Human Factors and Ergonomics in Manufacturing & Service Industries最新文献

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.

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.

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.

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.

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.

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.