Ergonomics最新文献

Passive exoskeleton chairs can alleviate fatigue, enhance efficiency, and reduce the risk of musculoskeletal diseases for workers standing for prolonged hours and have gradually been applied in recent years. In this study, by strategically distributing elastic and non-elastic fabrics, physical interfaces of the attachment system for the exoskeleton chair were revised to better adapt to bodily deformations and movements. We conducted an experiment using motion capture systems and subjective questionnaires to evaluate the performance of the initial and revised attachment systems of an exoskeleton chair worn by participants while performing multiple simulated assembly tasks. The results indicated that when wearing the revised one, some adverse effects on gait were significant reduced, as was the relative displacement of straps and discomfort in lumbar and abdominal, and system usability was improved, all of which were considered to be helpful in design to improve the performance of the attachment system in the future.

Automated vehicles could increase the risk of motion sickness because occupants are not involved in driving and do not watch the road. This paper aimed to investigate the influence of motion predictability on motion sickness in automated vehicles, as better motion anticipation is believed to mitigate motion sickness. In a simulator-based study, twenty participants experienced two driving conditions differing only in turn directions. The repetitive condition featured a repeating turn direction pattern. The non-repetitive condition contained pseudo-randomly ordered turn directions. To mimic an 'eyes-off-the-road' setting and prevent visual motion anticipation, road visuals were omitted. No significant differences in sickness or head motion, a metric for motion anticipation, were found between the conditions. No participant recognised the repeating turn pattern. This suggests no increased motion anticipation in the repetitive condition, possibly due to a reduced ability to recognise a repeating motion pattern in one degree of freedom within more complex motion.

Human Factors Engineers need accurate anthropometric data to design military equipment that is safe, comfortable and enables performance under extreme operational conditions and in the most severe environments. The Ministry of Defence (MOD) acknowledges that its current anthropometry dataset is becoming increasingly unrepresentative of today's Armed Forces personnel, particularly women and minority ethnic groups. To address this issue, MOD has launched a new, comprehensive anthropometry survey. Whilst this survey has the potential to benefit the design of all new military equipment and clothing, the principal driver for the study is the development of new body armour for the UK's Armed Forces personnel. This paper describes the requirements underpinning this survey, with a focus on body armour; the planned solution; and the results of Pilot Studies that have tested the robustness, reliability and accuracy of the measurement technologies and procedures. The work reported in this paper has been funded by the MOD's Defence Innovation Unit.Practitioner statement: The UK Ministry of Defence has launched a new, comprehensive anthropometry survey, in acknowledgement of the fact that its current anthropometry dataset is becoming increasingly unrepresentative of today's Armed Forces personnel. This paper describes the requirements underpinning the survey, the planned solution and the result of Pilot Studies.

In cervical health, the Posture Monitoring System (PMS) employs sensors to capture and transmit posture data to the cloud via Wi-Fi. This systematic review examines wearable PMS devices for cervical posture, analysing their attributes, findings, and limitations. Using systematic literature analysis, related studies were collected from diverse databases concentrating on wearable cervical posture devices. The review analysed the outcomes of each neck posture and each monitor type on the CVA ratio based on PMS. However, limitations, such as small sample sizes, limited functions, and privacy concerns were noted across the devices. The findings underscore the importance of considering user comfort and data accuracy in designing and implementing wearable posture monitors. Future studies should also explore the integration of advanced technologies and user-centred design principles to develop more accurate and user-friendly devices.

Ergonomics is evolving towards including a broader consideration of ecological and political factors in the design of work environments by adopting an interconnected systems paradigm, highlighting the importance of considering the ecological and political impacts of business. The emergence of 'green ergonomics' encourages sustainable human systems integrated in their natural environment, while respecting ethical values such as respect for human rights and diversity. On the one hand, this is leading to new areas of ergonomic research and intervention, such as energy management and corporate CSR, and on the other, to the need to develop specific skills to anticipate the future in innovative ways. For Human Factors and Ergonomics professionals (HF&E professionals), this means adopting prospective approaches that incorporate tools and methods to promote foresight, as well as creativity to imagine potential future work activities.

Despite the gains in performance that AI can bring to human-AI teams, they also present them with new challenges, such as the decline in human ability to respond to AI failures as the AI becomes more autonomous. This challenge is particularly dangerous in human-AI teams, where the AI holds a unique role in the team's success. Thus, it is imperative that researchers find solutions for designing AI team-mates that consider their human team-mates' needs in their adaptation logic. This study explores adaptive autonomy as a solution to overcoming these challenges. We conducted twelve contextual inquiries with professionals in two teaming contexts in order to understand how human teammate perceptions can be used to determine optimal autonomy levels for AI team-mates. The results of this study will enable the human factors community to develop AI team-mates that can enhance their team's performance while avoiding the potentially devastating impacts of their failures.

Consumer behaviour changes and strategic management decisions are driving adaptations in manufacturing routines. Based on the theory of situational strength, we investigated how contextual and person-related factors influence workers' adaptation in a two-worker position routine. Contextual factors, like retrieval cues (Study 1), time pressure (Study 2), and convenience (Study 3), were varied. Person-related factors included retentivity, general and routine-specific self-efficacy, and perceived adaptation costs. Dependent variables included various error types and production time before and after adaptation. In each study, 148 participants were trained in a production routine at t1 and executed an adapted routine at t2, one week later. Repeated measures ANOVA for performance at t1 and t2, and MANOVA for performance at t2, revealed that time increased for all groups at t2. For participants in Studies 1 & 2, error rates remained consistent. Retentivity significantly impacted errors at both t1 and t2, emphasising that routine changes in a 'running business' take time, regardless of contextual factors. Workers with lower retentivity may require additional support.

This study examines the impact of Human-Drone Interaction (HDI) modalities on construction workers' safety and balance control within virtual environments. Utilising virtual reality (VR) simulations, the study explored how gesture and speech-based communications influence workers' physical postures and balance, contrasting these modalities with a non-interactive control group. One hundred participants were recruited, and their movements and balance control were tracked using motion sensors while they interacted with virtual drones through either gesture, speech, or without communication. Results showed that interactive modalities significantly improved balance control and reduced the risk of falls, suggesting that advanced HDI can enhance safety on construction sites. However, speech-based interaction increased cognitive workload, highlighting a trade-off between physical safety and mental strain. These findings underscore the potential of integrating intuitive communication methods into construction operations, although further research is needed to optimise these interactions for long-term use and in diverse noise environments.

Background: Desk-work-related musculoskeletal pain is more prevalent among female workers than male workers. This may be contributed to by sex and/or gender differences in postural variability however, the mechanisms underpinning these differences are poorly understood. This review investigates whether desk-based postural variability studies investigate sex-gender differences and, how they explain the mechanisms behind these differences.

Methods: A scoping review was conducted with four databases (PubMed, Embase, Scopus and ProQuest) searched in June and July 2023. Studies investigating postural variability among desk-based workers were included and a narrative approach used to synthesise results.

Results: 15 studies were included. Only four reported on sex-gender differences. None collected psychological or social information to explore reasons for sex-gender differences.

Conclusion: The mechanisms behind postural variability differences between sexes and genders are complex and multifactorial. Studies largely do not consider sex and gender and do not collect the information necessary to explain their results.

Recent studies have focused on accurately estimating mental workload using machine learning algorithms and extracting features from physiological measures. However, feature extraction leads to the loss of valuable information and often results in binary classifications that lack specificity in the identification of optimum mental workload. This study investigates the feasibility of using raw physiological data (EEG, facial EMG, ECG, EDA, pupillometry) combined with Functional Data Analysis (FDA) to estimate the mental workload of human drivers. A driving scenario with five tasks was employed, and subjective ratings were collected. Results demonstrate that the FDA applied nine different combinations of raw physiological signals achieving a maximum 90% accuracy, outperforming extracted features by 73%. This study shows that the mental workload of human drivers can be accurately estimated without utilising burdensome feature extraction. The approach proposed in this study offers promise for mental workload assessment in real-world applications.