Human Factors and Ergonomics in Manufacturing & Service Industries最新文献

A critical but often overlooked road safety concern is gender differences in road safety issues. The limited investigations have reported gender differences in road safety issues such as traffic accident rates and crash injuries. An emerging road safety issue is the safety of gig workers. Despite the exponential growth of the gig economy, little is known about road safety issues faced by gig workers, including gender-specific road safety issues. The present study aimed to investigate whether there are gender differences in gig workers' involvement in road safety incidents and the likelihood of reporting incidents when gig working. Gig workers who provide, or have provided, ride-hailing, courier, and food delivery services via digital platforms in Australia were invited to participate in an online survey on their involvement in road safety incidents and the likelihood that they would report road and personal safety incidents when gig working. Data from 71 gig workers (35 female gig workers; 36 male gig workers) were reported in the study. No gender differences were found in their involvement in road safety incidents and the likelihood of reporting road and personal safety incidents when gig working. Despite the lack of gender differences observed, findings from the study contributed to the research field and to closing the gender data gap. A research agenda is proposed for further data collection in gig work using a systems thinking approach to better our understanding of factors influencing the gendered participation in the gig workforce, and the health, safety, and wellbeing of gig workers.

This field study examined the automatic speech recognition (ASR) of voice-directed computerized systems for order selectors employed in large industrial settings (e.g., fulfillment centers, distribution centers, warehouses, and manufacturing plants). Voice-directed systems for order selection require selectors to listen to instructions via a headset and speak into a microphone, directing each worker to select products for store orders throughout the facility. Originally, ASR used voice recognition that required “voice enrollment” (voice setup) for each worker plus a trainer's time required as part of the setup. Voice setup generally averaged about 60 min for both the worker and the trainer. Lately, a newer technology now utilizes “speech recognition,” which eliminates voice enrollment altogether. This study measured order selector voice setup times between voice recognition and speech recognition in five facilities. In two distribution centers where speech recognition was implemented, all voice setup hours for all order selectors (n = 55) plus the trainer's time were eliminated. This amounted to a total savings of 110 h. Moreover, using speech recognition becomes a recurring saving for each new employee entering the organization. Now the focus of training is shifted from voice setup to immediately training workers to select orders via voice, an ergonomic improvement.

Interdisciplinary collaboration frequently comes into play when existing problems cannot be solved by one discipline alone. However, the interlocking of contributions from different disciplines is by no means trivial. This exploratory study examines one foundation of successful teamwork, namely shared mental models. To this end, we compared the contents of mental models between members of different but interdependent disciplines who collaboratively solve knowledge-intensive, creative tasks. Five automation and five mechanical engineers were recruited from a company that produces packaging machines. In semi-structured interviews, participants reported their approach to evaluating the process behavior of a packaging machine, and their mental models were represented in concept maps. Quantitative analyses revealed that the maps of automation engineers were smaller than those of mechanical engineers. In qualitative analyses, the focus on different levels of abstraction and on contents from the two disciplines was examined. Automation engineers represented a large proportion of rather abstract machine functions, whereas mechanical engineers additionally represented the physical implementation of these functions. The disciplinary focus also differed in the sense that automation engineers mainly attended to automated machine processes, while mechanical engineers attended to both mechanical and automated processes. Overall, automation engineers' focus was narrower than that of mechanical engineers. We explain these results by considering typical tasks and reasoning processes in both disciplines, and discuss how shared mental models can aid the integration of different disciplinary perspectives, for instance, during Systems Engineering.

Manual handling of heavy grain bags is a commonplace activity across agriculture produce supply chain. In the present research, a manual material handling involving lifting, lowering, and carrying grain bags along the paths of variable characteristics is critically analyzed to explore the risk associated with it. The humans engaged in this activity are found to be facing discomfort, suffering from pain and musculoskeletal disorders, and undergoing medication for the same. This study proposes and demonstrates a structured ergonomic evaluation methodology to not only assess the level of discomfort/pain but also evaluate risk factors such as load handled, method of handling, frequency of handling, awkward postures, path characteristics, and so on. It employs a systematic multimethod approach consisting of Nordic Musculoskeletal Questionnaire and Borg Rating of Perceived Exertion, Ovako Working posture Assessment System, NIOSH Lifting Equation, Rapid Entire Body Assessment, and Key Indicator Method. The study reveals that about 94% of the sample population suffered from moderate to severe discomfort in ankle, knee, and lower back. Eight basic activities responsible for risky postures are identified. Frequency of handling, weight being handled, back bending/twisting, poor coupling, and walking surface are observed as major contributors to musculoskeletal discomfort. Carrying on shoulder is found to be riskier than carrying on back; however, the risk is found to be significantly lowered by carrying on backpack. The present study identifies the need for task redesign, proper gripping arrangements, auxiliary devices for lifting/carrying, and improvement in the path characteristics.

Nowadays, the combination of “Nanotechnology” and “Ergonomics” has been known as “Nano-ergonomics.” Nano-ergonomics can help to develop more comfortable conditions in workplaces. So, the aim of this study is to reveal benefits of nanotechnology for occupational health and safety design, especially ergonomic design. The search strategy was provided based on cochrane guidelines with main search terms of “Nanotechnology,” “Nanomaterial,” and “Nanoparticle” combined with “Ergonomics” and “Human Factors.” PubMed, Scopus, Web of Sciences and Google Scholar Databases were researched for relevant articles. Also, Google search engine was used to find nano-ergonomic commercial products and to complete the research with identifying additional information. A total of 32 articles were first achieved. By providing Synthesis Without Meta-analysis reporting guideline, finally, four studies were regarded as appropriate. The results showed that nanotechnology has developed in three major areas of ergonomics such as physical, environmental, and cognitive ergonomics, which is a positive step toward more protection of workers' health. Although, there are not any original article related to nanotechnology for ergonomic product design, they are offered as the commercial products by the largest companies such as Amazon. Also, workers at the nanotechnology-related industries have the challenges of exposure to toxic nanomaterials. So, before the application of nanomaterials, we should have proper knowledge of nanomaterials-caused toxic hazards and how to handle them.

Electric micromobility (e-micromobility) offers the potential to enhance the sustainability of first- and last-mile journeys in urban areas by reducing the number of private vehicle trips. As a new mode of transport, it is imperative that it is not subject to the same male bias that has been evidenced across our existing transport networks. An in-depth qualitative study was conducted with 24 UK participants (12 females) to assess the gender factors that relate to the incentives and barriers of e-micromobility (electric bike and electric scooter) use. Focus groups and interviews were conducted and the data analysis was disaggregated by gender to reveal the differences and similarities between female and male perspectives on e-micromobility use. Differences in the types of trips made and perceptions of fear were prevalent. Key gender-related findings and recommendations are made. By reviewing, and acting upon, the different perspectives that males and females have towards e-micromobility they can be made more inclusive for all. This can enhance their uptake and reduce the dependence on private vehicles.

The main hazards in the process of manual handling work are triggered by human factors and ergonomics. This study is intended to implement a valid approach to quantitatively evaluate the risk level during manual handling work. A risk assessment model for manual handling workers was proposed based on subjective and objective correlation. A simulation experiment of manual handling process was developed and an ergonomics evaluation method was carried out. The 33-point human joint model of BlazePose neural network and the Rapid Upper Limb Analysis (RULA) method were utilized to determine the risk posture and risk index of the manual handling workers. This study brought together hemodynamic parameters and the score of Borg Rating of Perceived Exertion (RPE) scale and Physical Resources Scale (PRS) to obtain the final comprehensive index of work risk. The results showed that the risk indexes from the three stages of the experiment obtained by RULA method were 3, 4, and 7. And the scores of comprehensive indexes obtained by the risk assessment model were 1.841, 1.900, and 1.987, suggesting that the evaluation model based on subjective and objective correlation had the same ability to determine the risk levels of different handling tasks. Therefore, the risk assessment model proposed in this study verified the effectiveness of the comprehensive evaluation index integrating hemodynamic parameters and subjective evaluation scores.

Maritime Autonomous Surface Ships (MASS) currently have no formal regulations developed specifically for their operation, as their regulatory framework is still under development. Rasmussen's Risk Management Framework has been used to develop an actor map of the current MASS system in the UK, to show who the actors, decision-makers, and planners are within the wider sociotechnical system and the level at which they sit. From the actor map, two social networks were created, one to show the connections that currently exist between the actors within the MASS system and another to show what a future MASS system could look like if regulations and standards were put in place for MASS. Social Network Analysis was then used to investigate the wider MASS system's dynamics, to understand which actors currently have a high degree of influence within the UK MASS system, and where the shortfalls are in the current MASS system. The analysis showed that the industry and end user levels lacked support from the higher system levels, and the addition of formal regulations and standards in the future MASS system would increase the MASS system's resilience. System recommendations for each level in the Risk Management Framework were then made to suggest ways to increase the influence of the regulators and promote the safe operation of MASS.

When errors of automated vehicles (AVs) occur, drivers' trust can easily be destroyed, resulting in the reduction of the use of AVs. This study aims to examine how error of AVs declines driver's trust by impacting their subjective perceptions. A driving simulator experiment is conducted, in which 104 participants (male = 58; female = 46) experienced automated driving with automation errors and rated their trust. The results indicate that automation error will affect the driver's perceived predictability, perceived reliability, and perceived safety, which will lead to the decline of trust and abandonment of automated driving. With the occurrence of automation error of AVs, perceived safety plays a more critical role in drivers' trust. In addition, when automation errors occur in specific tasks with low risk, the trust of drivers will drop faster than that in high-risk tasks. This paper has explored the internal effects of the decline of driver's trust after automation errors of AVs, and further considers the influence of different external risks on these perception factors and trust. This study can help AVs manufacturers to formulate different degrees of trust repair strategies according to different driving tasks and accident severity.

As robots enabled by artificial intelligence become more agentic, people may come to develop trust schemas based on a robot's actions and attribute blame to the robot as they would with a human partner. Trust and blame have yet to be investigated during dynamic physical coordination tasks despite the potential ramifications for manufacturing and service industries that could benefit from effective human–robot physical coordination. In anticipation of future human–robot work configurations, we developed a joint physical coordination task as a preliminary test environment for understanding trust and blame in a work partner. Fifty-five participants were asked to jointly balance and transport a weighted box along a fixed path, and we used this test environment to evaluate the impact of a surprising event on trust in a work partner, and attribution of blame following a negative performance outcome. Results indicate that the group who experienced a surprising event compared to the group who did not trusted their partner more, but there was no difference in the attribution of blame to themselves, their partner, or to the surprising event. Conversely, the group who did not experience a surprising event tended to blame themselves for the negative outcome. These findings suggest that environmental uncertainty may prompt people's attribution of blame across multiple parties, including themselves. Moreover, people may build trust in work partners through the shared experience of surprising events. Future work would benefit from adopting our study design to investigate whether these findings are extendable to human–robot joint actors.