IISE transactions on occupational ergonomics and human factors最新文献

OCCUPATIONAL APPLICATIONSExposure Variation Analysis (EVA) is a method for describing variation in exposure, capturing not only the intensity of exposure (e.g., muscle activity amplitudes or posture levels) but also how long exposures of different intensities occur in uninterrupted sequences. The present review of studies applying EVA found that this method has been used across a range of occupational sectors, from computer-based office work to physically demanding jobs, such as assembly and caregiving, and for a range of exposures, including electromyography and postures. EVA offers a way to identify patterns of exposure and recovery that may contribute to fatigue, discomfort, or disorders, or conversely promote health. The flexibility of EVA makes it suitable even for use with wearable sensor data. By adopting EVA, ergonomics professionals can better assess risk and inform the design of tasks that reduce sustained exposures and promote more balanced activity patterns.

OCCUPATIONAL APPLICATIONSAssessing workers' strength capacities is a common practice prior to return to work following injury or illness, or assessing capabilities for strenuous jobs. Because it requires 50% or more of maximum strength capacity, hip abductor muscle strength is a strong predictor of both middle- and older-aged individuals' ability to reliably balance on one leg and of their risk of falls. Our results suggest subjects were able to augment their hip abductor moment during unipedal weight stance via gluteus maximus activity. Weight-bearing hip abduction strength measures are important for assessing worker capacity for jobs requiring reliable unipedal balance whether during lateral loading, while walking in gusty winds, on slippery footing or resisting lateral deck movements on board ship or train. Measurements of hip abductor strength should be made in a full unipedal weight bearing posture; non-weight-bearing measurements significantly underpredicted hip abductor strength as well as endurance.

OCCUPATIONAL APPLICATIONSWe used an algorithmic approach to assign tasks among a human worker and a collaborative robot assistant. When optimized on force and time as a measure for ergonomics cost, the algorithm assigned physically demanding aspects of the job to the robot, such as applying forces, while the human worker was assigned more dexterous tasks, such as the alignment of parts. Using this approach, operator stress was reduced while the cycle time for the job decreased. This method may be useful for better integrating collaborative robots into jobs requiring ergonomics interventions.

OCCUPATIONAL APPLICATIONSHand forces during material handling can be measured or estimated using different methods, including the application of biomechanical models. Biomechanical models for estimating hand forces are reviewed. These models consider object weight, type of lift (e.g., one- or two-handed), grip and arm posture, speed of movement, and object surface material. Case studies are presented that compare estimated hand forces to hazard assessment guideline threshold limits. Biomechanical models can predict the applied hand forces during material handling, but selecting the appropriate model depends on the type of grip, the speed of movement, the object weight, and other factors.

OCCUPATIONAL APPLICATIONSThe analysis of communication, attention, and cognitive engagement relative to leadership and non-technical skills (NTS) is important for understanding the team dynamic within clinical settings. We found negative correlations between passive communication (receiving information instead of dictating information) and leadership scores, suggesting that passive leadership traits lead to weaker technical leadership performance. Additionally, continuously sharing goals and providing affirmations to patients had positive relationships with leadership scores. These concepts should be utilized by leaders, as the dissemination of information to team members and consistent care and respect for patients can lead to a safer clinical environment. Relationships of eye-tracking and brain activity metrics to leadership scores indicated strong leadership was associated with awareness of struggling team members and decisiveness. Overall, passive leadership, such as indecisiveness, passive communication, and cognitive overload, show a negative relationship with non-technical skills and technical leadership skills in clinical care environments.

OCCUPATIONAL APPLICATIONSThe COVID-19 pandemic has significantly impacted workers and work. As Work from Home (WFH) modalities become more widely utilized, employers and workers alike face new benefits and challenges. The key aim of this report is to better understand the impact of WFH on the well-being of workers and identify potential risks of the WFH environment for this population subset. Using the NIOSH Worker Well-Being Questionnaire (WellBQ) tool, we quantified worker's well-being across five categories defined by the tool. Scores from individual items within these categories were then evaluated across different demographic factors (age, gender, education level, income, and WFH level) to better understand the implications of the WFH modality for the diverse WFH workforce. Evaluating well-being against the demographic scores may shed light on groups that need additional support, resources and tools to perform their work and remain in good health.