IISE transactions on occupational ergonomics and human factors最新文献_第4页

From Anger Detection to Intensity Modeling: A Two-Stage Machine Learning Approach Using Driving Performance and Physiological Signals. 从愤怒检测到强度建模：使用驾驶性能和生理信号的两阶段机器学习方法。

IF 1.8

IISE transactions on occupational ergonomics and human factors Pub Date : 2026-05-06 DOI: 10.1080/24725838.2026.2665228

Manhua Wang, Haoyu Teng, Myounghoon Jeon

{"title":"From Anger Detection to Intensity Modeling: A Two-Stage Machine Learning Approach Using Driving Performance and Physiological Signals.","authors":"Manhua Wang, Haoyu Teng, Myounghoon Jeon","doi":"10.1080/24725838.2026.2665228","DOIUrl":"https://doi.org/10.1080/24725838.2026.2665228","url":null,"abstract":"OCCUPATIONAL APPLICATIONSThis study demonstrated that driver anger can be feasibly modeled across three intensity levels using combined driving performance metrics and physiological signals. A two-stage machine learning framework, which first determines anger presence and then classifies its intensity, substantially improved accuracy and reduced neutral state misclassification compared to a single-stage four-class model. These findings have direct implications for reducing safety risks and promoting long-term health for workers whose jobs involve extensive driving (e.g., commercial drivers, bus operators), who encounter anger-eliciting situations more frequently than non-occupational drivers. Integrating anger-intensity detection into driver monitoring systems can enable adaptive, context-aware assistance systems that consider both intervention timing and emotional intensity. Aggregated emotion-intensity information may also inform operational decisions (e.g., dispatch assignments and break scheduling) by identifying periods when drivers may benefit from reduced demands or modified tasks. These implications can enhance fleet safety programs and support worker well-being by reducing exposure to emotionally demanding conditions.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":" ","pages":"1-14"},"PeriodicalIF":1.8,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Integrated Open-Source Framework for Automated Analysis of Driver Activity, Glance, and Vehicle Automation Mode. 一个集成的开源框架，用于自动分析驾驶员活动，Glance和车辆自动化模式。

IF 1.8

IISE transactions on occupational ergonomics and human factors Pub Date : 2026-04-23 DOI: 10.1080/24725838.2026.2642716

Praneet Sahoo, David Strayer, Francesco Biondi

引用次数: 0

Safety and Mental Health Challenges in Emergency Medical Services: A Qualitative Investigation of Rural Paramedic Experiences. 紧急医疗服务中的安全和心理健康挑战：农村护理人员经验的定性调查。

IF 1.8

IISE transactions on occupational ergonomics and human factors Pub Date : 2026-04-01 Epub Date: 2025-12-26 DOI: 10.1080/24725838.2025.2600979

Christopher McGlynn, Avishek Choudhury

{"title":"Safety and Mental Health Challenges in Emergency Medical Services: A Qualitative Investigation of Rural Paramedic Experiences.","authors":"Christopher McGlynn, Avishek Choudhury","doi":"10.1080/24725838.2025.2600979","DOIUrl":"10.1080/24725838.2025.2600979","url":null,"abstract":"Background: Paramedics experience high levels of stress, burnout, and mental health concerns that may also compromise safety for both workers and patients. Although these issues have been examined broadly, few qualitative studies have focused on rural U.S. paramedics.Purpose: To explore how rural paramedics perceive sources of occupational stress, mental health challenges, and how these factors contribute to safety risks.Method: Semi-structured interviews were conducted with 24 paramedics in rural Louisiana. Interviews were guided by the Systems Engineering Initiative for Patient Safety (SEIPS) framework and analyzed using thematic analysis to generate codes, subthemes, and overarching themes.Results: Seven major themes were identified: work system processes, occupational stressors, organizational conditions, coping and support strategies, impacts on performance and safety, help-seeking and stigma, and mental health outcomes. Participants described excessive workloads, inadequate staffing, limited resources, and poor management practices as key stressors. Stress and fatigue were linked to cognitive errors, near misses, and driving incidents. Coping strategies relied heavily on individual or informal peer approaches, while stigma and cultural norms discouraged help-seeking. Reported mental health outcomes included burnout, emotional exhaustion, hypervigilance, and suicidal ideation.Conclusion: Occupational stress among rural paramedics represents both a mental health concern and an occupational safety hazard. Addressing staffing, fatigue, stigma, and organizational support as safety priorities may reduce risks for paramedics and patients alike.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":" ","pages":"103-115"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145835692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An In-Field Evaluation of Forearm Muscle Activity During a Manual Weld Task While Using a Soft Hand Exoskeleton. 在使用软手外骨骼时，手工焊接任务期间前臂肌肉活动的现场评估。

IF 1.8

IISE transactions on occupational ergonomics and human factors Pub Date : 2026-04-01 Epub Date: 2026-02-11 DOI: 10.1080/24725838.2026.2628733

Daniel Cousins, Ryan Porto, Michael Holmes, Joel Cort

引用次数: 0

Sensation Seeking, Physiological Arousal, and Emotion Regulation Predict Risk-Taking Behavior? A Preliminary Flight-Simulation Study. 感觉寻求、生理唤醒和情绪调节预测冒险行为？初步飞行模拟研究。

IF 1.8

IISE transactions on occupational ergonomics and human factors Pub Date : 2026-04-01 Epub Date: 2026-04-08 DOI: 10.1080/24725838.2026.2645647

Shan Gao, Tianyuan Liu, Yu Bai, Lei Wang

引用次数: 0

Asymmetric Manual Arm Strength Evident Only in Right-Handed Individuals. 不对称的手臂力量仅在右撇子个体中明显。

IF 1.8

IISE transactions on occupational ergonomics and human factors Pub Date : 2026-04-01 Epub Date: 2026-02-13 DOI: 10.1080/24725838.2026.2626494

Gillian E Slade, Michael W B Watterworth, Fahima Wakeely, Ryan C A Foley, Nicholas J La Delfa

引用次数: 0

Human Factors Influencing Trust in Healthcare Artificial Intelligence: Systematic Literature Review. 影响医疗人工智能信任的人为因素：系统文献综述。

IF 1.8

IISE transactions on occupational ergonomics and human factors Pub Date : 2026-04-01 Epub Date: 2026-02-11 DOI: 10.1080/24725838.2026.2625655

Avishek Choudhury, Hamid Shamszare

引用次数: 0

A Machine Learning Approach to Predict Objective and Subjective Low Back Fatigue Using Postural Control Features During Sustained Trunk Flexion. 机器学习方法预测客观和主观腰背疲劳使用姿势控制特征在持续躯干弯曲。

IF 1.8

IISE transactions on occupational ergonomics and human factors Pub Date : 2026-03-27 DOI: 10.1080/24725838.2026.2646995

Sang Hyeon Kang, Jaejin Hwang, Mostafa Etebar Zadeh

{"title":"A Machine Learning Approach to Predict Objective and Subjective Low Back Fatigue Using Postural Control Features During Sustained Trunk Flexion.","authors":"Sang Hyeon Kang, Jaejin Hwang, Mostafa Etebar Zadeh","doi":"10.1080/24725838.2026.2646995","DOIUrl":"https://doi.org/10.1080/24725838.2026.2646995","url":null,"abstract":"OCCUPATIONAL APPLICATIONSThis study investigated whether tree-based machine learning algorithms can predict objective (EMG-based) and subjective (Borg CR10) low back fatigue during a sustained trunk flexion, with and without back-support exosuit assistance, using postural control features. The results revealed that all tree-based algorithms reasonably predicted objective and/or subjective fatigue during sustained trunk flexion (F1-score range: 74.1%-97.7%), with Extra Trees achieving the highest mean F1-score (91.4%). Fatigue detection was more accurate with subjective or combined fatigue labeling than with objective fatigue labeling alone, and exosuit assistance improved model precision and F1-score. Feature selection identified vertical peak and mean ground reaction forces as the most influential predictors. These results suggest that noninvasive postural control monitoring via smart insoles or force-sensing shoes, combined with machine learning, can enable real-time assessment of fatigue during trunk flexion, supporting timely ergonomic interventions and workload adjustments, or exoskeleton use to mitigate the risk of low back disorders.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":" ","pages":"1-14"},"PeriodicalIF":1.8,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147534921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpersonal Physiological Synchrony in Mixed Reality Manufacturing Environments: Evaluating Shared Mental Models in Dyadic and Triadic Teams. 混合现实制造环境中的人际生理同步性：评估二元和三元团队中的共享心智模型。

IF 1.8

IISE transactions on occupational ergonomics and human factors Pub Date : 2026-03-27 DOI: 10.1080/24725838.2026.2645067

Khalid Bello, Israa Azzam, Farid El Breidi, Faisal Aqlan

{"title":"Interpersonal Physiological Synchrony in Mixed Reality Manufacturing Environments: Evaluating Shared Mental Models in Dyadic and Triadic Teams.","authors":"Khalid Bello, Israa Azzam, Farid El Breidi, Faisal Aqlan","doi":"10.1080/24725838.2026.2645067","DOIUrl":"https://doi.org/10.1080/24725838.2026.2645067","url":null,"abstract":"OCCUPATIONAL APPLICATIONSThis study explores the relationship between interpersonal physiological synchrony and shared mental models (SMMs) in collaborative mixed reality (MR) manufacturing environments. By integrating heart rate (HR) and heart rate variability (HRV) data with team-level SMM assessments, we show that real-time physiological alignment serves as a meaningful correlate of team cognition during virtual collaboration. Physiological synchrony measures also distinguished high- and low-performing teams, indicating potential value for monitoring team effectiveness. These findings have direct implications for occupational training and performance monitoring in high-stakes manufacturing contexts. Physiological synchrony offers practitioners a non-intrusive, real-time indicator of interaction quality complementing conventional approaches. Our findings further suggest that triadic teams exhibited stronger physiological alignment and more robust SMMs than dyads, informing decisions about team structuring and collaborative task design. Incorporating physiological monitoring into MR training platforms can identify breakdowns in shared understanding and provide adaptive feedback, enabling targeted interventions to improve coordination, safety, and productivity.","PeriodicalId":73332,"journal":{"name":"IISE transactions on occupational ergonomics and human factors","volume":" ","pages":"1-18"},"PeriodicalIF":1.8,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147534985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Behavioral Reliance on AI-Based Depression Treatment Recommendations Among Engineering Graduate Students: A Pilot Study Using EEG Signals. 预测工程学研究生对基于人工智能的抑郁症治疗建议的行为依赖：一项使用脑电图信号的初步研究。

IF 1.8

IISE transactions on occupational ergonomics and human factors Pub Date : 2026-03-07 DOI: 10.1080/24725838.2026.2638568

Yeganeh Shahsavar, Avishek Choudhury

引用次数: 0