Ergonomics最新文献

This study proposes a systematic approach to address ergonomic factors, including physical, environmental and psychosocial aspects, in solving assembly line balancing problems. A three-stage framework is developed, starting with determining weights for ergonomic risk assessment methods using the interval-valued spherical fuzzy analytical hierarchy process. In the second stage, a fuzzy logic model for integrated ergonomic risk assessment is constructed based on these weights, and the integrated ergonomic risk score is determined. In the third stage, a mathematical model is formulated to minimise the cycle time while balancing the ergonomic risk level. A case study conducted in a wire harness factory validated the effectiveness of the proposed approach, showing a 10-11% improvement in line efficiency and a 12-25% enhancement in ergonomic risk balancing performance. These findings underscore the potential benefits of implementing this approach, which can significantly improve occupational safety and overall performance.

This study compared the number of postural shifts and perceived discomfort while leaning and sitting on an air-filled seat cushion for 1 hour. Sixty office workers typed a standard text while leaning on a cushion placed behind the low back, sitting on a cushion placed under the buttocks, and sitting without a cushion (a control condition). The number of postural shifts was collected using a seat pressure mat device. Low back discomfort was assessed using the Borg CR-10 scale. Leaning on a seat cushion (22 shifts/h) led to a significantly higher number of postural shifts than sitting on a seat cushion (18 shifts/h) and the control condition (20 shifts/h). Leaning or sitting on a seat cushion significantly decreased low back discomfort compared to the control condition (p < 0.05). Leaning on a seat cushion placed behind the low back may be an effective means of preventing low back pain among office workers.

Industry 4.0 technology is promoted as improving manufacturing flexibility, and competitiveness; though Australia has been slow to adopt. The Australian Navy shipbuilding program provides opportunities for accelerating technology adoption, revitalising manufacturing productivity and competitiveness. Adopting a sociotechnical systems lens, our research sought to identify usability, workload, and user experience of an augmented reality head-mounted display (AR-HMD) deployed to complete multiple work tasks in a workflow (electrical assembly, collaborative robot (cobot) mediated inspection, and remote troubleshooting using video call). Usability was rated 'average' (System Usability Scale mean = 69.8) and workload 'acceptable' (NASA Task Load Index mean = 25.8) for the AR-HMD alone, with usability of the integrated work system (IWS) rated 'good' (SUS mean = 79.2). Results suggest software interfaces, tracking, and gesturing methods for the AR-HMD require improvement. This trial shows the AR-HMD provides a versatile platform for integrating multiple digital technologies without hindering effectiveness of end-user performance, potentially benefiting productivity and quality.

This study aims to analyse and determine the effect of Big Data, the Internet of Things (IoT), and physical-cyber system variables on human factors in refinery industry operators and the influence of human factors and managerial initiatives on sustainable manufacturing. The method used in this study is a quantitative method using partial least square-structural equation modelling (PLS-SEM). The respondents in this study were workers of Indonesia's upstream oil and gas sector. The results of this study indicate that Big Data, IoT, and Physical Cyber Systems (PCS) have a positive and significant effect on the human factor. In addition, there is a significant relationship between human factors and sustainable manufacturing. Furthermore, it is also found that there is a relationship between managerial initiatives and sustainable manufacturing. However, the managerial initiative cannot moderate the human factor and sustainable manufacturing.

This study examines the barriers to integrating portable Magnetic Resonance Imaging (MRI) systems into ambulance services to enable effective triaging of patients to the appropriate hospitals for timely stroke care and potentially reduce door-to-needle time for thrombolytic administration. The study employs a qualitative methodology using a digital twin of the patient handling process developed and demonstrated through semi-structured interviews with 18 participants, including 11 paramedics from an Emergency Medical Services system and seven neurologists from a tertiary stroke care centre. The interview transcripts were thematically analysed to determine the barriers based on the Systems Engineering Initiative for Patient Safety framework. Key barriers include the need for MRI operation skills, procedural complexities in patient handling, space constraints, and the need for training and policy development. Potential solutions are suggested to mitigate these barriers. The findings can facilitate implementing MRI systems in ambulances to expedite stroke treatment.

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.

Single-item scales of perceived usability are attractive due to their efficiency and non-verbal scales are attractive because they enable collecting data from individuals irrespective of their language proficiency. We tested experimentally whether single-item verbal and pictorial scales can compete with their 10-item counterparts at reflecting the difference in usability between well-designed and poorly designed systems. N = 1079 (Experiment 1) and N = 1092 (Experiment 2) participants worked with two systems whose usability was experimentally manipulated. Perceived usability was assessed using the 10-item System Usability Scale, the single-item Adjective Rating Scale, the 10-item Pictorial System Usability Scale and the Pictorial Single-Item Usability Scale. The single-item scales reflect the difference in usability as good as their 10-item counterparts. The pictorial scales are nearly as valid as their verbal counterparts. The single-item Adjective Rating Scale and the Pictorial Single-Item Usability Scale are thus efficient and valid alternatives to their 10-item counterparts.

Understanding the characteristics and measurement properties of the observational methods for biomechanical exposure analysis contributes to choosing and planning workplace ergonomic interventions. This systematic review search was performed in the four databases. In 99 studies, 75 observational methods were identified. Posture/movement, force and repetitiveness were the most evaluated risk factors for the upper limbs, trunk and head. EAWS, OCRA, the expanded PATH and QEC evaluate more biomechanical risk factors. EAWS, PATH, QEC, CADEP, ROSA, REBA, modified REBA, RULA, all body segments. Criterion validity, reliability and agreement are REBA and ROSA's most tested measurement properties. The quality of evidence ranged from moderate to high for 23 methods. ALLA, HAL, OFFERA, simplified PATH and the expanded PATH stand out with high-quality evidence. The expanded PATH and QEC are the most complete in evaluating several occupational tasks, respectively, with high- and moderate-quality evidence.

With their increased capability, AI-based chatbots have become increasingly popular tools to help users answer complex queries. However, these chatbots may hallucinate, or generate incorrect but very plausible-sounding information, more frequently than previously thought. Thus, it is crucial to examine strategies to mitigate human susceptibility to hallucinated output. In a between-subjects experiment, participants completed a difficult quiz with assistance from either a polite or neutral-toned AI chatbot, which occasionally provided hallucinated (incorrect) information. Signal detection analysis revealed that participants interacting with polite-AI showed modestly higher sensitivity in detecting hallucinations and a more conservative response bias compared to those interacting with neutral-toned AI. While the observed effect sizes were modest, even small improvements in users' ability to detect AI hallucinations can have significant consequences, particularly in high-stakes domains or when aggregated across millions of AI interactions.

Detecting daily stress is of vital importance for workplace safety and health, and natural speech is recommended as one of the main methods of mental stress detection. This study developed machine-learning models for daily stress detection from real-life speeches by fusing its acoustic and semantic signals. First, we collected real-life speech data from life-stress-catharsis room of online chat platform and established a speech database with real daily stress. Second, we obtained the model performances of common machine-learning classifiers for stress detection and compared them with human performance. The stress-detection classifiers achieved a promising performance of 74.25% accuracy and 83.73% F1-score using only acoustic signal. By fusing with the semantic signal, the stress detection model performance was significantly improved and achieved a performance of 81.20% accuracy and 87.46% F1-score, which validated the importance of semantic information in daily stress detection. Meanwhile, the best performance of the machine learning model was close to the human recognition capability. The results of this study validated the feasibility of detecting daily stress based on real speech. The models developed in this study could be used for daily stress detection in real life and can provide information for stress interventions to ease the negative effects on health.