participants weighted cues similarly across observation sources.

Conclusion: People weighted cues similar to and different from when they watched automation perform a task relative to when they watched humans, supporting the Media Equation and Unique Agent Hypotheses.

Application: This study adds to a growing understanding of judgments in human-human and human-automation interactions.

Objective: We investigated the impact of low-tempo, repetitive hand movements on vibrotactile sensitivity by employing various temporal and spatial patterns in the hand and wrist area.

Background: The investigation of a human's ability to perceive vibrotactile stimuli during dynamic hand movements remains understudied, despite the prevalence of slow to mild hand motions in applications such as hand navigation or gesture control using haptic gloves in Virtual Reality (VR) and Augmented Reality (AR).

Method: We investigated vibrotactile sensitivity, analyzing the impact of various factors, including Motion (static and low-tempo repetitive hand movements), Temporal Patterns (Single or Double vibrations with varying onset times), Tactor Placements (hand and wrist), Spatial Patterns, and Biological Sex.

Results: Our study revealed that Motion significantly influences vibrotactile sensitivity in the hand and wrist areas, leading to reduced accuracy rates during dynamic conditions. Additionally, as the stimulus onset approached in Double vibrations, accuracy rates markedly decreased. Notably, Hand Placement resulted in significantly higher accuracy rates compared to the Wrist Placement.

Conclusion: Our findings underscore the impact of motion in reducing vibrotactile sensitivity on the back of the hand and around the wrist.

Application: This research has wide-ranging practical applications, particularly in the field of VR/AR experiences, rehabilitation programs, and accessibility solutions through the use of haptic gloves. Insights from our study can be harnessed to enhance the efficacy of haptic gloves in conveying vibrotactile cues within these contexts.

Objective: We investigate the impact of event uncertainty, decision support (DS) display format, and DS sensitivity on participants' behavior, performance, subjective workload, and perception of DS usefulness and performance in a classification task.

Background: DS systems can positively and negatively affect decision accuracy, performance time, and workload. The ability to access DS selectively, based on informational needs, might improve DS effectiveness.

Method: Participants performed a sensory classification task in which they were allowed to request DS on a trial-by-trial basis. DS was presented in separated-binary (SB), separated-likelihood (SL), or integrated-likelihood (IL) formats. Access preferences, task performance, performance time, subjective workload, and perceived DS usefulness and performance were recorded.

Results: Participants accessed DS more often when it was highly sensitive, stimulus information was highly uncertain, or the DS cue and stimulus information were perceptually integrated. Effective sensitivity was highest with the integrated likelihood DS. Although the separated likelihood DS provided more information than the binary likelihood DS, it was accessed less often, leading to lower sensitivity.

Conclusion: Participants are most likely to access DS when raw stimulus information is highly uncertain and appear to make effective use of likelihood DS only when DS cues are integrated with raw stimulus information within a display.

Application: Results suggest that DS use will be most effective when likelihood DS cues and raw stimulus information are perceptually integrated. When DS cues and raw stimuli cannot be perceptually integrated, binary cues from the DS will be more effective than likelihood cues.

Objective: We investigated how different deceleration intentions (i.e. an automated vehicle either decelerated for leading traffic or yielded for pedestrians) and a novel (Slow Pulsing Light Band - SPLB) or familiar (Flashing Headlights - FH) external Human Machine Interface (eHMI) informed pedestrians' crossing behaviour.

Background: The introduction of SAE Level 4 Automated Vehicles (AVs) has recently fuelled interest in new forms of explicit communication via eHMIs, to improve the interaction between AVs and surrounding road users. Before implementing these eHMIs, it is necessary to understand how pedestrians use them to inform their crossing decisions.

Method: Thirty participants took part in the study using a Head-Mounted Display. The independent variables were deceleration intentions and eHMI design. The percentage of crossings, collision frequency and crossing initiation time across trials were measured.

Results: Pedestrians were able to identify the intentions of a decelerating vehicle, using implicit cues, with more crossings made when the approaching vehicles were yielding to them. They were also more likely to cross when a familiar eHMI was presented, compared to a novel one or no eHMI, regardless of the vehicle's intention. Finally, participants learned to take a more cautious approach as trials progressed, and not to base their decisions solely on the eHMI.

Conclusion: A familiar eHMI led to early crossings regardless of the vehicle's intention but also led to a higher collision frequency than a novel eHMI.

Application: To achieve safe and acceptable interactions with AVs, it is important to provide eHMIs that are congruent with road users' expectations.

Objective: To examine the impact of secondary task performance on contextual blindness arising from the suppression and masking of temporal and spatial sequence learning.

Background: Dual-task scenarios can lead to a diminished ability to use environmental cues to guide attention, a phenomenon that is related to multitasking-induced inattentional blindness. This research aims to extend the theoretical understanding of how secondary tasks can impair attention and memory processes in sequence learning and access.

Method: We conducted three experiments. In Experiment 1, we used a serial reaction time task to investigate the impact of a secondary tone counting task on temporal sequence learning. In Experiment 2, we used a contextual cueing task to examine the effects of dual-task performance on spatial cueing. In Experiment 3, we integrated and extended these concepts to a simulated driving task.

Results: Across the experiments, the performance of a secondary task consistently suppressed (all experiments) and masked task learning (experiments 1 and 3). In the serial response and spatial search tasks, dual-task conditions reduced the accrual of sequence knowledge and impaired knowledge expression. In the driving simulation, similar patterns of learning suppression from multitasking were also observed.

Conclusion: The findings suggest that secondary tasks can significantly suppress and mask sequence learning in complex tasks, leading to a form of contextual blindness characterized by impairments in the ability to use environmental cues to guide attention and anticipate future events.

Application: These findings have implications for both skill acquisition and skilled performance in complex domains such as driving, aviation, manufacturing, and human-computer interaction.

Objective: The goal of this study was to assess how different real-time gaze sharing visualization techniques affect eye tracking metrics, workload, team situation awareness (TSA), and team performance.

Background: Gaze sharing is a real-time visualization technique that allows teams to know where their team members are looking on a shared display. Gaze sharing visualization techniques are a promising means to improve collaborative performance on simple tasks; however, there needs to be validation of gaze sharing with more complex and dynamic tasks.

Methods: This study evaluated the effect of gaze sharing on eye tracking metrics, workload, team SA, and team performance in a simulated unmanned aerial vehicle (UAV) command-and-control task. Thirty-five teams of two performed UAV tasks under three conditions: one with no gaze sharing and two with gaze sharing. Gaze sharing was presented using a fixation dot (i.e., a translucent colored dot) and a fixation trail (i.e., a trail of the most recent fixations).

Results: The results showed that the fixation trail significantly reduced saccadic activity, lowered workload, supported team SA at all levels, and improved performance compared to no gaze sharing; however, the fixation dot had the opposite effect on performance and SA. In fact, having no gaze sharing outperformed the fixation dot. Participants also preferred the fixation trail for its visibility and ability to track and monitor the history of their partner's gaze.

Conclusion: The results showed that gaze sharing has the potential to support collaboration, but its effectiveness depends highly on the design and context of use.

Application: The findings suggest that gaze sharing visualization techniques, like the fixation trail, have the potential to improve teamwork in complex UAV tasks and could have broader applicability in a variety of collaborative settings.

Objective: Physical and cognitive workloads and performance were studied for a corrective shared control (CSC) human-robot collaborative (HRC) sanding task.

Background: Manual sanding is physically demanding. Collaborative robots (cobots) can potentially reduce physical stress, but fully autonomous implementation has been particularly challenging due to skill, task variability, and robot limitations. CSC is an HRC method where the robot operates semi-autonomously while the human provides real-time corrections.

Methods: Twenty laboratory participants removed paint using an orbital sander, both manually and with a CSC robot. A fully automated robot was also tested.

Results: The CSC robot improved subjective discomfort compared to manual sanding in the upper arm by 29.5%, lower arm by 32%, hand by 36.5%, front of the shoulder by 24%, and back of the shoulder by 17.5%. Muscle fatigue measured using EMG, was observed in the medial deltoid and flexor carpi radialis for the manual condition. The composite cognitive workload on the NASA-TLX increased by 14.3% for manual sanding due to high physical demand and effort, while mental demand was 14% greater for the CSC robot. Digital imaging showed that the CSC robot outperformed the automated condition by 7.16% for uniformity, 4.96% for quantity, and 6.06% in total.

Conclusions: In this example, we found that human skills and techniques were integral to sanding and can be successfully incorporated into HRC systems. Humans performed the task using the CSC robot with less fatigue and discomfort.

Applications: The results can influence implementation of future HRC systems in manufacturing environments.

Objective: To examine operator state variables (workload, fatigue, trust in automation, task engagement) that potentially predict return-to-manual (RTM) performance after automation fails to complete a task action.

Background: Limited research has examined the extent to which within-person variability in operator states predicts RTM performance, a prerequisite to adapting work systems based on expected performance degradation/operator strain. We examine whether operator states differentially predict RTM performance as a function of degree of automation (DOA).

Method: Participants completed a simulated air traffic control task. Conflict detection was assisted by either a higher- or lower-DOA. When automation failed to resolve a conflict, participants needed to prevent that conflict (i.e., RTM). Participants' self-reported workload, fatigue, trust in automation, and task engagement were periodically measured.

Results: Participants using lower DOA were faster to resolve conflicts (RTM RT) missed by automation than those using higher DOA. DOA did not moderate the relationship between operator states and RTM performance. Collapsed across DOA, increased workload (relative to participants' own average) and increased fatigue (relative to sample average, or relative to own average) led to the resolution of fewer conflicts missed by automation (poorer RTM accuracy). Participants with higher trust (relative to own average) had higher RTM accuracy.

Conclusions: Variation in operator state measures of workload, fatigue, and trust can predict RTM performance. However, given some identified inconsistency in which states are predictive across studies, further research is needed.

Applications: Adaptive work systems could be designed to respond to vulnerable operator states to minimise RTM performance decrements.