Applied Ergonomics最新文献

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.

This analysis examined systemic causes of Uncrewed Air Vehicle (UAV) accidents identifying operator, environmental, supervisory, and organisational factors through the use of the Human Factors Analysis and Classification System (HFACS). HFACS is a system-based analysis method for investigating the causal factors associated with accidents and incidents and has previously been used to reliably and systematically identify active and latent failures associated with both military and general aviation accidents. Whilst HFACS has previously been applied to UAV accidents, the last known application was conducted in 2014. Using reports retrieved from nine accident investigation organisations’ databases, causal factors were coded against unsafe acts, preconditions, and failures at the supervisory, organisational, and environmental levels. Causal factors were assessed on 77 medium or large UAV mishaps/accidents that occurred over a 12-year period up to 2024. 42 mishap reports were deemed to involve a human factor as a causal factor. A large proportion of the mishaps contained factors attributed to Decision Errors at level 1 (Unsafe Acts) which was found to be associated with both the Technological Environment and Adverse Mental State at level 2 (Pre-conditions). Causal factors were identified at each of the other 3 levels (Supervisory, Organisational and External) with a number of emergent associations between causal factors. These data provide support for the identification and development of interventions aimed at improving the safety of organisations and advice of regulators for Uncrewed Air Systems.

Integrating Artificial Intelligence (AI) and drone technology into bridge inspections offers numerous advantages, including increased efficiency and enhanced safety. However, it is essential to recognize that this integration changes the cognitive ergonomics of the inspection task. Gaining a deeper understanding of how humans process information and behave when collaborating with drones and AI systems is necessary for designing and implementing effective AI-assisted inspection drones. To further understand human-drone-AI intricate dynamics, an experiment was conducted in which participants’ biometric and behavioral data were collected during a simulated drone-enabled bridge inspection under two conditions: with an 80% accurate AI assistance and with no AI assistance. Results indicate that cognitive and behavioral factors, including vigilance, cognitive processing intensity, gaze patterns, and visual scanning efficiency can influence inspectors' performance respectively in either condition. This highlights the importance of designing inspection protocols, drones and AI systems based on a comprehensive understanding of the cognitive processes required in each condition to prevent cognitive overload and minimize errors. We also remark on the visual scanning and gaze patterns associated with a higher chance of missing critical information in each condition, insights that inspectors can use to enhance their inspection performance.

Industrial robots are increasingly commonplace, but research on prototypical accidents and injuries has been sparse, hindering evidence-based safety strategies. Using Severe Injury Reports (SIRs) from the U.S. Occupational Safety and Health Administration (OSHA), we identified 77 robot-related accidents from 2015-2022. Of these, 54 involved stationary robots, resulting in 66 injuries, mainly finger amputations and fractures to the head and torso. Mobile robots caused 23 accidents, leading to 27 injuries, mainly fractures to the legs and feet. A two-stage deductive–inductive thematic analysis was performed using text data from the final narratives in the reports to discover patterns in tasks, precipitating mechanisms, and contributing factors. Findings highlight the need for guards and collision avoidance systems that detect individual extremities. Post-contact strategies should focus on mitigating finger amputations. More structured and detailed narratives in the SIRs are needed.

Earplugs’ comfort is primarily evaluated through cost-effective laboratory evaluations, yet these evaluations often inadequately capture the multidimensional comfort aspects due to design limitations that do not replicate real-world conditions. This paper introduces a novel laboratory method for comprehensive assessment of the multidimensional comfort aspects of earplugs, combining questionnaire-based evaluations and objective perceptual tests within virtual industrial sound environments replicating in-situ noise exposure. Objective perceptual results confirm that the sound environment affect participants’ ability to detect alarms in a noisy environment and comprehend speech-in-noise while wearing earplugs. Subjective questionnaire results reveal that the earplugs family has an effect on the primary attributes of the acoustical, physical and functional comfort’s dimension. Participants reported the physical dimension as the most important factor they take into account when evaluating earplugs’ comfort. The functional dimension was considered the second most important factor by the participants, followed by the psychological dimension, and the acoustical dimension.