Human Factors最新文献

Objective: To examine the effects of asymmetry and lower extremity mobility restrictions on the effectiveness of a passive back-support exosuit in short-duration, static trunk flexion postures.

Background: The effectiveness of trunk exoskeletons/suits for sagittally symmetric trunk posture maintenance has been investigated, but there has been limited study of the effects of asymmetric trunk postures or lower extremity motion restriction.

Method: Sixteen participants held trunk flexion postures involving trunk flexion (20°, 40°, 60°), asymmetry (0°, 30°), and lower extremity mobility (Free, Restricted) for 3 s. Participants held these postures with and without an exosuit while erector spinae and abdominal muscle activities were collected.

Results: There were no significant interactions between exosuit and asymmetry or exosuit and lower extremity motion restrictions, indicating no significant effects of these factors on the effectiveness of the exosuit at reducing trunk muscle activity. The exosuit was found to be effective at reducing erector spinae muscle activity regardless of asymmetry of posture or lower extremity restrictions (average 21%, from 11.2% MVC to 8.8% MVC). The magnitude of the erector spinae activity at 60° of trunk flexion with the exosuit was similar to that seen at 20° without the exosuit.

Conclusion: The exosuit consistently provided biomechanical benefit through reduced activation of the erector spinae muscles and neither asymmetry of trunk posture nor lower extremity restriction influenced this effectiveness.

Application: Trunk exoskeletons/suits can reduce trunk muscle activation and understanding how characteristics of the trunk postures assumed impact these responses may help target tasks wherein these devices may be effective.

Objective: We investigated the extent to which a voluntary-use range and bearing line (RBL) tool improves return-to-manual performance when supervising high-degree conflict detection automation in simulated air traffic control.

Background: High-degree automation typically benefits routine performance and reduces workload, but can degrade return-to-manual performance if automation fails. We reasoned that providing a voluntary checking tool (RBL) would support automation failure detection, but also that automation induced complacency could extend to nonoptimal use of such tools.

Method: Participants were assigned to one of three conditions, where conflict detection was either performed: manually, with RBLs available to use (Manual + RBL), automatically with RBLs (Auto + RBL), or automatically without RBLs (Auto). Voluntary-use RBLs allowed participants to reliably check aircraft conflict status. Automation failed once.

Results: RBLs improved automation failure detection - with participants intervening faster and making fewer false alarms when provided RBLs compared to not (Auto + RBL vs Auto). However, a cost of high-degree automation remained, with participants slower to intervene to the automation failure than to an identical manual conflict event (Auto + RBL vs Manual + RBL). There was no difference in RBL engagement time between Auto + RBL and Manual + RBL conditions, suggesting participants noticed the conflict event at the same time.

Conclusions: The cost of automation may have arisen from participants' reconciling which information to trust: the automation (which indicated no conflict and had been perfectly reliable prior to failing) or the RBL (which indicated a conflict).

Applications: Providing a mechanism for checking the validity of high-degree automation may facilitate human supervision of automation.

Objective: To examine the effects of navigation controls and field-of-view modes on cybersickness severity and gait dynamics after cessation of exposure to a virtual environment (VE).

Background: The applications of virtual reality are increasing in various fields; however, whether changes in interaction techniques and visual contents could mitigate the potential gait disturbance following VE exposure remains unclear.

Method: Thirty healthy adults wore a head-mounted display to complete six sessions of 12-min run-and-gun tasks using different navigation controls (gamepad, head, natural) and field-of-view modes (full, restricted). Forward and backward walking tasks were performed before and after VE exposure. The degrees of cybersickness and presence were evaluated using questionnaires, along with the in-session task performance. Spatiotemporal gait measures and their variabilities were calculated for each walking task.

Results: The participants experienced less cybersickness with the head and natural controls than with the gamepad. Natural control, based on matching body movements, was associated with the highest degree of presence and best performance. VE navigation using the gamepad showed reduced cadences and increased stride times during postexposure forward-walking tasks. When the VE was presented via the restricted field-of-view mode, increased gait variabilities were observed from backward-walking tasks after VE exposure.

Conclusion: Body movement-based navigation controls may alleviate cybersickness. We observed gait adaptation during both ambulation tasks, which was influenced by the navigation control method and field-of-view mode.

Application: This study provides the first evidence for gait adaptation during balance-demanding tasks after VE exposure, which is valuable for designing guidelines for virtual reality interactions.

Objective: We manipulate the presence, skill, and display of artificial intelligence (AI) recommendations in a strategy game to measure their effect on users' performance.

Background: Many applications of AI require humans and AI agents to make decisions collaboratively. Success depends on how appropriately humans rely on the AI agent. We demonstrate an evaluation method for a platform that uses neural network agents of varying skill levels for the simple strategic game of Connect Four.

Methods: We report results from a 2 × 3 between-subjects factorial experiment that varies the format of AI recommendations (categorical or probabilistic) and the AI agent's amount of training (low, medium, or high). On each round of 10 games, participants proposed a move, saw the AI agent's recommendations, and then moved.

Results: Participants' performance improved with a highly skilled agent, but quickly plateaued, as they relied uncritically on the agent. Participants relied too little on lower skilled agents. The display format had no effect on users' skill or choices.

Conclusions: The value of these AI agents depended on their skill level and users' ability to extract lessons from their advice.

Application: Organizations employing AI decision support systems must consider behavioral aspects of the human-agent team. We demonstrate an approach to evaluating competing designs and assessing their performance.

Objective: This study investigated the effect of auditory working memory task on situation awareness (SA) and eye-movement patterns in complex dynamic environments.

Background: Many human errors in aviation are caused by a lack of SA, and distraction from auditory secondary tasks is a serious threat to SA. However, it remains unclear how auditory working memory tasks affect SA and eye-movement patterns.

Method: Participants (n = 28) were randomly allocated to two groups and received different periods of visual search training (short versus long). They subsequently completed a situation awareness measurement task in three auditory secondary task conditions (without secondary task, auditory calculation task, and auditory 2-back task). Eye-movement data were collected during the situation awareness measurement task.

Results: The auditory 2-back task significantly reduced overall SA, Level 1 SA, dwell times, and total percentage of fixation time on task-related areas of interests in the SA measurement task. Overall SA and Level 3 SA were not reduced by the auditory 2-back task in individuals in the longer visual search training time condition.

Conclusion: Auditory working memory load impairs SA in the perception and projection stage; however, greater experience can overcome impairment of SA in the projection stage.

Application: This study provided possible approaches to preventing loss of SA: (1) improving crew members' communication skills to ensure the accurate and clear transmission of information, reducing the difficulty of processing information, and (2) providing targeted cognitive training tailored to each pilot's level of experience.

Objective: We created and validated a scale to measure perceptions of system trustworthiness.

Background: Several scales exist in the literature that attempt to assess trustworthiness of system referents. However, existing measures suffer from limitations in their development and validation. The current study sought to develop a scale based on theory and methodological rigor.

Method: We conducted exploratory and confirmatory factor analyses on data from two online studies to develop the System Trustworthiness Scale (STS). Additional analyses explored the manipulation of the factors and assessed convergent and divergent validity.

Results: The exploratory factor analyses resulted in a three-factor solution that represented the theoretical constructs of trustworthiness: performance, purpose, and process. Confirmatory factor analyses confirmed the three-factor solution. In addition, correlation and regression analyses demonstrated the scale's divergent and predictive validity.

Conclusion: The STS is a psychometrically valid and predictive scale for assessing trustworthiness perceptions of system referents.

Applications: The STS assesses trustworthiness perceptions of systems. Importantly, the scale differentiates performance, purpose, and process constructs and is adaptable to a variety of system referents.

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: We review the current state-of-the-art in team cognition research, but more importantly describe the limitations of existing theories, laboratory paradigms, and measures considering the increasing complexities of modern teams and the study of team cognition.

Background: Research on, and applications of, team cognition has led to theories, data, and measures over the last several decades.

Method: This article is based on research questions generated in a spring 2022 seminar on team cognition at Arizona State University led by the first author.

Results: Future research directions are proposed for extending the conceptualization of teams and team cognition by examining dimensions of teamness; extending laboratory paradigms to attain more realistic teaming, including nonhuman teammates; and advancing measures of team cognition in a direction such that data can be collected unobtrusively, in real time, and automatically.

Conclusion: The future of team cognition is one of the new discoveries, new research paradigms, and new measures.

Application: Extending the concepts of teams and team cognition can also extend the potential applications of these concepts.

Objective: This study aims to improve workers' postures and thus reduce the risk of musculoskeletal disorders in human-robot collaboration by developing a novel model-free reinforcement learning method.

Background: Human-robot collaboration has been a flourishing work configuration in recent years. Yet, it could lead to work-related musculoskeletal disorders if the collaborative tasks result in awkward postures for workers.

Methods: The proposed approach follows two steps: first, a 3D human skeleton reconstruction method was adopted to calculate workers' continuous awkward posture (CAP) score; second, an online gradient-based reinforcement learning algorithm was designed to dynamically improve workers' CAP score by adjusting the positions and orientations of the robot end effector.

Results: In an empirical experiment, the proposed approach can significantly improve the CAP scores of the participants during a human-robot collaboration task when compared with the scenarios where robot and participants worked together at a fixed position or at the individual elbow height. The questionnaire outcomes also showed that the working posture resulted from the proposed approach was preferred by the participants.

Conclusion: The proposed model-free reinforcement learning method can learn the optimal worker postures without the need for specific biomechanical models. The data-driven nature of this method can make it adaptive to provide personalized optimal work posture.

Application: The proposed method can be applied to improve the occupational safety in robot-implemented factories. Specifically, the personalized robot working positions and orientations can proactively reduce exposure to awkward postures that increase the risk of musculoskeletal disorders. The algorithm can also reactively protect workers by reducing the workload in specific joints.

Objective: The current paper conducted two parallel studies to explore user experiences of well-being conversational agents (CAs) and identify important features for engagement.

Background: Students transitioning into university life take on greater responsibility, yet tend to sacrifice healthy behaviors to strive for academic and financial gain. Additionally, students faced an unprecedented pandemic, leading to remote courses and reduced access to healthcare services. One tool designed to improve healthcare accessibility is well-being CAs. CAs have addressed mental health support in the general population but have yet to address physical well-being support and accessibility to those in disadvantaged socio-economic backgrounds where healthcare access is further limited.

Method: Study One comprised a thematic analysis of mental health applications featuring CAs from the public forum, Reddit. Study Two explored emerging usability themes of an SMS-based CA designed to improve accessibility to well-being services alongside a commercially available CA, Woebot.

Results: Study One identified several themes, including accessibility and availability, communication style, and anthropomorphism as important features. Study Two identified themes such as user response modality, perceived CA role, question specificity, and conversation flow control as critical for user engagement.

Conclusion: Various themes emerged from individuals' experiences regarding CA features, functionality, and responses. The mixed experiences relevant to the communication and conversational styles between the CA and the user suggest varied motivations for using CAs for mental and physical well-being.

Application: Practical recommendations to encourage continued use include providing dynamic response modalities, anthropomorphizing the chatbot, and calibrating expectations early.