International Journal of Industrial Ergonomics最新文献

{"title":"Teatime Tales: A Deep Dive into the social sustainability of the tea garden ecosystem","authors":"Ankit Basak ,&nbsp;Shiv Kumar Verma","doi":"10.1016/j.ergon.2025.103841","DOIUrl":"10.1016/j.ergon.2025.103841","url":null,"abstract":"<div><div>Tea gardens are one of India's oldest organised sectors. However, people employed in the sector still face issues similar to those in the unorganised sector. Most studies in this domain have primarily focused on tea leaf production and operational efficiency rather than the social sustainability aspects of the tea garden ecosystem. Moreover, there is also a dearth of research that includes the perspective of females working in the domain. The current exploratory study examines the tea garden ecosystem to understand the various factors that affect workers and their working conditions, with a particular focus on gender dynamics and social sustainability. We adopted a qualitative research methodology for the study. Direct observation was conducted to study the behaviour of workers, various processes, and events in the tea garden. Videos and images were collected for a visual ethnography. Lastly, we conducted semi-structured interviews with various stakeholders in the tea garden, including workers, administrators, managers, and doctors, followed by thematic analysis to analyse the collected data. The findings reveal persistent challenges such as low wages, gender-based division of labour, lack of ergonomic support, and even the influence of gender-specific clothing on workers' health, particularly during pesticide spraying. The findings from direct observation, visual ethnography, and semi-structured interviews were then combined to provide a comprehensive view of the various issues, challenges, and working conditions faced by the tea garden workers. The research provides a foundational understanding that can inform policy, design, and future interdisciplinary studies aimed at enhancing social sustainability in similar labour-intensive sectors.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103841"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749070","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":"Guiding by touch: A vibrotactile navigation system for underwater situational awareness","authors":"Giovanna Camacho ,&nbsp;Matthew L. Bolton ,&nbsp;Amanda Watson ,&nbsp;Henry Bearden ,&nbsp;Sharon Lu","doi":"10.1016/j.ergon.2025.103864","DOIUrl":"10.1016/j.ergon.2025.103864","url":null,"abstract":"<div><div>This study will test the usability of wearable, vibrotactile cues in providing intuitive orientation and communication cues to participants in visually challenging underwater navigation tasks. The device’s signals were designed to communicate the three levels of situational awareness (SA; perceive, comprehend, and project) intuitively, as if one was being guided by a partner’s hand. We evaluated the effectiveness of this device in a human subject experiment with divers wearing fully blacked-out dive masks. Performance with the vibrotactile display was compared against the Scubapro heads-up display, along with dive rescue team rope pulls based on performance measures (navigation, accuracy, and time). Subjective measures of mental workload, situational awareness, and usability were collected; as well as surveys designed to understand how participants classified tactor signals into SA levels. The results showed that the tactile design enhanced accuracy, but increased navigation time. This design was comparable to other standard methods across subjective mental workload, SA, and usability measures. The paper discusses the significance of these results for the navigation of both commercial and professional divers. It also explores the implications for navigation support in other visually challenging environments.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103864"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798266","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":"How can human emotion be regulated by scent in intelligent cockpits? Evidence from EEG-based multimodal assessment with AI support","authors":"Xianhui Wu ,&nbsp;Zhuoxi Jiang ,&nbsp;Zhongjing Xia ,&nbsp;Chaojie Fan ,&nbsp;Chengxi Li ,&nbsp;Ziteng Zhang ,&nbsp;Meng Zheng ,&nbsp;Yong Peng","doi":"10.1016/j.ergon.2025.103876","DOIUrl":"10.1016/j.ergon.2025.103876","url":null,"abstract":"<div><div>Emotion is one of the core psychological attributes of human beings, and maintaining a stable emotional state is a key objective of human factors design, particularly in high cognitive-load driving scenarios. As intelligent cockpits shift from function-centered to human-centered design, emotion perception and response are becoming key directions in human-machine interaction. However, systematic research and practical support for emotion regulation mechanisms remain limited. This study adopted olfactory perception as an entry point to explore the mechanisms and neural basis of scent intervention in alleviating drivers' negative emotions and optimizing driving performance. A multi-modal perspective was employed, integrating neural, behavioral, and subjective data. An emotion regulation experiment with 22 participants assessed the effects of four representative scents. Regulatory efficacy was quantified using repeated-measures ANOVA and the Friedman test. To further evaluate emotional changes, a deep learning based emotion recognition model computed an Emotion Alleviation Index, which was correlated with EEG features via Spearman correlation for cross-dimensional validation. Results showed that scent stimulation significantly modulated theta and alpha oscillations in the frontal region, alleviated negative emotions, and enhanced cognitive and driving performance, improving behaviors such as lane-keeping and throttle control. Furthermore, regulatory efficacy depended on the interaction between pleasantness and arousal. High arousal scents such as citrus and tea enhanced theta-band activity and executive control, while high pleasantness scents such as citrus and lavender increased alpha-band energy and induced positive states. A dual-dimensional pleasantness–arousal regulatory framework strategy was proposed to guide the design of olfaction-based affective human-machine interaction systems.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103876"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884439","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":"Work and women's sacral spine acute injuries: an underestimated risk","authors":"Claudia Giliberti ,&nbsp;Silvana Salerno","doi":"10.1016/j.ergon.2025.103869","DOIUrl":"10.1016/j.ergon.2025.103869","url":null,"abstract":"<div><h3>Introduction</h3><div>Spine is the third most commonly injured anatomical region among Italian working women, after upper and lower limbs. Lumbar spine work injuries are well-known, particularly in the Healthcare sector, while no studies on sacral spine work-related injuries were found, although they represent a “silent epidemic” for general population, producing severe disabilities among women.</div></div><div><h3>Objective</h3><div>The aim of this study is to analyze sex/gender differences in compensated work-related sacral spine injuries in mainly female-dominated work sectors.</div></div><div><h3>Methods</h3><div>Compensated work-related acute sacral spine injuries among women and men<strong><u>,</u></strong> from the Italian Compensation Authority (Inail) database in the last five years, were studied in selected work sectors and the statistical analysis was performed as Incidence Rate Ratio (IRR) and Odds Ratio (OR) (p &lt; 0,05). Sacral spine work-related lesions such as bruises, dislocations and fractures were analyzed per sex/gender and work sectors.</div></div><div><h3>Results</h3><div>Women showed a statistically significant IRR for sacral spine work-related injuries in all the analyzed work sectors (IRR 2.22; CI95 % 2.11–2.34), especially in Catering, Cleaning and Trade. Women suffered more sacral fractures than men (OR 1.28; CI95 % 1.14–1.44), especially in Manufacturing (OR 1.47; CI95 % 1.08–1.99), where women are mainly employed in food processing. The role of work falls is discussed, together with the need of an intersectional ergonomic approach to prevent this underestimated risk among women.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103869"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840541","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":"An investigation of the ethical and social risks of wearable robots in the construction industry: A delphi study and focus group approach","authors":"Mariam Ayobami Tomori ,&nbsp;Omobolanle R. Ogunseiju ,&nbsp;Joshua Nsiah Addo Ofori ,&nbsp;Yong Cho","doi":"10.1016/j.ergon.2025.103873","DOIUrl":"10.1016/j.ergon.2025.103873","url":null,"abstract":"<div><div>The construction industry remains one of the most hazardous fields, with workers exposed to strenuous and repetitive tasks. Wearable robots such as exoskeletons are emerging as ergonomic interventions that enhance strength, reduce muscle fatigue and discomfort, yet they also introduce unintended social and ethical concerns. While health and safety risks have been explored, limited research has examined the social and ethical risks associated with exoskeleton use in construction. This study investigates these risks, their effects on workers’ safety, and their influence on wearable robot implementation. Exploratory factor analysis identified seven main categories of concern: design and inclusivity, safety and autonomy, social perception and acceptance, dependency and health, usability, data privacy and security, and economic burden. The results reveal a distinction between the perceived impact of these risks and their criticality for adoption. Although many risks were viewed as highly impactful, only those related to safety, accessibility, and equitable deployment were considered very critical. Identity-related risks, for instance, were seen as highly impactful but less critical for implementation. The findings support the need for ethical and socially responsible approaches to the design and deployment of exoskeletons in construction, with the aim of protecting workers, promoting equitable adoption, and improving the human-wearable robot experience.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103873"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884437","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":"Detection of perceived risk during partially automated driving on real road","authors":"Yanyi Li ,&nbsp;Yi Wang ,&nbsp;Xin Zhou ,&nbsp;Wei Zhang ,&nbsp;Jingyue Zheng","doi":"10.1016/j.ergon.2025.103842","DOIUrl":"10.1016/j.ergon.2025.103842","url":null,"abstract":"<div><div>With the rapid advancement of automated driving technologies, understanding drivers' perceived risk in real-road environments is crucial for the acceptance of automated vehicles (AVs) and ensuring safety. This study proposed a deep learning framework combining convolutional neural network (CNN) and long short-term memory (LSTM) to detect the perceived risk. The framework utilized vehicle, environmental, physiological, and facial data collected from real-road experiments involving partially automated vehicles. Potential high perceived risk events were captured via a smartphone. They were further classified into low/medium/high levels through subjective evaluation. Compared with other methods, the CNN-LSTM model performs the best, achieving an accuracy of 82.8 %, an F1 score of 83.6 %, and an AUC score of 86.5 %. Key features sensitive to perceived risk changes include the steering angle, motorcycle count, skin conductance level, root mean square of electromyographic signals, and eye-related features. The model and the findings of the study may contribute to improved the design of take-over request and driving style adjustment for automated vehicles to improve safety and user acceptance.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103842"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685325","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 novel driver monitoring and feedback system improves takeover performance in conditional automated driving","authors":"N. Zhang ,&nbsp;M. Fard ,&nbsp;S. Tohmuang ,&nbsp;J. Xu ,&nbsp;J.L. Davy ,&nbsp;S.R. Robinson","doi":"10.1016/j.ergon.2025.103862","DOIUrl":"10.1016/j.ergon.2025.103862","url":null,"abstract":"<div><div>As automated driving evolves, ensuring seamless human-vehicle interaction remains a critical challenge. Building upon authors’ previous study, the present study develops and investigates how a Driver Monitoring and Feedback System (DMFS) influences takeover performance, physiological responses, and user experience in conditional automated driving. Seventeen participants engaged in simulated driving sessions with two Non-Driving Related Tasks (NDRTs), namely working and resting, both with and without an active DMFS. Metrics were collected for driving performance, Heart Rate Variability (HRV) and subjective evaluations. The findings indicate that the DMFS mitigated the adverse effects of NDRTs on takeover performance by up to 47 %, particularly during resting conditions. Although the DMFS was generally perceived positively regarding effectiveness and accuracy, lower user experience scores suggest a need for a balance between functionality and user comfort. This study highlights the potential of DMFSs to enhance safety in automated driving, while also identifying challenges in maintaining driver readiness and optimising human-automation interaction. The results underscore the importance of developing adaptive, user-centric DMFS designs for future automated driving systems.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103862"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749069","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":"Clueless, careless, or lacking control? Understanding the psychological determinants of unsafe riding behavior among urban E-bike users","authors":"Tingru Zhang,&nbsp;Yinglin Wu,&nbsp;Yuanjia Zheng,&nbsp;Yanxuan Zhang,&nbsp;Da Tao,&nbsp;Disi Tian,&nbsp;Xingda Qu","doi":"10.1016/j.ergon.2025.103871","DOIUrl":"10.1016/j.ergon.2025.103871","url":null,"abstract":"<div><div>With the rapid adoption of e-bikes in urban transportation, traffic safety concerns associated with their use have become increasingly prominent. Unsafe riding behaviors have been identified as a major cause of e-bike-related crashes, yet the underlying psychological determinants remain insufficiently understood. This study aims to address a critical question: Are unsafe behaviors the result of e-biker riders being <em>clueless</em>, <em>careless</em>, or <em>lacking control</em>? To this end, a theoretical model incorporating safety knowledge, risk perception, and perceived behavioral control was developed to predict unsafe behaviors and was validated using data from a sample of 719 urban e-bike riders in China. In addition, multigroup analyses were conducted to examine how these relationships vary across occupational (regular vs. delivery riders) and age (≤30 vs. &gt;30 years) subgroups. The findings indicate that perceived behavioral contro<strong>l</strong> is the strongest predictor of unsafe riding behaviors, particularly pronounced among delivery riders. This suggests that a perceived lack of control, often attributed to external constraints, is a key antecedent of unsafe behaviors. Safety knowledge exerts an indirect effect on behavior via safety attitude and demonstrates a consistently negative influence across all subgroups. In contrast, the impact of risk perception differs significantly by age: while it helps mitigate unsafe behaviors among older riders, it is positively associated with such behaviors among younger riders, reflecting a “reverse careless” effect. These findings highlight the complexity of the psychological mechanisms underlying unsafe riding behaviors and highlight the need for targeted, subgroup-specific behavioral interventions.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103871"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840538","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":"Intelligent prediction of ergonomics evaluation metrics in human-AI collaboration based on machine learning","authors":"Jiabing Zhang,&nbsp;Qingxuan Jia,&nbsp;Siyi Li,&nbsp;Shiyu Zhang,&nbsp;Gang Chen","doi":"10.1016/j.ergon.2025.103851","DOIUrl":"10.1016/j.ergon.2025.103851","url":null,"abstract":"<div><div>Ergonomic evaluations in human-AI collaboration systems are often time-consuming, labor-intensive, and prone to bias. The complexity of formulas and factors complicates automated ergonomic evaluation. To address this issue, this paper proposes a machine learning-based framework for predicting ergonomics evaluation metrics. First, a human-AI collaboration experimental method is presented for ergonomics evaluation metric data collection. During the experiment, a total of 18 human-AI collaborative experiments were conducted, comprising 12 human-AI teams and 6 all-human teams, covering various tasks like Flight, Attack, etc. Then, four regression models-linear neural network, nonlinear deep neural network, random forest regressor with data augmentation, and random forest with k-fold cross-validation-are designed to predict ergonomics evaluation metrics. Data augmentation techniques are employed to expand the dataset and enhance the model’s generalization capability. The dataset grew from 72 to 72,000 samples for neural networks and 7200 for random forests through data augmentation. The random forest model was also trained with 6-fold cross-validation, and test sets for all models were derived from the original data. Finally, the models’ accuracy and reliability in predicting these metrics are comprehensively evaluated using relative absolute error, mean relative absolute error, root mean squared error, relative root mean squared error, and the coefficient of determination <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>, ensuring their validity. The results show that the proposed machine learning framework achieves high prediction accuracy, especially the data-augmented random forest model, which outperforms other models in terms of prediction accuracy. This enables effective automated ergonomic evaluations within human-AI collaboration systems and thus guides designing fluent and efficient human-AI teams.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103851"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658445","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":"Workstation ergonomics in the era of multi-monitor technology: A narrative review and survey","authors":"Andrea A. Vivaldi,&nbsp;David Claudio,&nbsp;Maria A. Velazquez,&nbsp;Laura Punnett","doi":"10.1016/j.ergon.2025.103863","DOIUrl":"10.1016/j.ergon.2025.103863","url":null,"abstract":"<div><div>Multi-monitor workstations are becoming more common, offering productivity gains and better workflow. However, their ergonomic impact is still not well understood, and current guidelines have not kept up with technology. This review looks at published studies on multi-monitor setups and finds mixed methods and results on productivity, user comfort, and musculoskeletal risk. Differences in study design and reporting make it hard to reach clear conclusions, leaving gaps in guidance. To add real-world data, we surveyed 208 computer users (85 % confidence level, ±5 % margin of error). We used descriptive statistics and K-means clustering to explore patterns. About 65 % of respondents used multiple monitors. Out of 135 participants using multiple monitors, two-monitor setups were most common (71 %), with L-shaped layouts used by half of multi-monitor users. Cluster analysis showed four main user types, from triple-monitor “power users” (about 8.5 h/day) to laptop-focused dual-monitor users (about 7.2 h/day). These groups differed in screen size, layout, and how time was split between monitors. Current research lacks consistency and does not address newer options like ultrawide monitors, making practical guidance difficult. Survey data further reveal a growing reliance on dual-monitor configurations, with users likely adopting suboptimal arrangements, which may contribute to musculoskeletal discomfort. This study highlights the urgency of developing updated ergonomic recommendations and research that balance efficiency with user well-being, ensuring that productivity gains do not come at the cost of discomfort or injury.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103863"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798267","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}