Ergonomics最新文献

In a novel, on-road study, using a 'Ghost Driver' to emulate an automated vehicle (AV), we captured over 10 hours of video (n = 520) and 64 survey responses documenting the behaviour and attitudes of pedestrians in response to the AV. Three prototype external human-machine interfaces (eHMIs) described the AV's behaviour, awareness and intention using elements of anthropomorphism: High (human face), Low (car motif), Abstract (partial representation of human features that lacked precise visual reference); these were evaluated against a (no eHMI) baseline. Despite many pedestrians reporting that they still relied on vehicular cues to negotiate their crossing, there was a desire/expectation expressed for explicit communication with future AVs. High and Low anthropomorphism eHMIs received the most positive responses for clarity, confidence and trust, with High also attracting significantly more/longer glances and the highest preference rating. In contrast, Abstract was considered least clear and subsequently invited the lowest confidence and trust ratings.

This study simulates the natural movement of plants in indoor environments to investigate whether these plants can effectively facilitate psychological, physiological, and emotional recovery from fatigue caused by short vigilance tasks. A total of 63 participants completed baseline assessments of emotional and physiological stress as well as attention and memory (including the POMS-SF, blood pressure, pulse, and Digit Span Backward). They then performed a vigilance task to induce fatigue, followed by a second measurement of stress and cognition. After random assignment to dynamic plant, static plant, or no-plant conditions for a rest intervention, participants underwent a final assessment. The results showed that all three conditions experienced significant fatigue induced by the vigilance task, with increases in stress and reductions in cognition. Following the intervention, those in the dynamic plant condition exhibited notably greater recovery across multiple indices-particularly in emotional stress and pulse-than those in the other conditions.

Ergonomics and Human Factors (E/HF) practitioners are increasingly engaged in projects meant to centre underserved communities and reduce inequities. The subdiscipline of E/HF that has emerged to explore the application of E/HF in this way is called community ergonomics. In this qualitative-descriptive study, we reflect on the progress made in the field of community ergonomics since its original conceptualisation in 1994. We present six E/HF case studies carried out in North America, South America, and Africa in a variety of community contexts to highlight the challenges of conducting community-based work. From those case studies, we synthesise six lessons learned that can be used to guide future community ergonomics projects. Finally, we provide methodological and epistemological recommendations for doing ethical community-based work, calling for E/HF practitioners to consider how their own ideologies are shaping their interactions with the communities they aim to serve.

Situation awareness (SA) is the ability to perceive, comprehend and project environmental information. Neural activity is closely associated with SA. However, it remains unclear how neural activity represents SA at different levels. Here, three tasks were used to assess SA at three levels, behavioural and electroencephalogram data were collected. Relationships between SA and neural activity were explored through comparisons of EEG power between high and low SA. For each SA level, EEG power significantly differed between high and low SA. Brain region-based analyses further revealed neural activities originating from distinct brain regions were recruited to represent SA at different levels. These EEG pattern features differed between high and low SA could be used to decode SA with the KNN (k-nearest neighbour) classifier. The present study marked a significant step in augmenting our understanding of the neural mechanism that characterise SA.

The use of exoskeletons is increasingly considered as a solution to reduce workers' exposure to physical risk factors, such as low-back disorders. The aim of this study was to evaluate the effects of the CORFOR^® occupational soft-back exoskeleton on trunk muscle activity and kinematics during an order picking manual task performed in the field. 10 workers, with at least 4 weeks' experience using the exoskeleton, performed a 1.5-hour order picking task with and without the exoskeleton. Trunk muscle activity, upper-body kinematics and the exoskeleton's acceptance were assessed. Erector spinae muscle activity was significantly reduced by 7.5% with the use of the exoskeleton. Moreover, trunk flexor muscles activity, trunk kinematics, or low-back pain were not affected. Further, the acceptance of the exoskeleton was rated as favourable. Thus, at least in the test company, the integration of the CORFOR^® exoskeleton for order picking tasks is promising.

A critical metaphor for the development, implementation and penetration of autonomous machine systems into the world of human work is presented. Most especially, the 'Isles of Autonomy' concept is articulated which argues that the expropriation of human pre-eminence will be marked by a series of threshold events, some of which are, even now becoming evident. In particular, it indicates that there will be a watershed event in which differing and distinct expressions of applied autonomous systems will spontaneously coalesce to produce an emergent, general artificial intelligence. The latter may well be unrelated to the original goals, aims and constraints of the disparate entities that have joined together. This threshold will be a harbinger of cascading unifications in which an unrestrained aggregate will assume de facto control over disparate work domains. The nature of such a development, most especially in light of associated human roles, is here evaluated. While emergent systems possess no necessary privilege, neither are their non-linear properties and behaviours directly inferable from their componential elements. The demi-sesquicentennial (75th) marking of the future of a science that is focused most especially on the predominance of human, work, is considered in light of these impending forces of change.

To enhance the convenience of human body 3D modelling, this study proposes a low-cost method for 3D body reconstruction under limited views, aiming to easily acquire client body size information through smart phone photography. The human body photos of the front, side and back view are captured, and background removal is performed using the U²-Net human segmentation model. The PIFuHD model is utilised to obtain single-view point cloud patches, which are then mapped onto 2D images. A point cloud registration approach that incorporates regional segmentation and contour supervision is employed to reconstruct a complete human body model. B-spline curves are employed to fit key girths for obtaining perimeter measurements, and the effectiveness of girth measurements is validated using body data from 80 subjects. The results indicate that the proposed method exhibits closer proximity to manual measurements compared to 3D scanning, with average absolute errors within 2 cm.

This study investigates the impact of the weight and centre of mass (COM) position of Head-Mounted Displays (HMDs) on the subjective evaluation of users during prolonged wearing tasks. This study involved 88 participants completing 1860 sets of experiments under three conditions: sitting still, turning the head, and moving, providing subjective evaluations of wearing HMDs. A static torque testing device was used to simulate neck torque under flexion states. Using Aligned Rank Transform (ART) data, A Multifactor Analysis of Variance (MANOVA) was conducted to analyse the relationship between subjective comfort and the weight and centre of mass (COM) of HMDs. Using cluster analysis to classify head length and identify the relationship between head length and comfort. A Support Vector Regression (SVR) model was ultimately established, providing detailed weight range references for the engineering design of HMDs.

Despite recent advances in technology use for education and training, the approach to pilot training over the past several decades has largely remained unchanged. Student pilots complete their training in actual aircraft, with very few flight hours conducted in flight training devices. This study aimed to investigate the effectiveness of various levels of simulator fidelity on ab initio pilot training. Thirty student pilots were invited to train using a virtual reality simulator, desktop simulator, or flight training device. Performance was evaluated using a modified Transport Canada Flight Test Guide alongside the NASA Task Load Index, Subjective Stress Scale, and Simulator Sickness Questionnaire, giving insight into mental workload, stress, and experience of simulator sickness, respectively. Findings show potential for virtual reality and desktop simulators regarding training procedural tasks; however, trainees must be aware of the limitations virtual reality and desktop simulators have concerning the training of aircraft handling tasks.

Numerous computer vision algorithms have been developed to automate posture analysis and enhance the efficiency and accuracy of ergonomic evaluations. However, the most effective algorithm for conducting ergonomic assessments remains uncertain. Therefore, the aim of this study was to identify the optimal camera position and monocular 3D pose model that would facilitate precise and efficient ergonomic evaluations. We evaluated and compared four currently available computer vision algorithms: Mediapipe BlazePose, VideoPose3D, 3D-pose-baseline, and PSTMO to determine the most suitable model for conducting ergonomic assessments. Based on the findings, the side camera position yielded the lowest Mean Absolute Error (MAE) across static, dynamic, and combined tasks. This positioning proved to be the most reliable for ergonomic assessments. Additionally, VP3D_FB demonstrated superior performance among evaluated models.Practitioner Summary: This study aimed to determine the most effective computer vision algorithm and camera position for precise and efficient ergonomic evaluations. Evaluating four algorithms, we found that the side camera position with VideoPose3D yielded the lowest Mean Absolute Error (MAE), ensuring precise and efficient evaluations.