Human Factors最新文献

Objective: This study evaluated whether pedestrians can use augmented reality (AR) overlays to guide their road-crossing decisions when crossing two lanes of opposing traffic.

Background: Emerging technologies for enhancing traffic safety often focus on alerting drivers to hazards. Less attention has been given to understanding how pedestrians respond to technology designed to aid their road-crossing decisions, particularly in more complex traffic.

Method: Participants repeatedly crossed two lanes of opposing traffic displayed in a virtual reality system. Participants in the AR condition viewed matching-colored bars (AR overlays) suspended just above the gaps between cars where there was sufficient time to safely cross a pair of near and far lane gaps. Participants in the control condition performed the same road-crossing task but saw no AR overlays.

Results: Participants who viewed AR cues were more likely than participants who did not view AR cues to accept gap pairs classified as crossable and less likely to accept gap pairs classified as uncrossable. However, there was no difference between the AR and control conditions in time to spare when exiting the roadway. NASA Task Load Index (2020) responses indicated that perceived performance was higher and perceived frustration was lower in the AR than control condition, but perceived workload was higher in the AR condition.

Conclusion: The AR cues helped participants identify crossable gap pairs but did not lead to greater time to spare when exiting the roadway.

Application: These results show both the promise and risks of assistive technologies designed to increase pedestrian safety in more complex traffic situations.

Objective: This study aims to investigate the ability of auditory cues for predicting motion and lead times to mitigate visually induced motion sickness (VIMS).

Background: The vehicle information systems predominantly utilize visual displays, which can introduce conflicts between visual and vestibular motion cues, potentially resulting in VIMS. In these scenarios, auditory cues may provide a viable solution, especially when visual cues are diminished by fatigue or distractions.

Methods: In two distinct studies, a total of 180 participants were involved in investigating the impact of auditory cues on VIMS. In Study 1, participants were categorized based on the type of auditory cue they received (speech, nonspeech, or no-cue). Study 2 examined the effects of three different lead times (1 s, 2 s, and 3 s) between the activation of the auditory cue and the occurrence of car braking or turning in nonspeech conditions. VIMS severity was assessed with the Simulator Sickness Questionnaire (SSQ) before and after the simulation phase.

Results: Nonspeech cues significantly reduced VIMS compared to speech or no-cue. VIMS was notably lower with a 2 s lead time than with 1 s or 3 s lead times, and females reported higher levels of VIMS than males.

Conclusion: Results across two studies suggest using nonspeech cues with a 2-second lead time to reduce VIMS. It also recommends investigating the effects of duration, tone, and voice frequency. Furthermore, the study proposes extensive research into lead time settings for various scenarios such as driving fatigue, hillside roads, and traffic congestion.

Application: These findings offer potential value in designing auditory cues to reduce VIMS in autonomous driving, simulators, VR games, and films.

Objective: To study the performance consequences of binary versus likelihood decision support systems in low-prevalence visual search.

Background: Hit rates in visual search are often low if target prevalence is low, an issue that is relevant for numerous real-world visual search tasks (e.g., luggage screening and medical imaging). Given that binary decision support systems produce many false alarms at low prevalence, they have often been discounted as a solution to this low-prevalence problem. By offering additional information about the certainty of target-present indications through splitting these into warnings and alarms, likelihood-based systems could potentially boost hit rates without raising the number of false alarms.

Method: We used a simulated medical search task with low target prevalence in a paradigm where participants sequentially uncovered parts of the stimulus with their mouse. In two sessions, participants completed the task either while being supported by a binary or a likelihood system.

Results: Hit rates were higher when interacting with the likelihood systems than with the binary system, at no cost of higher false alarms.

Conclusion: Likelihood systems are a promising way to tackle the low-prevalence problem, and might further be an effective means to make systems more transparent.

Application: Simple-to-process information about system certainty for each case might be a solution to low hit rates in domains with low target prevalence, such as radiology.

Objective: The impact of the context in which automation is introduced to a decision-making system was analyzed theoretically and empirically.

Background: Previous work dealt with causality and responsibility in human-automation systems without considering the effects of how the automation's role is presented to users.

Methods: An existing analytical model for predicting the human contribution to outcomes was adapted to accommodate the context of automation. An aided signal detection experiment with 400 participants was conducted to assess the correspondence of observed behavior to model predictions.

Results: The context in which the automation's role is presented affected users' tendency to follow its advice. When automation made decisions, and users only supervised it, they tended to contribute less to the outcome than in systems where the automation had an advisory capacity. The adapted theoretical model for human contribution was generally aligned with participants' behavior.

Conclusion: The specific way automation is integrated into a system affects its use and the perceptions of user involvement, possibly altering overall system performance.

Application: The research can help design systems with automation-assisted decision-making and provide information on regulatory requirements and operational processes for such systems.

Objective: To better understand automation transparency, we experimentally isolated the effects of additional information and decision recommendations on decision accuracy, decision time, perceived workload, trust, and system usability.

Background: The benefits of automation transparency are well documented. Previously, however, transparency (in the form of additional information) has been coupled with the provision of decision recommendations, potentially decreasing decision-maker agency and promoting automation bias. It may instead be more beneficial to provide additional information without decision recommendations to inform operators' unaided decision making.

Methods: Participants selected the optimal uninhabited vehicle (UV) to complete missions. Additional display information and decision recommendations were provided but were not always accurate. The level of additional information (no, medium, high) was manipulated between-subjects, and the provision of recommendations (absent, present) within-subjects.

Results: When decision recommendations were provided, participants made more accurate and faster decisions, and rated the UV system as more usable. However, recommendation provision reduced participants' ability to discriminate UV system information accuracy. Increased additional information led to faster decisions, lower perceived workload, and higher trust and usability ratings but only significantly improved decision (UV selection) accuracy when recommendations were provided.

Conclusion: Individuals scrutinized additional information more when not provided decision recommendations, potentially indicating a higher expected value of processing that information. However, additional information only improved performance when accompanied by recommendations to support decisions.

Application: It is critical to understand the potential differential impact of, and interaction between, additional display information and decision recommendations to design effective transparent automated systems in the modern workplace.

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: Four web-based experiments investigated flexibility of disembodiment of a virtual object that is no longer actively controlled. Emphasis was on possibilities to modify the timescale of this process.

Background: Interactions with virtual objects are commonplace in settings like teleoperation, rehabilitation, and computer-aided design. These objects are quickly integrated into the operator's body schema (embodiment). Less is known about how long such embodiment lasts. Understanding the dynamics of this process is crucial because different applied settings either profit from fast or slow disembodiment.

Method: To induce embodiment, participants moved a 2D virtual hand through operating a computer mouse or touchpad. After initial embodiment, participants either stopped or continued moving for a fixed period of time. Embodiment ratings were collected continuously during each trial.

Results: Results across all experiments indicated that embodiment for the virtual hand gradually increased during active use and gradually decreased after stopping to use it. Disembodiment unfolded nearly twice as fast as embodiment and showed a curved decay pattern. These dynamics remained unaffected by anticipation of active control that would be required in an upcoming task.

Conclusion: The results highlight the importance of continuously experiencing active control in virtual interactions if aiming at inducing stable embodiment of a virtual object.

Application: Our findings suggest that applications of virtual disembodiment such as virtual tools or interventions to affect a person's body representation critically depend on continuous updating of sensorimotor experience. However, if switching between virtual objects, for example, during teleoperation or video gaming, after-effects are unlikely to affect performance.

Objective: This work examined the relationship of the constructs measured by the trust scales developed by Chancey et al. (2017) and Jian et al. (2000) using a multilevel confirmatory factor analysis (CFA).

Background: Modern theories of automation trust have been proposed based on data collected using trust scales. Chancey et al. (2017) adapted Madsen and Gregor's (2000) trust scale to align with Lee and See's (2004) human-automation trust framework. In contrast, Jian et al. (2000) developed a scale empirically with trust and distrust as factors. However, it remains unclear whether these two scales measure the same construct.

Method: We analyzed data collected from previous experiments to investigate the relationship between the two trust scales using a multilevel CFA.

Results: Data provided evidence that Jian et al. (2000) and Chancey et al. (2017) automation trust scales are only weakly related. Trust and distrust are found to be distinct factors in Jian et al.'s (2000) scale, whereas performance, process, and purpose are distinct factors in Chancey et al.'s (2017) trust scale.

Conclusion: The analysis suggested that the two scales purporting to measure human-automation trust are only weakly related.

Application: Trust researchers and automation designers may consider using Chancey et al. (2017) and Jian et al. (2000) scales to capture different characteristics of human-automation trust.

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: This review surveys the literature on sensorimotor challenges impacting performance in laparoscopic minimally invasive surgery (MIS).

Background: Despite its well-known benefits for patients, achieving proficiency in MIS can be challenging for surgeons due to many factors including altered visual perspectives and fulcrum effects in instrument handling. Research on these and other sensorimotor challenges has been hindered by imprecise terminology and the lack of a unified theoretical framework to guide research questions in the field.

Method: We conducted a systematic survey of the MIS literature, focusing on studies investigating sensorimotor challenges affecting laparoscopic performance. To provide a common foundation for cross-study comparisons, we propose a standardized taxonomy that distinguishes between different experimental paradigms used in the literature. We then show how the computational motor learning perspective provides a unifying theoretical framework for the field that can facilitate progress and motivate future research along clearer, hypothesis-driven lines.

Results: The survey identified diverse sensorimotor perturbations in MIS, which can be effectively categorized according to our proposed taxonomy. Studies investigating monitor-, camera-, and tool-based perturbations were systematically analyzed, elucidating their impact on surgical performance. We also show how the computational motor learning perspective provides deeper insights and potential strategies to mitigate challenges.

Conclusion: Sensorimotor challenges significantly impact MIS, necessitating a systematic, empirically informed approach. Our proposed taxonomy and theoretical framework shed light on the complexities involved, paving the way for more structured research and targeted training approaches to enhance surgical proficiency.

Application: Understanding the sensorimotor challenges inherent to MIS can guide the design of improved training curricula and inform the configuration of setups in the operating room to enhance surgeon performance and ultimately patient outcomes. This review offers key insights for surgeons, educators, and researchers in surgical performance and technology development.