搜索结果：IISE transactions on occupational ergonomics and human factors

Artificial intelligence (AI)-based models used to improve different fields including healthcare, and finance. One of the field that receive advantages of AI is automation. However, it is important to consider human factors in application of AI in automation. This paper reports on a systematic review of the published studies used to investigate the application of AI in PM. This comprehensive systematic review used ScienceDirect to identify relevant articles. Of the 422 articles found, 40 met the inclusion and exclusion criteria and were used in the review. Selected articles were classified based on categories of human factors and areas of application. The results indicated that application of AI in automation with respect to human factors could be divided into three areas of physical ergonomics, cognitive ergonomic and organizational ergonomics. The main areas of application in physical and cognitive ergonomics are including transportation, User experience, and human-machine interactions.

In recent years, the potential applications of Large Multimodal Models (LMMs) in fields such as healthcare, social psychology, and industrial design have attracted wide research attention, providing new directions for human factors research. For instance, LMM-based smart systems have become novel research subjects of human factors studies, and LMM introduces new research paradigms and methodologies to this field. Therefore, this paper aims to explore the applications, challenges, and future prospects of LMM in the domain of human factors and ergonomics through an expert-LMM collaborated literature review. Specifically, a novel literature review method is proposed, and research studies of LMM-based accident analysis, human modelling and intervention design are introduced. Subsequently, the paper discusses future trends of the research paradigm and challenges of human factors and ergonomics studies in the era of LMMs. It is expected that this study can provide a valuable perspective and serve as a reference for integrating human factors with artificial intelligence.

While generative artificial intelligence (Gen AI) increasingly transforms academic environments, a critical gap exists in understanding and mitigating human biases in AI interactions, such as anchoring and confirmation bias. This position paper advocates for metacognitive AI literacy interventions to help university students critically engage with AI and address biases across the Human-AI interaction workflows. The paper presents the importance of considering (1) metacognitive support with deliberate friction focusing on human bias; (2) bi-directional Human-AI interaction intervention addressing both input formulation and output interpretation; and (3) adaptive scaffolding that responds to diverse user engagement patterns. These frameworks are illustrated through ongoing work on "DeBiasMe," AIED (AI in Education) interventions designed to enhance awareness of cognitive biases while empowering user agency in AI interactions. The paper invites multiple stakeholders to engage in discussions on design and evaluation methods for scaffolding mechanisms, bias visualization, and analysis frameworks. This position contributes to the emerging field of AI-augmented learning by emphasizing the

Debugging transactions and understanding their execution are of immense importance for developing OLAP applications, to trace causes of errors in production systems, and to audit the operations of a database. However, debugging transactions is hard for several reasons: 1) after the execution of a transaction, its input is no longer available for debugging, 2) internal states of a transaction are typically not accessible, and 3) the execution of a transaction may be affected by concurrently running transactions. We present a debugger for transactions that enables non-invasive, post-mortem debugging of transactions with provenance tracking and supports what-if scenarios (changes to transaction code or data). Using reenactment, a declarative replay technique we have developed, a transaction is replayed over the state of the DB seen by its original execution including all its interactions with concurrently executed transactions from the history. Importantly, our approach uses the temporal database and audit logging capabilities available in many DBMS and does not require any modifications to the underlying database system nor transactional workload.

Human-robot collaboration in surgery represents a significant area of research, driven by the increasing capability of autonomous robotic systems to assist surgeons in complex procedures. This systematic review examines the advancements and persistent challenges in the development of autonomous surgical robotic assistants (ASARs), focusing specifically on scenarios where robots provide meaningful and active support to human surgeons. Adhering to the PRISMA guidelines, a comprehensive literature search was conducted across the IEEE Xplore, Scopus, and Web of Science databases, resulting in the selection of 32 studies for detailed analysis. Two primary collaborative setups were identified: teleoperation-based assistance and direct hands-on interaction. The findings reveal a growing research emphasis on ASARs, with predominant applications currently in endoscope guidance, alongside emerging progress in autonomous tool manipulation. Several key challenges hinder wider adoption, including the alignment of robotic actions with human surgeon preferences, the necessity for procedural awareness within autonomous systems, the establishment of seamless human-robot information exchange, and th

This paper describes a novel approach in human robot interaction driven by ergonomics. With a clear focus on optimising ergonomics, the approach proposed here continuously observes a human user's posture and by invoking appropriate cooperative robot movements, the user's posture is, whenever required, brought back to an ergonomic optimum. Effectively, the new protocol optimises the human-robot relative position and orientation as a function of human ergonomics. An RGB-D camera is used to calculate and monitor human joint angles in real-time and to determine the current ergonomics state. A total of 6 main causes of low ergonomic states are identified, leading to 6 universal robot responses to allow the human to return to an optimal ergonomics state. The algorithmic framework identifies these 6 causes and controls the cooperating robot to always adapt the environment (e.g. change the pose of the workpiece) in a way that is ergonomically most comfortable for the interacting user. Hence, human-robot interaction is continuously re-evaluated optimizing ergonomics states. The approach is validated through an experimental study, based on established ergonomic methods and their adaptation f

Charting the intellectual evolution of a scientific discipline is crucial for identifying its core contributions, challenges, and future directions. The IISE Annual Conference proceedings offer a rich longitudinal archive of the Industrial and Systems Engineering (ISE) community's development, but the sheer volume of scholarship produced over two decades makes a holistic analysis difficult. Traditional reviews often fail to capture the full scale of thematic shifts and complex collaboration networks that define the community's growth. This paper presents a computational analysis of IISE proceedings from 2002 to 2025, drawing on an initial dataset of 9,350 titles from ProQuest for thematic analysis and 8,958 titles from Google Scholar for citation analysis, to deliver a cartography of the ISE field's intellectual history. Leveraging Large Language Models (LLMs) for domain-aware classification, Natural Language Processing, and Network Science, our study systematically maps thematic evolution to identify dominant, emerging, and receding research topics. We analyze citation data and co-authorship networks to uncover influential papers and authors, providing critical insights into knowl

Human-robot collaborative assembly systems enhance the efficiency and productivity of the workplace but may increase the workers' cognitive demand. This paper proposes an online and quantitative framework to assess the cognitive workload induced by the interaction with a co-worker, either a human operator or an industrial collaborative robot with different control strategies. The approach monitors the operator's attention distribution and upper-body kinematics benefiting from the input images of a low-cost stereo camera and cutting-edge artificial intelligence algorithms (i.e. head pose estimation and skeleton tracking). Three experimental scenarios with variations in workstation features and interaction modalities were designed to test the performance of our online method against state-of-the-art offline measurements. Results proved that our vision-based cognitive load assessment has the potential to be integrated into the new generation of collaborative robotic technologies. The latter would enable human cognitive state monitoring and robot control strategy adaptation for improving human comfort, ergonomics, and trust in automation.

The recognition of actions performed by humans and the anticipation of their intentions are important enablers to yield sociable and successful collaboration in human-robot teams. Meanwhile, robots should have the capacity to deal with multiple objectives and constraints, arising from the collaborative task or the human. In this regard, we propose vision techniques to perform human action recognition and image classification, which are integrated into an Augmented Hierarchical Quadratic Programming (AHQP) scheme to hierarchically optimize the robot's reactive behavior and human ergonomics. The proposed framework allows one to intuitively command the robot in space while a task is being executed. The experiments confirm increased human ergonomics and usability, which are fundamental parameters for reducing musculoskeletal diseases and increasing trust in automation.

Ergonomics and human comfort are essential concerns in physical human-robot interaction. Common practical methods in the area either fail in estimating the correct posture due to occlusion or suffer from inaccurate ergonomics models in performing postural optimization. We propose a novel alternative framework for posture estimation, assessment, and optimization for ergonomically intelligent physical human-robot interaction. We show that we can estimate human posture solely from the trajectory of the interacting robot with median deviation of 5 deg from motion capture. We propose DULA, a differentiable ergonomics assessment tool with 99.73% accuracy comparing to RULA. We use DULA in postural optimization for physical human-robot interaction tasks such as co-manipulation and teleoperation. We evaluate our framework through human and simulation experiments.

The high prevalence of work-related musculoskeletal disorders (WMSDs) could be addressed by optimizing Human-Robot Collaboration (HRC) frameworks for manufacturing applications. In this context, this paper proposes two hypotheses for ergonomically effective task delegation and HRC. The first hypothesis states that it is possible to quantify ergonomically professional tasks using motion data from a reduced set of sensors. Then, the most dangerous tasks can be delegated to a collaborative robot. The second hypothesis is that by including gesture recognition and spatial adaptation, the ergonomics of an HRC scenario can be improved by avoiding needless motions that could expose operators to ergonomic risks and by lowering the physical effort required of operators. An HRC scenario for a television manufacturing process is optimized to test both hypotheses. For the ergonomic evaluation, motion primitives with known ergonomic risks were modeled for their detection in professional tasks and to estimate a risk score based on the European Assembly Worksheet (EAWS). A Deep Learning gesture recognition module trained with egocentric television assembly data was used to complement the collabora

Human factors and ergonomics are the essential constituents of teleoperation interfaces, which can significantly affect the human operator's performance. Thus, a quantitative evaluation of these elements and the ability to establish reliable comparison bases for different teleoperation interfaces are the keys to select the most suitable one for a particular application. However, most of the works on teleoperation have so far focused on the stability analysis and the transparency improvement of these systems, and do not cover the important usability aspects. In this work, we propose a foundation to build a general framework for the analysis of human factors and ergonomics in employing diverse teleoperation interfaces. The proposed framework will go beyond the traditional subjective analyses of usability by complementing it with online measurements of the human body configurations. As a result, multiple quantitative metrics such as joints' usage, range of motion comfort, center of mass divergence, and posture comfort are introduced. To demonstrate the potential of the proposed framework, two different teleoperation interfaces are considered, and real-world experiments with eleven par

A major challenge for autonomous vehicles is interacting with other traffic participants safely and smoothly. A promising approach to handle such traffic interactions is equipping autonomous vehicles with interaction-aware controllers (IACs). These controllers predict how surrounding human drivers will respond to the autonomous vehicle's actions, based on a driver model. However, the predictive validity of driver models used in IACs is rarely validated, which can limit the interactive capabilities of IACs outside the simple simulated environments in which they are demonstrated. In this paper, we argue that besides evaluating the interactive capabilities of IACs, their underlying driver models should be validated on natural human driving behavior. We propose a workflow for this validation that includes scenario-based data extraction and a two-stage (tactical/operational) evaluation procedure based on human factors literature. We demonstrate this workflow in a case study on an inverse-reinforcement-learning-based driver model replicated from an existing IAC. This model only showed the correct tactical behavior in 40% of the predictions. The model's operational behavior was inconsiste

When we go for a walk with friends, we can observe an interesting effect: From step lengths to arm movements - our movements unconsciously align; they synchronize. Prior research found that this synchronization is a crucial aspect of human relations that strengthens social cohesion and trust. Generalizing from these findings in synchronization theory, we propose a dynamical approach that can be applied in the design of non-humanoid robots to increase trust. We contribute the results of a controlled experiment with 51 participants exploring our concept in a between-subjects design. For this, we built a prototype of a simple non-humanoid robot that can bend to follow human movements and vary the movement synchronization patterns. We found that synchronized movements lead to significantly higher ratings in an established questionnaire on trust between people and automation but did not influence the willingness to spend money in a trust game.

While the proliferation of foundation models has significantly boosted individual productivity, it also introduces a potential challenge: the homogenization of creative content. In response, we revisit Design-by-Analogy (DbA), a cognitively grounded approach that fosters novel solutions by mapping inspiration across domains. However, prevailing perspectives often restrict DbA to early ideation or specific data modalities, while reducing AI-driven design to simplified input-output pipelines. Such conceptual limitations inadvertently foster widespread design fixation. To address this, we expand the understanding of DbA by embedding it into the entire creative process, thereby demonstrating its capacity to mitigate such fixation. Through a systematic review of 85 studies, we identify six forms of representation and classify techniques across seven stages of the creative process. We further discuss three major application domains: creative industries, intelligent manufacturing, and education and services, demonstrating DbA's practical relevance. Building on this synthesis, we frame DbA as a mediating technology for human-AI collaboration and outline the potential opportunities and inhe

Recent advances in artificial intelligence (AI) and robotics have drawn attention to the need for AI systems and robots to be understandable to human users. The explainable AI (XAI) and explainable robots literature aims to enhance human understanding and human-robot team performance by providing users with necessary information about AI and robot behavior. Simultaneously, the human factors literature has long addressed important considerations that contribute to human performance, including human trust in autonomous systems. In this paper, drawing from the human factors literature, we discuss three important trust-related considerations for the design of explainable robot systems: the bases of trust, trust calibration, and trust specificity. We further detail existing and potential metrics for assessing trust in robotic systems based on explanations provided by explainable robots.

This chapter focuses on the evolution of Human-Centered Design (HCD) in aerospace systems over the last forty years. Human Factors and Ergonomics first shifted from the study of physical and medical issues to cognitive issues circa the 1980s. The advent of computers brought with it the development of human-computer interaction (HCI), which then expanded into the field of digital interaction design and User Experience (UX). We ended up with the concept of interactive cockpits, not because pilots interacted with mechanical things, but because they interacted using pointing devices on computer displays. Since the early 2000s, complexity and organizational issues gained prominence to the point that complex systems design and management found itself center stage, with the spotlight on the role of the human element and organizational setups. Today, Human Systems Integration (HSI) is no longer only a single-agent problem, but a multi-agent research field. Systems are systems of systems, considered as representations of people and machines. They are made of statically and dynamically articulated structures and functions. When they are at work, they are living organisms that generate emergi

Direct measurement ergonomic assessment is reshaping occupational safety by facilitating highly reliable risk estimation. Industry 5.0, advocating human-centricity, has catalysed increasing adoption of direct measurement tools in manufacturing industries. However, due to technical and feasibility constraints in their practical implementations, especially within non routine manufacturing processes, task based approach to ergonomic assessment is utilized. Despite enabling operationalization of robust ergonomic assessment technologies within complicated industrial processes, task based approach raises several validity concerns. Hence, to ascertain functional utility of the resultant safety interventions, this study evaluates the construct validity of task based ergonomic assessment within non routine work utilizing Multitrait multimethod (MTMM) matrix followed by video-based content analysis. Ergonomic exposure traits were collected for 46 participants through direct measurement and self reported techniques utilizing inertial motion capture and Borg's RPE rating scale respectively. Findings include unsubstantiated convergent validity (low same trait correlations from 0.149 to 0.243) a

A limitation for collaborative robots (cobots) is their lack of ability to adapt to human partners, who typically exhibit an immense diversity of behaviors. We present an autonomous framework as a cobot's real-time decision-making mechanism to anticipate a variety of human characteristics and behaviors, including human errors, toward a personalized collaboration. Our framework handles such behaviors in two levels: 1) short-term human behaviors are adapted through our novel Anticipatory Partially Observable Markov Decision Process (A-POMDP) models, covering a human's changing intent (motivation), availability, and capability; 2) long-term changing human characteristics are adapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that selects a short-term decision model, e.g., an A-POMDP, according to an estimate of a human's workplace characteristics, such as her expertise and collaboration preferences. To design and evaluate our framework over a diversity of human behaviors, we propose a pipeline where we first train and rigorously test the framework in simulation over novel human models. Then, we deploy and evaluate it on our novel physical experiment setup that in