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Individual and Organizational Resilience: Relationships, Antecedents, and Consequences
IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Pub Date : 2025-02-05 DOI: 10.1002/hfm.21063
Claudia Cristina Alvares Beltrão de Medeiros, Tarcisio Abreu Saurin

Resilience in socio-technical systems has a myriad of manifestations and outcomes that are often not made explicit in the literature. This drawback might be a source of misunderstandings and hinder the design of work systems supportive of desirable resilient performance. Two crucially distinctive manifestations refer to individual and organizational resilience. This study presents a model of how these two types relate to each other and how they relate to antecedents and consequences of resilience. To this end, we carried out a case study of freight road transport, emphasizing the truck drivers' perspectives. Data collection included 14 interviews with drivers and managers of logistics operations, in addition to non-participant observations of drivers' workplaces. Based on a thematic analysis, the model was developed consisting of seven themes: work constraints, investments, individual resilience practices, organizational resilience practices, operational efficiency, human costs, and overall costs. The first two themes are antecedents and the last three are consequences of resilience. The main relationships between the themes are presented as seven propositions for theory-testing. The model suggests three main approaches for a balanced distribution between individual and organizational resilience. These approaches consist of: tackling work constraints, especially production pressures; investing in organizational practices adopting a long-term view; and promoting individual practices that proactively support health and safety. The truck drivers' study offers examples of the applicability of these approaches.

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引用次数: 0
Combined Effects of Ambient Light and Color on Cognitive Performance and Sleepiness in a Simulated Working Environment
IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Pub Date : 2025-01-30 DOI: 10.1002/hfm.21061
Reza Shahidi, Rostam Golmohammadi, Ebrahim Darvishi, Mohsen Aliabadi, Mohammad Babmiri, Javad Faradmal

This study investigated the combined effect of correlated color temperature and wall color on subjective sleepiness and cognitive performance in a simulated workplace. Six combined conditions were designed by partitioning a room into three booths with the same dimensions in three colors of blue, red, and white and two cool and warm light: color temperatures of 6000 and 3000°K (Red × 3000, Red × 6000, Blue × 3000, Blue × 6000, White × 3000, and White × 6000) during the day. Thirty-three healthy males aged 21–35 were recruited. They were asked to conduct cognitive tests in three workload levels and finally estimate the subjective sleepiness level. The findings indicated that cool light had a more significant effect on reducing sleepiness when compared to warm light, particularly in white and blue colors. However, this effect was not observed in the case of red color. The rate of sleepiness was higher in the cool light and red color compared to warm light. The blue color slightly decreased sleepiness compared to the white and red colors. The mean correct responses of the cognitive tests in cool light and white color were more than in other conditions. Moreover, the effect of blue and red were higher in the correct response percentages, compared to white in warm and cool light. There were no significant differences in reaction time between two different lights in all colors. However, reaction time was better in blue than in two other colors. To conclude, designing a work environment with a combination of cool light and blue-colored walls may improve employee alertness and performance.

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引用次数: 0
Stakeholder Risk Perception About Heat: An Interview-Based Study Among Outdoor Workers in South India
IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Pub Date : 2025-01-23 DOI: 10.1002/hfm.21062
Sneha Ragupathy, Shanmukh Pranavi Annadata, P. K. Latha, Sarada Satyamoorthy Garg, Vidhya Venugopal

Heat waves in Southeast Asia are expected to intensify in the upcoming decades thereby raising the vulnerability of at-risk workers to heat-related illnesses (HRIs). Identifying and strengthening workers' self-protection knowledge is crucial to effective heat adaptation and management. The study aimed to investigate outdoor workers' perceptions of heat-related risks, changes in protective behavior during hotter seasons, knowledge and awareness of regional heat action plans (HAPs), and protection measures. It aimed to find effective means of communication and strategies for improving heat protection among these workers. We used a validated and structured mixed-method survey questionnaire and one-on-one interviews to assess 140 outdoor workers' heat-related risk perceptions, protective behavior changes during hot seasons, and knowledge of regional HAPs protective measures in April–July 2022. The estimated worker's seasonal average WBGT exposure using meteorological data was 34.4°C ± 0.02°C, which exceeded acceptable limits. Heat was a big concern for outdoor workers, the study revealed. Workers believed that knowing about heat hazards could help reduce individual risks. Many workers were aware of the heat's health risks but felt the nature of their jobs prevented them from taking precautions. Some workers assumed that business owners' concerns about productivity and cooling costs would prevent government measures to safeguard at-risk employees from succeeding. Workers responded more positively to business owners' HAP communications. Workers are more likely to use practical, simple sector-based warning HAPs. We urgently need management policy reforms to protect millions of workers in low- and middle-income countries (LMICs), and the implementation of cost-effective, practical, and sustainable heat protection infrastructure and behavioral change solutions through trusted channels is crucial.

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引用次数: 0
The Impact of Individual and Team-Level Variables on Burnout in Healthcare Providers 个人和团队层面的变量对医护人员职业倦怠的影响
IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-11-25 DOI: 10.1002/hfm.21060
Logan M. Gisick, Jenna Korentsides, Joseph R. Keebler, Elizabeth H. Lazzara, Philip E. Greilich, Susan Matulevicius

This study evaluates the relationships between individual and team-level factors in influencing burnout among clinical healthcare providers. Focusing on psychological safety, perceived autonomy, perceived team effectiveness, and emotional intelligence, the research aims to understand how these elements contribute to the prevalence and severity of burnout symptoms. Using electronic questionnaires analyzed through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), the study sampled 180 healthcare providers from one large US medical center. The study results found that psychological safety significantly decreases levels of burnout, particularly emotional exhaustion. The results on team effectiveness suggest a complex relationship with burnout, with different dimensions having varied impacts. The study did not find support for the negative prediction of burnout by perceived autonomy and emotional intelligence, contrary to expectations based on prior research. These findings have practical implications for healthcare management, stressing the importance of psychological safety and effective team dynamics in reducing burnout. Overall, this study contributes significantly to understanding burnout in healthcare, emphasizing the critical role of team structures and individual emotional resilience in managing workplace well-being.

本研究评估了影响临床医疗服务提供者职业倦怠的个人因素和团队因素之间的关系。研究重点关注心理安全、自主感知、团队效率感知和情商,旨在了解这些因素如何导致职业倦怠症状的发生率和严重程度。该研究使用电子问卷,通过确认性因子分析(CFA)和结构方程模型(SEM)进行分析,从美国一家大型医疗中心的 180 名医疗服务提供者中抽取样本。研究结果发现,心理安全能显著降低职业倦怠水平,尤其是情绪衰竭。团队有效性的研究结果表明,心理安全与职业倦怠之间存在复杂的关系,不同的维度会产生不同的影响。研究没有发现感知自主性和情商对职业倦怠有负面预测作用,这与之前研究的预期相反。这些发现对医疗保健管理具有实际意义,强调了心理安全和有效的团队动态对减少职业倦怠的重要性。总之,这项研究对理解医疗保健行业的职业倦怠有很大帮助,强调了团队结构和个人情绪恢复能力在管理职场幸福感方面的关键作用。
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引用次数: 0
Towards Holistic Functional Task Analysis 实现整体功能任务分析
IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-11-07 DOI: 10.1002/hfm.21059
Vahid Salehi, Paul M. Salmon, Catherine Burns, Alexis McGill, Doug Smith, Brian Veitch

Task analysis (TA) can contribute to work systems design, accident investigation, risk assessment, human error identification, planning, and training. Despite the advantages of existing sequential and hierarchical methods, they decompose tasks into their structure and focus on the order in which tasks are accomplished. They do not trace all interactions among elements/subtasks/operations at different levels. As the complexity of tasks increases, not keeping track of all interactions may result in poor, unwanted outcomes. This research introduces a different approach to TA that decomposes tasks into their constituent functions, describes the functionality of the overall work system, traces (dynamic nonlinear) interactions among functions, and highlights the role of functional variability in forming emergent outcomes. This approach to TA is called functional task analysis (FTA). A case study on nursing work was used to demonstrate the suitability of the FTA approach. The findings of this study show that the FTA approach contributes to task modeling by building a nonsequential, nonhierarchical functional model of a complex task considering dynamic, nonlinear interactions among functions. The FTA also contributes to task description by explaining different ways a task can be accomplished. It also increases the understanding, interpretation, and analysis of how changes in work conditions shape good/acceptable and poor/unacceptable outcomes. The FTA can complement the TA by adding some aspects, including functionality, nonlinearity, dynamics, and emergence, that the TA does not normally consider. The findings highlight how the functional approach to TA can be deployed as an alternative (or complement) to other task analysis methods.

任务分析(TA)有助于工作系统设计、事故调查、风险评估、人为错误识别、规划和培训。尽管现有的顺序法和分层法具有优势,但它们将任务分解为不同的结构,并侧重于完成任务的顺序。它们无法跟踪不同层次的元素/子任务/操作之间的所有交互。随着任务复杂性的增加,不跟踪所有交互可能会导致不良的、不必要的结果。本研究介绍了一种不同的任务分配方法,它将任务分解为其组成功能,描述整个工作系统的功能,追踪功能之间的(动态非线性)互动,并强调功能变异性在形成突发结果中的作用。这种 TA 方法被称为功能任务分析(FTA)。为证明 FTA 方法的适用性,我们对护理工作进行了案例研究。研究结果表明,FTA 方法通过建立复杂任务的非序列、非等级功能模型,考虑功能间的动态、非线性相互作用,有助于任务建模。通过解释完成任务的不同方式,FTA 还有助于任务描述。它还能加深理解、解释和分析工作条件的变化如何形成好的/可接受的和差的/不可接受的结果。功能性方法可以补充技术援助,增加一些技术援助通常不考虑的方面,包括功能性、非线性、动态性和突发性。研究结果凸显了如何采用功能性方法来替代(或补充)其他任务分析方法。
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引用次数: 0
An expository analysis of biomechanical and subjective impacts induced by shoe inserts in asymptomatic subjects: A systematic review on functionality and mechanisms of action 对无症状受试者鞋垫引起的生物力学和主观影响的说明性分析:关于功能和作用机制的系统综述
IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-10-21 DOI: 10.1002/hfm.21058
Waseem Ahmad, Md Sarfaraz Alam

This systematic review explores the biomechanical and subjective effects of shoe inserts, including foot orthotics (FOs) and insoles, in asymptomatic subjects. Aimed at understanding their implications, the review poses two key research questions: (i) the influence of shoe inserts on lower extremity biomechanics and subjective perception and (ii) the effects of different design characteristics on these aspects. Following Preferred Reporting of Systematic Reviews and Meta-Analysis guidelines, a meticulous search of Scopus and PubMed from August 2022 to March 2023 yielded 34 articles, with 26 focusing on biomechanical effects and eight on comfort effects. The studies, conducted during static and dynamic activities, such as standing, walking, jogging, running, jumping, and cycling, reveal significant reductions in rearfoot eversion, knee joint forces, and lower extremity muscle forces through postings and wedging in FOs. Changes in stiffness impact rearfoot kinematics, plantar pressure distribution, and ankle–foot power distribution. Conversely, surface texture and arch variations demonstrate limited significance. FOs and shoe inserts, characterized by geometric, material, location, size, and fabrication features, effectively regulate forces and moments on the lower extremity. This control promotes uniform plantar pressure distribution and enhances comfort during various activities. These insights benefit manufacturers, clinicians, and stakeholders, providing a deeper understanding of the positive benefits of FOs and shoe inserts. However, further well-designed studies on clinical populations are necessary to validate these findings and establish their clinical efficacy, as the current focus remains on healthy subjects.

本系统综述探讨了鞋垫(包括足部矫形器和鞋垫)对无症状受试者的生物力学和主观影响。为了了解其影响,该综述提出了两个关键研究问题:(i) 鞋垫对下肢生物力学和主观感觉的影响;(ii) 不同设计特点对这些方面的影响。根据《系统综述和元分析首选报告指南》,从 2022 年 8 月到 2023 年 3 月,我们对 Scopus 和 PubMed 进行了细致的搜索,共搜索到 34 篇文章,其中 26 篇侧重于生物力学影响,8 篇侧重于舒适性影响。这些研究是在站立、行走、慢跑、跑步、跳跃和骑自行车等静态和动态活动中进行的,研究结果表明,通过在 FOs 中进行张贴和楔入,后足外翻、膝关节力和下肢肌肉力显著降低。硬度的变化会影响后足运动学、足底压力分布和踝足力量分布。相反,表面纹理和足弓变化的影响有限。FOs 和鞋垫的几何形状、材料、位置、尺寸和制造特点可有效调节下肢的力和力矩。这种控制可促进足底压力的均匀分布,并提高各种活动中的舒适度。这些见解使制造商、临床医生和利益相关者受益匪浅,让他们对 FOs 和鞋垫的积极益处有了更深入的了解。然而,由于目前的研究重点仍然是健康受试者,因此有必要在临床人群中开展进一步的精心设计的研究,以验证这些发现并确定其临床疗效。
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引用次数: 0
Evaluating human error probability in maintenance task: An integrated system dynamics and machine learning approach 评估维护任务中的人为错误概率:综合系统动力学和机器学习方法
IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-10-16 DOI: 10.1002/hfm.21057
Vahideh Bafandegan Emroozi, Mostafa Kazemi, Alireza Pooya, Mahdi Doostparast

Human error is often implicated in industrial accidents and is frequently found to be a symptom of broader issues within the sociotechnical system. Therefore, research exploring human error during maintenance activities is important. This article aims to assess the probability of human error in maintenance tasks at a cement factory using the Cognitive Reliability and Error Analysis Method and System Dynamics modeling. Given that human error probability (HEP) is influenced by various common performance conditions (CPCs) and their sub-factors, and changes dynamically in response to other variables, the SD method offers a practical approach for estimating and predicting human error behavior over time. This study identifies and quantifies the variables affecting HEP, explores their interactions and feedback in maintenance tasks, and assesses the associated costs. The machine learning technique is then used to estimate the relationship between HEP and these costs. The optimal value of the HEP function, 0.000772, is determined by identifying the minimum point of a cubic function, thereby minimizing associated costs and occupational accidents. Determining the optimal HEP is crucial for minimizing excessive costs and investing in improved ergonomics and CPCs for better performance. This addresses a significant gap in existing research where the impact of human error on maintenance tasks has not been estimated as a function. Furthermore, three scenarios are presented to help managers allocate the organization's budget more effectively.

人为错误往往与工业事故有关,而且经常被认为是社会技术系统中更广泛问题的一种表现。因此,研究维护活动中的人为错误非常重要。本文旨在利用认知可靠性和错误分析方法以及系统动力学建模,评估水泥厂维护任务中的人为错误概率。鉴于人为错误概率(HEP)受各种常见性能条件(CPC)及其子因素的影响,并随着其他变量的变化而动态变化,因此 SD 方法为估计和预测随时间变化的人为错误行为提供了一种实用的方法。本研究确定并量化了影响 HEP 的变量,探讨了它们在维护任务中的相互作用和反馈,并评估了相关成本。然后使用机器学习技术来估算 HEP 与这些成本之间的关系。通过确定立方函数的最小点,确定了 HEP 函数的最佳值 0.000772,从而将相关成本和职业事故降至最低。确定最佳 HEP 对于最大限度地降低过高成本以及投资于改进人体工程学和 CPC 以提高绩效至关重要。这弥补了现有研究中的一个重大缺陷,即没有将人为失误对维护任务的影响作为一个函数进行估算。此外,本文还提出了三种方案,以帮助管理人员更有效地分配组织预算。
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引用次数: 0
Enhancing experiential learning through virtual reality: System design and a case study in additive manufacturing 通过虚拟现实加强体验式学习:系统设计和增材制造案例研究
IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-09-27 DOI: 10.1002/hfm.21055
Rafia Rahman Rafa, Taufiq Rahman, Md Humaun Kobir, Yiran Yang, Shuchisnigdha Deb

The recent advancement in additive manufacturing (AM) leads to an extensive need for an industrial workforce in the near future. Workforce training in AM requires expensive capital investment for installing and maintaining this technology and proper knowledge about potential safety hazards. Traditional classroom settings often fail to bridge the critical gap between textbook learning and practical applications. Virtual reality (VR) training can simulate real-world scenarios in a safe and controlled environment and improve student involvement to foster practical learning. In this paper, a virtual training platform for 3D printing has been developed and studied to improve AM education. The developed environment contains a selective laser sintering printer, a preparation station with necessary supplies, a control panel for process planning, and a post-processing station. This platform provides students with excellent learning opportunities to gain hands-on experiences and critical engineering skills on operating process parameters and safety measures. Undergraduate students majoring in industrial engineering were exposed to this learning approach to enhance their engagement and cognitive processing skills. Students' attentions were measured using eye metrics (fixation duration and preference index), and their exposure experiences were collected through the simulation sickness questionnaire, presence questionnaire, and system usability scale. Pre- and post-VR training questionnaires and performance metrics (task completion time and accuracy) evaluated students' learning outcomes. Results provide valuable insights into students' attention, performance, and satisfaction with virtual training environments. Users' gaze behavior and subjective responses revealed many challenges that will help future researchers develop assistive instructions within this virtual educational platform.

最近,增材制造(AM)技术的发展导致了在不久的将来对工业劳动力的广泛需求。AM 方面的劳动力培训需要昂贵的资金投入来安装和维护这项技术,还需要适当了解潜在的安全隐患。传统的课堂教学往往无法弥合课本学习与实际应用之间的关键差距。虚拟现实(VR)培训可以在安全可控的环境中模拟真实世界的场景,提高学生的参与度,促进实践学习。本文开发并研究了一个 3D 打印虚拟培训平台,以改进 AM 教育。所开发的环境包含一台选择性激光烧结打印机、一个配备必要耗材的准备站、一个用于工艺规划的控制面板和一个后处理站。该平台为学生提供了绝佳的学习机会,让他们获得实践经验以及操作工艺参数和安全措施方面的关键工程技能。工业工程专业的本科生接触了这种学习方法,以提高他们的参与度和认知处理能力。学生们的注意力通过眼部指标(固定持续时间和偏好指数)进行测量,他们的接触体验通过模拟病症问卷、临场感问卷和系统可用性量表进行收集。虚拟现实培训前后的调查问卷和绩效指标(任务完成时间和准确性)对学生的学习成果进行了评估。结果为了解学生在虚拟培训环境中的注意力、表现和满意度提供了宝贵的信息。用户的注视行为和主观反应揭示了许多挑战,这将有助于未来的研究人员在这一虚拟教育平台中开发辅助指令。
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引用次数: 0
Analysis on pulse rate variability for pilot workload assessment based on wearable sensor 基于可穿戴传感器的飞行员工作量评估脉搏变异性分析
IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-09-27 DOI: 10.1002/hfm.21053
Yunbiao Wang, Chenyang Zhang, Chenglin Liu, Kun Liu, Fang Xu, Jixue Yuan, Chaozhe Jiang, Chuang Liu, Weiwei Cao

The workload levels of pilots directly affect their flight performance and the safety of the whole flight. To explore the real-time workload of pilots in different flight phases (takeoff, cruise, and landing), this paper leveraged National Aeronautics and Space Administration Task Load Index (NASA-TLX), a subjective evaluation scale, and PhotoPlethysmoGraphy (PPG) signals of 21 participants using a flight simulator and a wearable sensor. First, the workloads of pilots under different phases were explored by the NASA-TLX scales; secondly, the pulse rate variability (PRV) features were selected by variance analysis and random forest importance evaluation; finally, the performances of the k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were compared for workload levels identification. It is shown that the workloads are ranked as follows: landing > takeoff > cruise. SDNN, CVCD, CVNNI, LF, TP, SD2, and SD2/SD1 were used as selected features with significant differences in different flight phases. In addition, machine learning models can effectively identify pilot workloads, and feature selection enhances the performance of both KNN and RF classifiers. The best identification of workload was achieved using the selected PRV features as inputs to the KNN classifier, with an average accuracy of 88.9%. Our results indicate that the KNN classifier and PRV features are suitable for identifying pilot workload. The pilot workload is highest during the landing phase, which provides a reference for flight safety management. The findings from this research could contribute to developing a robust pilot workload detection system and improve current flight operation safety regulations.

飞行员的工作量水平直接影响其飞行性能和整个飞行的安全性。为了探索飞行员在不同飞行阶段(起飞、巡航和着陆)的实时工作量,本文利用美国国家航空航天局的任务负荷指数(NASA-TLX)、主观评价量表以及21名参与者使用飞行模拟器和可穿戴传感器获得的PPG(PhotoPlethysmoGraphy)信号。首先,通过 NASA-TLX 量表探讨了飞行员在不同阶段的工作负荷;其次,通过方差分析和随机森林重要性评估选择了脉率变异性(PRV)特征;最后,比较了 k-近邻(KNN)、随机森林(RF)和支持向量机(SVM)在工作负荷水平识别方面的性能。结果表明,工作负荷的排序如下:着陆> 起飞> 巡航。在不同飞行阶段,SDNN、CVCD、CVNNI、LF、TP、SD2 和 SD2/SD1 被选作具有显著差异的特征。此外,机器学习模型能有效识别飞行员的工作量,而特征选择能提高 KNN 和 RF 分类器的性能。使用选定的 PRV 特征作为 KNN 分类器的输入,实现了最佳的工作量识别,平均准确率为 88.9%。结果表明,KNN 分类器和 PRV 特征适用于识别飞行员的工作量。飞行员在着陆阶段的工作量最大,这为飞行安全管理提供了参考。这项研究的结果有助于开发一个强大的飞行员工作量检测系统,并改善目前的飞行运行安全法规。
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引用次数: 0
A novel deep learning-based technique for driver drowsiness detection 基于深度学习的新型驾驶员嗜睡检测技术
IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Pub Date : 2024-09-27 DOI: 10.1002/hfm.21056
Prithwijit Mukherjee, Anisha Halder Roy

Every year, many people lose their lives because of road accidents. It is evident from statistics that drowsiness is one of the main causes of a large number of car accidents. In our research, we wish to solve this major problem by measuring the drowsiness level of the human brain while driving. The study aims to develop a novel technique to detect different alertness levels (i.e., awake, moderately drowsy, and maximally drowsy) of a person while driving. A hybrid model using a stacked autoencoder and hyperbolic tangent Long Short-Term Memory (TLSTM) network with attention mechanism is designed for this purpose. The designed model uses different biopotential signals, such as electroencephalography (EEG), facial electromyography (EMG), and different biomarkers, such as pulse rate, respiration rate galvanic skin response, and head movement to detect a person's alertness level. Here, the stacked autoencoder model is used for automated feature extraction. TLSTM is used to predict a person's alertness level using stacked autoencoder network-extracted features. The proposed model can classify awake, moderately drowsy, and maximally drowsy states of a person with accuracies of 99%, 98.3%, and 98.6%, respectively. The novel contributions of the paper includes (i) incorporation of an attention mechanism into the TLSTM network of the proposed hybrid model to focus on the emphatic states to enhance classification accuracy, and (ii) utilization of EEG, facial EMG, pulse rate, respiration rate, galvanic skin reaction, and head movement pattern to assess a person's alertness level.

每年都有许多人因交通事故丧生。从统计数据可以看出,瞌睡是导致大量车祸的主要原因之一。在我们的研究中,我们希望通过测量驾驶时人脑的瞌睡程度来解决这一重大问题。这项研究旨在开发一种新型技术,以检测驾驶时人的不同警觉程度(即清醒、中度昏昏欲睡和极度昏昏欲睡)。为此,研究人员设计了一个混合模型,该模型采用了堆叠式自动编码器和双曲正切长短期记忆(TLSTM)网络以及注意力机制。所设计的模型使用不同的生物电位信号(如脑电图(EEG)、面部肌电图(EMG))和不同的生物标记(如脉搏率、呼吸率、皮肤电反应和头部运动)来检测人的警觉程度。在这里,堆叠自动编码器模型被用于自动特征提取。利用堆叠自动编码器网络提取的特征,TLSTM 可用于预测人的警觉程度。所提出的模型可对人的清醒、中度昏睡和极度昏睡状态进行分类,准确率分别为 99%、98.3% 和 98.6%。本文的新贡献包括:(i) 在所提出的混合模型的 TLSTM 网络中加入注意力机制,以关注强调状态,从而提高分类准确率;(ii) 利用脑电图、面部肌电图、脉搏率、呼吸率、皮肤电反应和头部运动模式来评估人的警觉程度。
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Human Factors and Ergonomics in Manufacturing & Service Industries
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