Applied Ergonomics最新文献

This article reports a systematic literature review that examined past research exploring the effectiveness of collaborative Augmented Reality (AR) enabled instruction, in higher education contexts. To be included, an article should consist of an experimental study investigating the use of collaborative AR for learning in higher education settings. An initial search was conducted on five databases that resulted in a total of 2537 articles, of which 20 were finalized for this review. The main findings suggest that AR-enabled collaborative learning benefits students’ overall learning outcomes and provides a positive collaboration experience in higher education settings. Further research is needed to determine the interaction elements, collaboration mechanisms, and information representation through AR that would potentially enhance student learning outcomes. This article concludes by discussing the implications of these findings, identifying challenges and strategies for developing effective collaborative AR-enabled learning content.

Carragher and Hancock (2023) investigated how individuals performed in a one-to-one face matching task when assisted by an Automated Facial Recognition System (AFRS). Across five pre-registered experiments they found evidence of suboptimal aided performance, with AFRS-assisted individuals consistently failing to reach the level of performance the AFRS achieved alone. The current study reanalyses these data (Carragher and Hancock, 2023), to benchmark automation-aided performance against a series of statistical models of collaborative decision making, spanning a range of efficiency levels. Analyses using a Bayesian hierarchical signal detection model revealed that collaborative performance was highly inefficient, falling closest to the most suboptimal models of automation dependence tested. This pattern of results generalises previous reports of suboptimal human-automation interaction across a range of visual search, target detection, sensory discrimination, and numeric estimation decision-making tasks. The current study is the first to provide benchmarks of automation-aided performance in the one-to-one face matching task.

Whole-body fatigue (WBF) presents a concerning risk to construction workers, which can impact function and ultimately lead to accidents and diminished productivity. This study proposes a new WBF monitoring technique by applying the Critical Power (CP) model, a bioenergetic model, with a wrist-worn heart rate sensor. The authors modified the CP model to calculate WBF from the percentage of heart rate reserve (%HRR) and generated a personalized model via WBF perception surveys. Data were collected for two days from 33 workers at four construction sites. The results showed that the proposed technique can monitor field workers’ perceived WBF with a mean absolute error of 12.8% and Spearman correlation coefficient of 0.83. This study, therefore, demonstrates the viability of wearable WBF monitoring on construction sites to support programs aimed at improving workplace safety and productivity.

Call-center-based telephone triage is an example of a complex sociotechnical system relying on successful interactions between patients, callers, and the integration of many digital technologies. Digital technologies such as computer decision support systems are used to standardize triage outcomes with little consideration of how these unique healthcare systems adapt to maintain functionality in response to real-world operating challenges. Using structured observations of call handlers in two call centers and guided by usability heuristics and the concept of ‘workarounds’, this paper aims to investigate the effects of technology design on workflow and system adaptations. Opportunities for improvement are highlighted, particularly, assessment prompts, and updating software to reflect dynamic real-world situations. Interactions between system components, especially technological and organizational processes affected workflow, making adaptations at the individual and organizational levels necessary to ensure callers could be triaged safely. System designers could consider these findings to improve systems and procedures during challenging periods.

Prior research has demonstrated experience with a forecasting algorithm decreases reliance behaviors (i.e., the action of relying on the algorithm). However, the influence of model experience on reliance intentions (i.e., an intention or willingness to rely on the algorithm) has not been explored. Additionally, other factors such as self-confidence and domain knowledge are posited to influence algorithm reliance. The objective of this research was to examine how experience with a statistical model, task domain (used car sales, college grade point average (GPA), GitHub pull requests), and self-confidence influence reliance intentions, reliance behaviors, and perceived accuracy of one's own estimates and the model's estimates. Participants (N = 347) were recruited online and completed a forecasting task. Results indicate that there was a statistically significant effect of self-confidence and task domain on reliance intentions, reliance behaviors, and perceived accuracy. However, unlike previous findings, model experience did not significantly influence reliance behavior, nor did it lead to significant changes in reliance intentions or perceived accuracy of oneself or the model. Our data suggest that factors such as task domain and self-confidence influence algorithm use more so than model experience. Individual differences and situational factors should be considered important aspects that influence forecasters' decisions to rely on predictions from a model or to instead use their own estimates, which can lead to sub-optimal performance.

Introduction

An integrated workplace health promotion program (WHPP) targeting multiple health behaviors by implementing activities at the individual and organizational level is potentially effective. The aim of this study was to evaluate the effect of implemented activities on targeted health behaviors.

Methods

Data from four organizations in a cluster randomized controlled trial, including 173 employees, were used. Linear multilevel analyses or generalized estimating equations were conducted to assess within- and between-condition differences for physical activity (PA) and nutrition.

Results

No between-condition differences were apparent for both health behaviors. Within the PA intervention condition, moderate PA increased and light PA decreased. Within the control condition the odds to consume more sugary drinks was lower.

Conclusion

Implemented activities did not affect the targeted health behaviors, although moderate PA increased within the PA intervention condition. Small sample sizes and implementation of minimal, irregular activities may underly the absence of effect. Future research should address this.

This mixed-method study evaluated the efficacy of lift assist device use (Binder®, Eagle®, Maxi Air®) relative to manual lifting/care-as-usual in reducing low back muscle activity and perceived exertion during simulated patient extrication tasks. User feedback was recorded to identify factors that might influence use. Twenty paramedics performed a floor to stretcher lift, lateral transfer, and confined space extrication care-as-usual and with lift assist devices. Use of a lift assist reduced low back muscle activity during floor to stretcher and confined space tasks by 34–47%. Paramedics perceived exertion decreased from ‘somewhat hard’ to ‘light’ or ‘very light’ when using an assistive device. Paramedics noted that ease of use, patient comfort, task time, patient acuity, among other considerations would influence use decisions. Lift assist devices were efficacious at reducing low back muscle activity and perceived exertion during floor to stretcher and patient extrication tasks.

Purpose

We investigated the influence of passive arm-support exoskeleton (ASE) with different levels of torque (50, 75, and 100%) on upper arm osteokinematics.

Methods

Twenty participants completed a cyclic overhead drilling task with and without ASE. Task duration, joint angles, and angular acceleration peaks were analyzed during ascent and descent phases of the dominant upper arm.

Results

Maximum ASE torque was associated with decreased peak acceleration during ascent (32.2%; SD 17.8; p < 0.001) and descent phases (38.8%; SD 17.8; p < 0.001). Task duration remained consistent. Increased torque led to a more flexed (7.2°; SD 5.5; p > 0.001) and internally rotated arm posture (17.6°; SD 12.1; p < 0.001), with minimal changes in arm abduction.

Conclusion

The small arm accelerations and changes in osteokinematics we observed, support the use of this ASE, even while performing overhead cyclic tasks with the highest level of support.

The integration of 3D gestural embodied human-computer interaction (eHCI) has provided an avenue for contactless interaction with systems. The design of gestural systems employs two approaches: Technology-based approach and Human-based approach. This study reviews the existing literature on development approaches for 3D gestural eHCI to understand the current state of research in 3D gestural eHCI using these approaches and identify potential areas for future exploration. Articles were gathered from three databases: PsycInfo, Science Direct and IEEE Xplore. A total of 35 articles were identified, of which 18 used human-based methods, and 17 used technology-based methods. Findings shed light on inconsistencies between developers and users in preferred hand gesture poses and identify factors influencing users’ gesture choice. Implementation of the consolidated findings has the potential to improve human readiness for 3D gestural eHCI technologies.

Improving fatigue management is critical to the occupational safety of professional drivers. We aimed to identify the factors that facilitated or hindered the implementation of digital sleep coaching in bus companies and to explore bus drivers’ experiences with it. Two bus companies implemented coaching for bus drivers. Using a mixed methods design, we collected data through two workshops (n = 30 and n = 27) attended by key personnel from the organisations and through questionnaires to the drivers (n = 30). Implementation was facilitated by, for example, the flexible participation and multichannel information of coaching, and hindered by restrictions on social support due to the COVID-19 pandemic, and lack of interest and inspiring examples. On average, the drivers rated the appropriateness and the feasibility of coaching as good. However, further development could lead to wider dissemination. It would also be important to involve the key people in the organisations and stakeholders more in supporting the implementation.