International Journal of Industrial Ergonomics最新文献

{"title":"An extended individualized two-node human thermal model for high-temperature environments","authors":"Donghua Liu ,&nbsp;Xuanye Tong ,&nbsp;Haitao Wang ,&nbsp;Jie Yang","doi":"10.1016/j.ergon.2025.103792","DOIUrl":"10.1016/j.ergon.2025.103792","url":null,"abstract":"<div><div>The human thermal model offers significant utility and advantages in predicting human heat strain. An advanced two-node human thermal model was proposed based upon Gagge's foundational to simulate individual physiological responses in high-temperature settings. This enhanced model incorporates the impacts of elevated temperatures on metabolic rate and the convective coefficient, and it accounts for individual variations in body surface area, the set points of body temperatures, and skin blood flow. Additionally, adjustments were made to the parameters representing clothing thermal insulation and vapor resistance. eight Chinese youths—four males and four females—were exposed to a high-temperature environment (35 °C/50 % RH) while wearing two different types of clothing to replicate light and moderate intensity activities to validate the model. The findings indicate that the model excels in predicting the thermal responses of individuals under this experimental condition. The maximal discrepancies between simulated and observed values for core and skin temperatures were confined to 0.3 °C and 0.6 °C, respectively. The model has been preliminarily demonstrated to reliably forecast individual physiological responses in given high-temperature environments. Nevertheless, the current framework does not take into account water loss in the human body, which is a key factor in long-term heat exposure. Looking to the future, more thermal regulation parameters can be comprehensively considered, and verified under various working conditions, so as to predict the thermal strain of the human body in high-temperature environments more comprehensively.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"109 ","pages":"Article 103792"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A virtual reality-based study on tunnel black hole effects on drivers' psychological stress and driving performance","authors":"Hanlin Xu ,&nbsp;Xianliang Ge ,&nbsp;Ke Zhang ,&nbsp;Hao Ni ,&nbsp;Lei Du ,&nbsp;Liezhong Ge ,&nbsp;Zhen Yang ,&nbsp;Duming Wang","doi":"10.1016/j.ergon.2025.103791","DOIUrl":"10.1016/j.ergon.2025.103791","url":null,"abstract":"<div><div>The phenomenon known as the \"black hole effect,\" which occurs at tunnel entrances, is a severe visual illusion induced by dramatic transitions in illuminance, space and reference. Previous studies on tunnel driving safety have been conducted mainly in real tunnels, making it difficult to isolate the effects of different tunnel lighting environments on driving safety and to accurately measure drivers' speed and distance estimations. Therefore, in this work, we introduce a virtual tunnel model with consistent lighting and introduce three scenarios: entering the tunnel (T1), outside the tunnel (T2), and open road (Base). We compared changes in ego speed and dynamic distance estimates for 30 drivers across the three scenarios using the time-to-contact (TTC) paradigm to investigate the effects of spatial transitions. Linear mixed model analyses revealed that spatial transitions caused drivers to significantly overestimate their own speed and underestimate the TTC and caused significant pupil dilation in the T1 scenario. However, results for T2 did not show significant differences in distance or speed estimation from the base results, but the pupil diameter increased significantly. The results of this experiment support the hypothesis that tunnels introduce spatial transitions that directly affect the ego speed and dynamic distance estimation and that tunnels induce driver stress immediately upon entry into the visual field.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"109 ","pages":"Article 103791"},"PeriodicalIF":3.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differences in types and causes of human errors between high-speed and conventional railway dispatching: the potential impact of automation","authors":"Zizheng Guo ,&nbsp;Huishan Pang ,&nbsp;Jun Liu ,&nbsp;Jiazhe Wang ,&nbsp;Jingyu Zhang ,&nbsp;Mingming Gao","doi":"10.1016/j.ergon.2025.103790","DOIUrl":"10.1016/j.ergon.2025.103790","url":null,"abstract":"<div><div>Dispatcher errors are critical for railway safety, yet the impact of new technology on error types and causes is less understood. High-speed railways (HSR) utilize advanced automated technologies that enhance efficiency but introduce new challenges. This study analyzed 8522 error records from both HSR and conventional rail (CR) dispatchers using a human error classification framework. We categorized these errors into three key task types: monitoring, planning and scheduling, and dispatching instructions. Results showed that HSR dispatchers make more errors in planning and scheduling tasks, particularly decision-making errors influenced by poor crew resource management and adverse mental states. In contrast, they make fewer errors in monitoring and dispatch instructions, with reduced decision errors and violations. Additionally, skill-based errors were less affected by factors such as personal readiness and supervisory issues. We explained these findings by linking them to how automation reduces procedural workload, decreases controllability and predictability, and induces out-of-the-loop unfamiliarity.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"109 ","pages":"Article 103790"},"PeriodicalIF":2.5,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Human-AI perceptual alignment through visual-haptic feedback system for autonomous drones","authors":"Jiahao Wu ,&nbsp;Bowen Sun ,&nbsp;Hengxu You ,&nbsp;Jing Du","doi":"10.1016/j.ergon.2025.103780","DOIUrl":"10.1016/j.ergon.2025.103780","url":null,"abstract":"<div><div>Artificial Intelligence (AI) has emerged as an effective agent for controlling autonomous drones in navigation and target search tasks across various applications with minimal human intervention. Despite their advantages, significant challenges exist in aligning human operators' perceptual understanding with autonomous drone AI's assessment of environmental changes, particularly in dynamic and complex urban settings. This study addresses this issue by proposing a human-machine sensory sharing system that integrates visual and haptic feedback to enhance situational awareness, reduce cognitive load, and improve trust in the AI agent that controls the drones. By bridging the perceptual gap between humans and AI, our approach fosters a more cohesive and responsive interaction, enabling operators to make informed decisions in real-time. Through a human-subject experiment (N = 30) in a simulated urban environment, participants assessed environmental changes and adjusted drone AI parameters based on multimodal sensory feedback. Eye-tracking data were collected to evaluate cognitive load and engagement under different feedback conditions. Results show that combining visual and haptic feedback significantly enhances user performance, satisfaction, and decision-making speed, reducing perceptual misalignment between humans and AI. Participants using multimodal feedback demonstrated faster response times and higher environmental assessment accuracy than single-modality feedback. This research advances the design of intuitive human-drone interaction systems, emphasizing the role of multimodal sensory integration and physiological monitoring in improving human-machine collaboration. These findings have implications for applications in logistics, search and rescue, surveillance, and environmental monitoring, where operator engagement and performance are critical.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"109 ","pages":"Article 103780"},"PeriodicalIF":2.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of potential factors affecting roughness perception","authors":"Hankyoul Kim,&nbsp;Sung H. Han,&nbsp;Subin Kim,&nbsp;Heetae Hwang,&nbsp;Junseong Park,&nbsp;Kimin Kwon","doi":"10.1016/j.ergon.2025.103778","DOIUrl":"10.1016/j.ergon.2025.103778","url":null,"abstract":"<div><div>The importance of tactile feedback in extended reality has gained increasing attention for its potential to enhance immersive user experience. However, the implementation of tactile feedback still faces various challenges not only due to technical difficulties but also because the cognitive processes involved remain unclear. Additionally, previous studies on roughness perception are fragmented due to varying implementations of tactile stimuli, specimen provision, and experimental environments. Therefore, a comprehensive understanding of roughness perception is essential. This study reviews existing literature to identify the key potential factors affecting roughness perception, categorizing them into four main elements based on the User Experience Framework: Interaction, System, User, and Context. Interaction refers to how subjects experience roughness, System pertains to the implementation of roughness stimuli, User represents the characteristics of subjects perceiving roughness, and Context addresses the environment of roughness perception. As a result, 32 factors and 8 factor groups were identified and systematically organized to better understand their effects on roughness perception. By structuring these factors within the User Experience Framework, this study provides a foundation for a more comprehensive understanding of roughness perception. Furthermore, these findings offer insights into the broader domain of tactile experience, suggesting potential applications in various fields such as haptic technology development, virtual reality, and human-computer interaction.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"108 ","pages":"Article 103778"},"PeriodicalIF":2.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing workload-informed crew configuration recommendations for emergency medical services","authors":"Setareh Darvishi ,&nbsp;Paul Misasi ,&nbsp;Laila Cure","doi":"10.1016/j.ergon.2025.103777","DOIUrl":"10.1016/j.ergon.2025.103777","url":null,"abstract":"<div><div>While crew configuration in primary care settings has been studied in terms of its impact on patient outcomes, less is known about how it influences the members' workload experience. This study investigates the workload implications of crew configuration based on members' certification in emergency medical services (EMS). Advanced life support (ALS) ambulance crews are commonly comprised of two paramedics (homogeneous crew) or an emergency medical technician (EMT) and a paramedic (heterogeneous crew). The goals of this study were the following: (1) to investigate differences in workload among members of the same crew, and (2) to use workload assessments to inform crew configuration strategies. We mapped one year of an EMS system's dispatch data to members' workload estimates using the visual, auditory, cognitive, and psychomotor (VACP) approach. We found that lead members (lead paramedics) experience higher workload levels compared to support members (support paramedics or EMTs) in both types of crews. Neither configuration had a consistently lower workload than the other, but differences varied for different shifts and stations. These results informed crew configuration recommendations for stations and shifts in the collaborating system, and in terms of more generalizable variables. A minimum number of staffed crews, half-half shift type (covering both day and night hours), and 30-day frequency of calls with priority P7 most significantly impacted the recommended crew configurations.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"108 ","pages":"Article 103777"},"PeriodicalIF":2.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The influence of interface attributes and interaction elements on user performance and cognitive load in task interruption scenarios","authors":"Chi Zhang ,&nbsp;Da Peng Wei ,&nbsp;Yue Ji ,&nbsp;Ding Chen ,&nbsp;Xin Yan Li ,&nbsp;Xiao Dong Gong","doi":"10.1016/j.ergon.2025.103761","DOIUrl":"10.1016/j.ergon.2025.103761","url":null,"abstract":"<div><div>This study aims to investigate the mechanisms by which digital interface attributes (interface complexity and similarity) and interaction elements (contextual guidance during the task resumption phase) influence user task performance and cognitive load in task interruption scenarios. A within-subject experimental design was employed, where participants were required to switch between primary and interrupting tasks while completing designated arithmetic tasks, thereby simulating real-world task interruptions. The results indicate that interface complexity is a key factor affecting task performance and cognitive load. High-complexity interfaces significantly prolonged task resumption lag times, increased error rates, and led to higher levels of cognitive load. The effect of interface similarity on performance showed a significant interaction with complexity. Under high complexity conditions, high-similarity interfaces increased resumption lag times; conversely, under low complexity conditions, high-similarity interfaces reduced resumption lag times. Furthermore, providing clear contextual guidance significantly shortened resumption lag times during the task resumption phase and reduced the number of selection errors, although it did not have a sufficiently significant impact on cognitive load. Despite the varying influence of each variable on resumption performance, the overall performance can be explained by the Memory for Goals Theory (MFG). The findings offer valuable theoretical and practical guidance for optimizing interface design in complex task environments, underscoring the importance of considering interface complexity, similarity, and resumption guidance when designing multitasking interfaces.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"108 ","pages":"Article 103761"},"PeriodicalIF":2.5,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gender-specific effects of passive wearable exoskeletons (pwExo) on gait stability and fall risk in simulated construction tasks","authors":"Muhammad Khan,&nbsp;Chukwuma Nnaji","doi":"10.1016/j.ergon.2025.103776","DOIUrl":"10.1016/j.ergon.2025.103776","url":null,"abstract":"<div><div>Exoskeletons (Exos) offer significant benefits, but they may also introduce balance-related challenges, which could vary across genders or be exacerbated by gender-specific factors. This study investigates their biomechanical impact on gait stability and fall risk, considering gender-specific differences and task-specific outcomes. Key metrics, including Peak Pressure (PP), Mean Pressure (MP), Pressure-Time Integral (PTI), and Full Width at Half Maximum (FWHM), were analyzed across dynamic and static tasks. Results revealed that pwExos significantly influenced plantar pressure distribution, particularly in dynamic tasks such as lifting and carrying. Female participants experienced substantial reductions in peak heel pressure (up to 17.7%, p = 0.021), metatarsal pressures (14.2%, p = 0.034), and arch PTI (up to 52.8%), suggesting enhanced load redistribution and stability, whereas males exhibited increased forefoot loading, including a 15.5% increase in metatarsal PP and a 90.8% rise in toe PTI, indicating potential compensatory adaptations. Mean Pressure in females decreased by 73.5% at the heel during placing tasks, while males showed increased anterior pressure in static tasks. The pwExos were most effective during high-load tasks such as lifting and walking with sandbags (10lb), where pressure redistribution was greatest. While static tasks showed limited benefits, improvements in the arch and metatarsal regions suggest partial stabilization. These findings emphasize the potential of pwExos to enhance stability and reduce biomechanical fatigue, highlighting the need for task-specific and gender-sensitive designs. Future research should assess long-term effects, real-world applicability, and the role of individual factors such as Body Mass Index to optimize Exo integration in construction safety and health management.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"108 ","pages":"Article 103776"},"PeriodicalIF":2.5,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of classical and ensemble models for predicting whole body vibration induced lumbar spine stress. A case study of agricultural tractor operators","authors":"Amandeep Singh ,&nbsp;Naser Nawayseh ,&nbsp;Philippe Doyon-Poulin ,&nbsp;Stephan Milosavljevic ,&nbsp;Krishna N. Dewangan ,&nbsp;Yash Kumar ,&nbsp;Siby Samuel","doi":"10.1016/j.ergon.2025.103775","DOIUrl":"10.1016/j.ergon.2025.103775","url":null,"abstract":"<div><div>Accurate prediction of lumbar health is necessary for developing effective ergonomic strategies for tractor operators exposed to whole-body vibration. This study aims to predict static compression dose (S_ed), a key measure of lumbar spine stress as per ISO 2631-5, by comparing classical regression and ensemble models. Three tractor operation parameters (average speed, average depth, and pulling force) are considered to assess S_ed during rotary tillage operation. The performance of two classical models (Linear and Huber regression) is compared with five ensemble models (Random Forest, Gradient Boosting, XGBoost, AdaBoost, and Bagging regressors) in predicting S_ed. The comparison identifies the best models in each category, with linear regression achieving a mean bootstrap R² of 0.91 (95 % CI: 0.87 to 0.94) and Random Forest achieving 0.93 (95 % CI: 0.90 to 0.95). To further enhance performance, meta-models are developed using two meta-learners (Random Forest and Gradient Boosting) to integrate classical and ensemble models. These models are optimized using different ensemble strategies: simple averaging, weighted averaging, stacking, and voting regressors. Among these, the stacking method proves most effective, achieving a mean bootstrap R² of 0.94 (95 % CI: 0.93 to 0.96). Feature importance analysis reveals that the multi-model combination of ensemble models achieves the highest predictive score (0.99) for S_ed. These findings demonstrate that ensemble models outperform classical models in predicting Sed, particularly when combined through stacking methods. This advancement has significant implications for improving occupational health and safety among tractor operators, potentially leading to better ergonomic tractor designs aimed at reducing lumbar spine stress.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"108 ","pages":"Article 103775"},"PeriodicalIF":2.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new measurement for workload assessment in agricultural tasks: EDA-based real-time model","authors":"Sujin Seong ,&nbsp;Jaehyun Park ,&nbsp;Jeong Ho Kim","doi":"10.1016/j.ergon.2025.103771","DOIUrl":"10.1016/j.ergon.2025.103771","url":null,"abstract":"<div><div>Agriculture is vital for the global economy but remains one of the most hazardous industries due to the high prevalence of musculoskeletal disorders (MSDs). This study aimed to develop and validate an electrodermal activity (EDA)-based model for non-invasive, real-time assessment of upper limb task workload, addressing the impact of environmental factors on workers' mental and physical strain. To achieve this, participants engaged in simulated pruning and harvesting tasks, which are known for imposing substantial workload and injury risks on the upper extremities in agriculture. Anthropometric data, EDA signals, and the Borg rating of perceived exertion (RPE) were collected. A multinomial logistic regression (MLR) model was employed to classify workload levels based on EDA wavelet features and key task-related variables. The developed models for pruning and harvesting tasks explained 40–50 % of the variance in RPE, demonstrating the highest accuracy in the middle RPE group (88–89 %). Specificity was notably high (&gt;91 %) across both tasks for low and high RPE groups. Additionally, the middle RPE group exhibited recall and F1 scores above 84.5 %, while the high RPE category demonstrated recall, precision, and F1 scores ranging from 73 % to 92 % for pruning and from 60 % to 75 % for harvesting. These findings underscore the model's potential for precise workload categorization and the development of effective management strategies. Furthermore, the proposed EDA-based framework may hold broader applicability across various occupational domains that require non-invasive and continuous workload monitoring.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"108 ","pages":"Article 103771"},"PeriodicalIF":2.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}