Emerging Technologies and Future of Work

AHFE International

Accelerating Open Access Science in Human Factors Engineering and Human-Centered Computing

Emerging Technologies and Future of Work

book-cover

Editors: Tareq Ahram, Waldemar Karwowski

Topics: Human Error, Reliability & Performance

Publication Date: 2023

ISBN: 978-1-958651-93-3

DOI: 10.54941/ahfe1004398

Articles

A Review of Sociotechnical Approaches for Nuclear Power Plant Modernization

The nuclear power continues to be a safe, reliable, and carbon-free electricity generating source for the United States (U.S.), though the cost of operating and maintaining the current U.S. nuclear power plant fleet has become uncompetitive with other sources. This gap is attributed to the advent of new digital technologies that other electricity generating industries are currently leveraging to streamline work and greatly reduce operating, maintenance, and support costs. To address the gap, the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program Plant Modernization Pathway is conducting targeted research and development (R&D) to keep the existing U.S. nuclear power plants economically viable and extend their lifespans by improving their performance through two complementary mission areas:1.Delivering a sustainable business model that enables a cost-competitive U.S. nuclear industry, and2.Developing technology modernization solutions that address aging and obsolescence challenges.Integrated operations for nuclear (ION) is a driving LWRS Program plant modernization research area that focuses on delivering a sustainable business model to provide direction and focus for cross-functional R&D across the LWRS Program that focuses on developing technology modernization solutions. Recent ION research provides a target cost reduction needed in the next 3–5 years for the nuclear industry to remain cost competitive. This research identified major technological advancements (herein referred to as critical work domains) that will significantly reduce cost needed to operate, maintain, and support the existing U.S. nuclear power plant fleet. Some of these key critical work domains include: •Digital instrumentation and control (I&C) and control room modernization•Work/ requirement reduction•Mobile worker technology•Condition-based monitoring•Remote collaboration•Plant automation•Advanced analytics and assuranceThe scope of ION across these critical work domains is to enable transformation of work in a way that improves plant performance and efficiencies through a holistic analysis of the work performed. ION thereby emphasizes the implementation of advanced technologies that eliminate tedious manual tasks, reduce workload, improve team situation awareness, and improve organizational decision-making. To enable the success of ION, this work positions the need to apply a sociotechnical approach that carefully considers the work domain and its constraints to inform the how new technologies can be incorporated to support continued safe and reliable operation of the plant while maximizing the benefits of the new technology. Specifically, this work explores sociotechnical approaches such as cognitive work analysis, co-active design/ interdependency analysis, and systems theoretic process analysis to address the function allocation and data visualization elements of an ION transformation to ensure the economic viability of the existing U.S. nuclear power plant fleet.

Casey Kovesdi

Conference Proceedings

Digital Twin Framework for the Resilient Remote Monitoring and Operation of Nuclear Microreactors

The nuclear industry is developing new advanced reactor technologies, and many companies are conceptualizing designs for microreactors, a class of nuclear reactor with a sub-20 MWth power output designed to be factory fabricated, easily transportable, and simple to control. Microreactors offer promising solutions to several use cases for which large-scale plants would not be suitable, and conventional power generation means, notably diesel electric generators, are expensive and logistically difficult. Many of the potential use cases are in isolated locations such as arctic communities, remote mines, and military installations. Therefore, the cost of microreactor deployment and traditional onsite operations pose a challenge that requires new technical solutions to address. One solution that has the potential to greatly improve economics is to operate and monitor microreactors remotely from a centralized location. Remote monitoring and operation are novel concepts to the nuclear industry and will greatly alter the tasks and responsibilities associated with current commercial nuclear power plant operators. As such, it is important to perform research on potential technological solutions and the impacts those solutions have on operations with end-goal of defining a safe and effective remote concept of operations. This paper proposes a framework for resilient remote operation of microreactors enabled by a novel digital twin implementation.

Kaeley Stevens, Joseph Oncken, Megan Culler, Stephen Bukowski, Thomas Ulrich, Izabela Gutowska, Ronald Boring

Conference Proceedings

An Investigation of Time Distributions for Task Primitives to Support the HUNTER Dynamic Human Reliability Analysis

As an effort to support data collection for dynamic human reliability analysis (HRA), this study investigates time distributions for task primitives defined in the Goals, Operators, Methods, and Selection rules (GOMS)–Human Reliability Analysis (HRA) method. GOMS-HRA was developed to provide cognition-based time and human error probability information for dynamic HRA calculation in the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework. HUNTER is a framework to support the dynamic modelling of human error in conjunction with other modelling tools. In this paper, we investigate time distributions using experimental data collected from the Simplified Human Error Experimental Program (SHEEP) study, which suggests an HRA data collection framework to complement full-scope simulator research as well as collect input data for dynamic HRA using simplified simulators such as the Rancor Microworld Simulator. In this study, time required for GOMS-HRA task primitives to satisfy thirteen statistical distributions is investigated. Then, the time distributions from student operators and professional operators are compared and discussed. As a result, this study identified several time distributions on five GOMS-HRA task primitives at a statistically significant level. According to analyses to date, a greater number of significant time distributions was found in abnormal or emergency operating procedures rather than standard operating procedures. In the future, it is expected that the result of this study can provide objective reference on elapsed time data for task primitives as well as help to realistically simulate scenarios within dynamic HRA.

Jooyoung Park, Taewon Yang, Jonghyun Kim, Ronald Boring, Chad Pope

Conference Proceedings

HUNTER Procedure Performance Predictor: Supporting New Procedure Development with a Dynamic Human Reliability Analysis Method

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a streamlined software framework for dynamic human reliability analysis (HRA). HUNTER simulates a reactor operator as a digital human twin, providing a platform by which to model human interactions with a digital hardware twin in the form of a simulated nuclear power plant. HUNTER gives realistic insights into human errors, actions, and time frames. Recent discussions with stakeholders in the U.S. nuclear sector have highlighted potential uses for HUNTER to support development of operating procedures for advanced control rooms. As nuclear power plants transition from analog to digital control rooms or develop advanced control systems for new reactors, a unique challenge arises concerning operating procedures. Established procedures for existing plants have undergone multiple iterations, but with the advent of digital control systems in control rooms, there's often a lack of operating experience to shape these new procedures. Such Version Null procedures are a pressing concern for those drafting them and ensuring plant safety. Expanding on HUNTER's procedural capabilities, a specialized version named the Procedure Performance Predictor (P3) is under development. HUNTER-P3 enables those writing procedures to draft new ones and then test them in a simulation to gauge both operator and plant responses. HUNTER-P3 identifies potential operator and procedure level shortcomings, offering a novel way to validate procedures.

Ronald Boring, Thomas Ulrich, Roger Lew, Jooyoung Park

Conference Proceedings

The Influence of Spatial Dimension on Task Completion in Human Reliability Analysis: A Pilot Study

In the context of nuclear power plants, human reliability analysis (HRA) is an assessment approach focused on analyzing human error probability in complex systems, minimizing human errors, and increasing safety at nuclear power plants. Both time and location are major influencing factors when it comes to dynamic HRA, because they can easily determine operator success or failure. Despite this, research on these factors is still in its early stages. This pilot study aims to provide preliminary data on four major factors—terrain, distractions, mobility restrictions, and load—to determine the influence of these factors on walking time. Four scenarios were developed to figure out whether movement factors can affect task completion time. By using experimental data, we derived the average walking time and speed under each condition, time increase rate as compared to the regular condition, and the relation of height and speed in given scenarios. These data were linearly regressed to extrapolate time for uncollected data. We found that task performance time varied significantly depending on the determining factor. For example, the distraction scenario drastically increased walking time, while performance changes under factors such as the uneven road were less severe. This research can be used to determine the influence of the spatial dimension during operator walking time, which can help minimize time-related human errors and enhance safety at nuclear power plants.

Huanlu Yuan, Hyesoo Lee, Jasmine Lima, Ronald Boring

Conference Proceedings

Uncertainty-Aware AI-Facilitated Decision Support System for Emergency Call Takers

This paper presents an artificial intelligence (AI) based decision support system to assist call takers sitting behind emergency numbers, such as 1-1-2 or 9-1-1. Accurate situational awareness of emergency incidents during emergency report intake is crucial for the initial response. However, the caller’s utterances in an emergency situation can be uncertain, therefore the AI model can often assist call recipients with false predictions, delaying the appropriate initial response to emergencies. To address this issue, we propose using an uncertainty-aware AI model in the decision support system. The proposed system displays a set of emergency situational candidates predicted based on the text transcribed in real-time as the voice report intake, supporting the call takers in effectively receiving the emergency calls. The type and number of emergency situational candidates are determined considering the uncertainty inferred from an uncertainty-aware AI model. We provide a detailed explanation of the proposed system and evaluate its performance using actual domestic 1-1-2 police emergency report data.

Minjung Lee, Sungwon Byon

Conference Proceedings

The need of change in complex workplaces of the O&G industry – from controlling human error to understanding the resilience of systems

Since the first oil drilling in History, the Drake’s well in Titusville, Pennsylvania, until the present-day offshore wells, drilled in the ultra-deep waters of the Gulf of Mexico and the Brazilian Pre-Salt, two aspects have always been present: the notable risks of dealing with crude oil and the need of human adaptabilities in the work systems. From this adaptability, there will be two possible outcomes: the normal work, adaptive and productive, and the accident, unwanted and harmful. For the first, over a long time no attention was given, because if nothing went wrong, (supposedly) there is nothing to do, except to continue working. On the other hand, for this second, the accident, since the first occurrences, dating from the 1st Industrial Revolution, much has been developed, addressed, mainly, on the unwanted action of the human element in a linear system. However, the technological evolution of work systems has transformed linear production lines into current complex sociotechnical systems, where there are intense and dynamic interrelationships between people, machines, and processes, immersed in a distinct organizational culture. In this context, the maintenance of certain linear epistemological concepts for the analysis of risks, as well as the investigation of accidents, seems to be limited, when not mistaken, for understanding and intervening in nowadays complex workplaces. In addition, normal work, that is, work carried out without the occurrence of accidents, as it is what mostly happens, is a notable source of learning, being neglected precisely because it is normal. In view of these considerations, methodologies, and concepts capable of dealing with this, such as FRAM (Functional Resonance Analysis Method) and Safety-I & Safety-II, arises as adequate solutions. In this study, having the O&G Industry as background, some accidents with FRAM are re-examined, as well as some practices of learning from normal work through Safety-I & Safety-II, demonstrating the need of change in complex workplaces of the O&G industry, evolving from controlling human error to understanding the resilience of systems.

Josue Franca

Conference Proceedings

Adapting a 3D Printer as a Robot for Testing Electronic Control Units in Automotive Context

As a result of a multitude of safety- and comfort-functions, most modern automobiles contain various electronic control units. The automobiles’ passengers can control several of those functions not only by activating mechanical switches, but also by using sensory control elements. Sensory control elements mostly use capacitive effects induced by contact to determine whether a function should be executed. Aforementioned contacts might be in the form of touches or slides. During the development of sensory control elements, it is desirable to test the devices as early and as often as possible to ensure a continual adaption of sensor calibration and evaluation. The significance of such tests is provided by repeating tests at equal positions with comparable velocities and applied forces. Therefore, robots are already used to perform such tests. The operational availability of the previously mentioned robots has been proven to be too short, which is why not all developers are provided with sufficient test capacities. To approach this problem, the development of a duplicatable robot system with respect to a constrained budget has been realized. Due to the high costs of complete robot systems, this thesis contains an experimental approach by remodeling a 3D printer. An adequate system has been developed by remodeling mechanical-, hardware- and firmware aspects of the original system. Furthermore, a user software has been programmed. Essential aspects of the remodeling include construction designs for mechanical changes, hardware- and firmware integration of a force sensor, implementing a force-dependent stop of movements and communication with the user software. Essential aspects for the user software include the design of a GUI as an interface between the user and the system, the automatic generation of coordinated movements and communications with the robot. Further development should include an improved validation of force measurements, a more accurate force-dependent stop, more accurate determination of reference points and the optimization of the built-in working area. Additionally, the current force sensor only measures forces in one axis. It is suggested to replace it with a three-axis sensor.

Daniel Schilberg, Alexander Letzel

Conference Proceedings

Augmented Learning for Environmental Robotics Technologies (ALERT)

The increasing environmental concerns call for more sophisticated and integrated educational methods. For sustainable outcomes, understanding and navigating complex environmental factors is essential. By imparting knowledge about environmental data and its applications, students can be better prepared to address environmental issues.The "Augmented Learning for Environmental Robotics Technologies (ALERT)" program introduces an educational method using augmented reality (AR) and artificial intelligence (AI). It provides students, particularly those in architecture, engineering, and construction (AEC), with an immersive learning experience focused on environmental data and robotics. Considering the significant environmental footprint of the AEC sector—emanating from energy-intensive buildings, roads, and infrastructures—the ALERT initiative strives to instill a comprehensive understanding of environmental data collection and visualization. This is done with the aim of promoting data-centric design and construction for a more eco-friendly built environment.In the ALERT program, AR is employed to fashion an augmented learning space where students can engage with both real-time and past environmental data. They learn to set up environmental sensors, collect data, and visualize it to unearth hidden trends and connections. Additionally, AI ensures a tailored learning journey for each student, offering optimal challenges and support. This innovative blend of AR and AI not only offers an enriching learning experience but also prepares AEC students to be at the forefront of transformative shifts, especially those influenced by advancements like robotic automation, fostering a profound understanding of environmental data.This paper outlines the preliminary stages of the ALERT project, detailing its foundational research. Topics include the educational theories guiding the creation of a groundbreaking Intelligent Learning System (ILS) and curriculum, as well as the projected impact of the program. ALERT emerges as a promising venture, potentially empowering students with the expertise to reduce the ecological footprint of infrastructure, paving the way for a greener future.

Biayna Bogosian, Shahin Vassigh, Eric Peterson

Conference Proceedings

Validating Trust in Human-Robot Interaction through Virtual Reality: Comparing Embodied and "Behind-the-Screen" Interactions

Human-agent interaction is commonplace in our daily lives, manifesting in forms ranging from virtual assistants on websites to embodied agents like robots that we might encounter in a physical setting. Previous research has largely been focused on “behind-the-screen” interactions, but these might not fully encapsulate the nuanced responses humans exhibit towards physically embodied agents. To address this gap, we use virtual reality to examine how simulated physical embodiment and the reliability of an agent (automated robotic crane) influence trust and performance in a task simulating a quality assurance role and compare it to a “behind-the-screen” interaction. Out of 119 participants, the data revealed there is a marked behavioral shift observed when reliability hits a 91% threshold, with no influence from embodiment. Furthermore, participants displayed a tendency to trust and defer to the decisions of embodied agents more, especially when these agents were not infallible. This study accentuates the need for transparency about an agent's capabilities and emphasizes the significance of ensuring that the agent's representation is congruent with the nature of the interaction. Our findings pave the way for a deeper understanding of human-agent interactions, suggesting a future where these interactions might seamlessly blend the virtual and physical realms.

Sebastian Rodriguez, Harsh Deep, Drshika Asher, James Schaffer, Alex Kirlik

Conference Proceedings

Development of a Compact Walking Assistive Robot for Exercise Promotion and Gait Training

With the growing population of aged citizens, the number of people coping with walking disorders is increasing. To address this problem, various types of walking assistive robots were developed. However, most of these robots are heavy, bulky, and with poor practicability.In this study, we developed a compact walking assistive robot which can be used for exercise promotion and gait training for the able-bodied elderly. By assisting only the user’s ankle joint, the robot can assist the foot lift based on the stretch reflex mechanism, inform the user of the correct motion and timing, and guide them to achieve ideal walking. The proposed robot consists of cover, servo motor, torque limiter, control unit, adjustable straps, shoes, and pressure sensors. By 3D printing the cover with resin, the total weight is 1.2 kg with battery. In addition, to reduce noise, arc-shape springs made of soft materials were used instead of straight-shape spring, and these design parameters were calculated from theoretical equation. Walking parameters and control mode can be adjusted by graphical user interface application. For a control mode, we used a gait-adaptive method for ankle assistive robots to adapt to the user’s changing gait for providing more accurate walking assistance.Finally, we conducted a walking test to investigate the gait-adaptive accuracy and how user feels. Participants were required to wear robot and walk continuously for 30 strides (60 steps) with same trends. The gait-adaptive accuracy achieved high accuracy exceeding 95 % on average using a gait-adaptive method based on regression. In addition, the user felt a positive impression, which user feels an assist force and fun, and can walk smoothly etc., for proposed robot during walking. As a result, a series of evaluation experiments verified an effectiveness, finally concluded that the proposed robot could be used for exercise promotion and gait training. The advantages of the proposed robot are low cost, light weight and easy-to-use.

Keisuke Osawa, Yifan Hua, Kaiwen Duan, Kei Nakagawa, Eiichiro Tanaka

Conference Proceedings

Behavioral Design Focusing on Personal Distance to Elicit Attachment to Pet Robots

What is the difference between a companion animal and a pet robot? In recent years, many pet robots with the theme of healing have been proposed in Japan. Although those pet robots are designed in various ways to be loved by humans, the number of pet robot owners is still far fewer than that of companion animal owners. This is partly because pet robots still cannot withstand long-term use because their interactions become obsolete at an early stage.Therefore, for the long-term use of pet robots, this research aims to build a long-term relationship between owners and pet robots by eliciting attachment to the pet robot from its owner, and to realize interactions that will prevent boredom with the pet robot.In particular, this paper focuses on the pet robot's “personal distance”. We evaluate whether the pet robot can elicit the owner’s attachment to the pet robot by shortening the personal distance as it interacts with its owner over a longer period.The experiment lasted for 14 days and involved 16 participants. The participants wore HMDs and observed the robot on the stereoscopic display during a certain period each day. Robots which behaved in 4 different patterns were presented, 3 of which shortened the personal distance each day, i.e., the robot gradually approached the participant each day, and the remaining 1 pattern is the one which the personal distance was constant and short, i.e., the robot always stayed at a close distance. The three types of robots that shortened their personal distance day by day were those that approached the participant remarkably in the first half of the experiment, those that approached the participant remarkably in the second half of the experiment, and those that approached the participant gradually and to a certain degree throughout the experiment.The participants were asked to answer a daily questionnaire to measure the degree of attachment they felt toward each robots daily. At the end of the experiment, participants were asked which robot they liked best.The results showed that the attachment scores of the robot with constant and short personal distance were significantly higher than those of the other robots for each of the 14 days, but the scores did not change significantly throughout each day of the experiment. On the other hand, among the robots whose personal distance varied, the evaluation value tended to increase throughout the experiment for the robot that approached the participant remarkably in the first half of the experiment. In addition, the results of the questionnaire asking which of the robots the participants liked best, showed the robot that approached the participant remarkably in the first half of the experiment was the most preferred among the four types of robots.These results suggest that by lengthening the experiment period, the attachment scores of the robot that approached the participant remarkably in the first half of the experiment might exceed that of the robot whose personal distance was constant and short, and elicit the owner's attachment to the robot.

Yusuke Jupiter Guard, Keiko Yamamoto, Yu Shibuya

Conference Proceedings

Adaptive Immersive Learning Environments for Teaching Industrial Robotics

AI, robotics, and automation are reshaping many industries, including the Architecture, Engineering, and Construction (AEC) industries. For students aiming to enter these evolving fields, comprehensive and accessible training in high-tech roles is becoming increasingly important. Traditional robotics education, while often effective, usually necessitates small class sizes and specialized equipment. On-the-job training introduces safety risks, particularly for inexperienced individuals. The integration of advanced technologies for training presents an alternative that reduces the need for extensive physical resources and minimizes safety concerns. This paper introduces the Intelligent Learning Platform for Robotics Operations (IL-PRO), an innovative project that integrates the use of Artificial Intelligence (AI), Virtual Reality (VR), and game-assisted learning for teaching robotic arms operations. The goal of this project is to address the limitations of traditional training through the implementation of personalized learning strategies supported by Adaptive Learning Systems (ALS). These systems hold the potential to transform education by customizing content to cater to various levels of understanding, preferred learning styles, past experiences, and diverse linguistic and socio-cultural backgrounds.Central to IL-PRO is the development of its ALS, which uses student progress variables and multimodal machine learning to infer students’ level of understanding and automate task and feedback delivery. The curriculum is organized into modules, starting with fundamental robotic concepts, and advancing to complex motion planning and programming. The curriculum is guided by a learner model that is continuously refined through data collection. Furthermore, the project incorporates gaming elements into its VR learning approach to create an engaging educational environment. Thus, the learning content is designed to engage students with simulated robots and input devices to solve sequences of game-based challenges. The challenge sequences are designed similarly to levels in a game, each with increasing complexity, in order to systematically incrementally build students' knowledge, skills, and confidence in robotic operations. The project is conducted by a team of interdisciplinary faculty from Florida International University (FIU), the University of California Irvine (UCI), the University of Hawaii (UH) and the University of Kansas-Missouri (UKM). The collaboration between these institutions enables the sharing of resources and expertise that are essential for the development of this comprehensive learning platform.

Shahin Vassigh, Seth Corrigan, Biayna Bogosian, Eric Peterson, Bhavleen Kaur

Conference Proceedings

Exploring Trust and Performance in Human-Automation Interaction: Novel Perspectives on Incorrect Reassurances from Imperfect Automation

Consider the following hypothetical scenario: Sarah, a skilled pharmacist, is responsible for filling the medication bottles for prescription orders. Recently, her pharmacy introduced an AI computer vision system that scans the filled bottles and identifies the medication, which serves as an additional layer of verification before dispensing. Sarah receives a prescription for patient Noah, who needs medication “X”. Five possible cases could occur:Case A: Sarah correctly fills the prescription bottle with pill "X", and the automated decision aid correctly predicts it as "X".Case B: Sarah correctly fills the prescription bottle with pill "X", and the automated decision aid incorrectly predicts it as "Y". Case C: incorrectly fills the prescription bottle with pill "Z", and the automated decision aid correctly predicts it as "Z".Case D: Sarah incorrectly fills the prescription bottle with pill "Z", and the automated decision aid incorrectly predicts it as "Y".Case E: Sarah incorrectly fills the prescription bottle with pill "Z", and the automated decision aid incorrectly predicts it as "X".The scenario presents unique characteristics that are not examined in existing research paradigms examining trust in and dependence on automation, wherein automated decision aids give recommendations based on the raw information. In contrast, in the hypothetical scenario, the input to the AI system is human-provided data (i.e., A pharmacist fills the bottle). This research aims to investigate the effects of the different cases on participants’ trust and performance. We developed a testbed involving human participants performing mental rotation tasks with the help of imperfect automation. In the experimental task, participants were presented with a reference image alongside five answer choices and needed to select the choice that matched the reference image. Participants provided initial answer choices, received automation predictions, and made final answer choices for 60 trials.Thirty-five university students participated in the experiment. The study employed a within-subject design to examine the cases. Dependent variables were trust adjustment, performance, reaction time, and confidence.Results revealed that Case E, when participants received incorrect reassurance from automation for wrong initial answers, had the largest trust decrement and the worst final performance. This result confirms our hypothesis that Case E is problematic and requires further in-depth investigation. Case B, when participants’ right initial answer choice was followed by incorrect machine prediction, had the second largest trust decrement. In addition, we found across the majority of the cases that invalid recommendations harmed users’ trust more than valid recommendations increased trust, which aligns with the “negativity bias” property reported in prior literature. Furthermore, for each pattern, participants' trust decrement was greater when their final answers were wrong, indicating valid recommendations are penalized when final performance is harmed and invalid recommendations are less penalized when final performance is not harmed. These findings contribute to a fundamental understanding of how human trust is influenced in scenarios of automation failures when input information is provided by humans. These insights have practical implications for the design and implementation of semi-automated decision aids in domains where safety and effectiveness are critical.

Jin Yong Kim, Szu Tung Chen, Corey Lester, X. Jessie Yang

Conference Proceedings

Use of collaborative robots to generate movement trajectories for rehabilitating patients with joint mobility limitations of the upper extremities.

In the 2000s, the application of collaborative robots began to be heard more frequently in various sectors, such as the Manufacturing industry and Healthcare. One of its main advantages is the way of interacting with the user; since it allows to share workspaces more closely without any fatal collisions. Currently, the price of these robots varies with the task type; the more transport load they support and the greater precision in their movements, the more expensive they will be. Nowadays, several works mention the use of collaborative robots to assist in the rehabilitation process of patients. These procedures are expensive since, initially, the purchase of the robot is required, and later the application software to generate the patient's rehabilitation movements. This article presents a methodology to generate the trajectory of the rehabilitation movements of patients with limitations in the upper joints. Engineering application software is used for the academic community (Professors and students). The licenses for operating this software application are free for the academy. In university courses, inverse kinematics projects of collaborative robots can be proposed to generate the rehabilitation trajectories of the patients mentioned above. With this methodology, only the collaborative robot would be required, reducing the initial investment of this type of treatment. When using student software applications, it would be possible to use the other tools that this type of computational tool has, such as 3D printing of parts, some ergonomic analysis of components, or the design of parts or fasteners through the finite element method. To test the methodology developed, a case study was used. It was a final project in the Automation of Manufacturing Systems course of the Tecnologico de Monterrey for students of the Mechatronics Engineering career. In this case study, the generated trajectories stimulate patients' motor skills to draw 2D contours. However, an advantage of the described methodology is that it can be used to generate any 2D or 3D trajectory as required by the patient. The methodology consists of the following stages, 1) 3D modeling of the parts of the collaborative robot that intervenes to generate trajectories, 2) consultation of the reference system of the axes of the collaborative robot, 3) definition of the appropriate movements for the rehabilitation of the patient and 4) programming of the robot. At the beginning of the article, different configurations and applications of collaborative robots are mentioned. Subsequently, the characteristics of the collaborative robot used for this work are described. Next, the methodology implemented for generating trajectories for rehabilitating patients with limitations of the movements of the upper limbs is detailed. Then, the developed methodology is implemented through a case study. Finally, the results, conclusions, and future work are presented.

Hector Rafael Morano-okuno, Rafael Caltenco Castillo, Guillermo Sandoval

Conference Proceedings

Investigating Emotional Expressivity in Robots Wearing Light-Emitting Clothing

People communicate smoothly through emotional expressions. Therefore, we consider that emotional expression by robots is necessary for human-robot coexistence as well. Various studies have been conducted on emotional expression by robots. In our previous study, we created a robot named "Tilting Robot" that tilts back and forward, and investigated the effect of the color of the clothing worn by the robot on its emotional expression. The results showed that the intensity of the emotional expression may change depending on the color of the clothing and the speed of the robot's motion. In this paper, we investigate the effect of using light-emitting EL sheets as a material for the robot's clothing. Many robots have been developed that express emotional expression by emitting light from their eyes and cheeks, but there is no research on emotional expression by emitting light from clothing. In addition, light-emitting clothing is easy to implement and does not cause discomfort when worn by a robot, so if light-emitting clothing is effective in emotional expression, it has great potential for application. In the experiment, the robot's clothing was colored red, green, blue, and white, and a light-emitting material (EL sheet) and a non-light-emitting material (felt) were prepared for each of the four colors. Also, for the light-emitting material, two conditions were set: one was to emit light continuously during the robot's motion (constant light-emitting condition), and the other was to emit light in the middle of the robot's motion (midway light-emitting condition). Therefore, there are three conditions for the clothing: the felt condition, the constant light-emitting condition, and the midway light-emitting condition. The robot made a total of five motions, including back and forward tilting motions, fast and slow motions, and no motion while in an upright posture. Subjects observed a total of 60 materials that were a combination of all conditions and motions on a monitor, and evaluated each material.The results of the experiment showed that, first of all, when the robot leaned backward quickly, subjects expressed an emotional expression of surprise, regardless of the type of clothing. In addition, when the motion was slow, the robot also expressed surprise in the mid-lighting condition. These results indicate that the intensity of instantaneous emotional expressions such as surprise depends on the speed of motion, and that the midway light may be used to express the emotion. In addition, it was found that the forward-tilting motion can express the emotional expression of sadness regardless of the type of clothing.Next, when we focused on the color of the clothing, we found that the color red evoked an emotional expression of anger, and that the two luminous conditions evoked anger more strongly than the felt condition, in which no luminescence was emitted. The blue color showed the same level of emotional expression of sadness in all clothing conditions. These results indicate that emotional expression can be achieved by changing the color of clothing, as in previous studies, but that the luminescence of the clothing may make it more pronounced.

Takashi Sugiyama, Masayoshi Kanoh

Conference Proceedings

Systemizing Long-Term Research: Assessing Long-Term Automation Effects and Behaviour Modification

Over the past years, numerous studies have paved the way towards a better understanding of human-automation interaction (HAI). However, there is neglect in research that focuses on the long-term effects of automation on user behaviour. The reason behind this has been highly emphasised. As, long-term research is one of the most critically challenging approaches and is quite expensive to conduct, among others. Moreover, many scholars argue that a major source of difficulty is defining how long a period is enough to consider the potential change in user behaviour or behaviour modification. In this discussion, we consider what constitutes long-term research, to prolifically draw knowledge on taxonomies and benchmarks for empirical evaluation strategies on changes in user behaviour. Further, we consider the trade-offs between long-term effects and learning effects. In addition, the reader should note that this paper is a fragment of dualistic parts of knowledge distribution on the topic of constructing a long-term research strategy for assessing learning effects, long-term effects and behaviour modification.

Naomi Y Mbelekani, Klaus Bengler

Conference Proceedings

Kinematics of Serial Robotics - Algorithms for simplified calculation of Direct & Inverse Kinematics in a Consistent Coordinate Reference system

A novel algorithm structure of direct and inverse kinematics for the motion calculation of articulated robots is presented. These algorithms are based on a 3D rotation matrix, which is known in itself but not established in robotics, as well as the principle of a normalized vector orientation, introduced here. The algorithms can handle any number of rotation and telescop axes, can be fully parameterized according to individual hardware and are also much clearer than the established & classic Denavit-Hartenberg conventions.State of the art According to current research (2022/23), the (published) robot mathematics are still based on the classical Denavit-Hartenberg conventions from 1955, which formalize the calculation of the "Direct" kinematics. For this purpose, they require a separate Local Coordinate System (LCS) for each robot arm in addition to the stationary (world) reference coordinate system. On the one hand, the spatial position of individual links to each other is therefore not directly comparable. On the other hand, the movement of a single robot arm changes the spatial point position of each following link in the kinematic chain accordingly - which is not represented by the LCS.For decades, the DH conventions have been considered state of the art for forward and direct kinematics in science and technology. Standard algorithms for backward and inverse kinematics, however, have not been disclosed to the general public. Companies that manufacture robots consider the computational core of their own algorithms to be a "trade secret". Only the operator level is published.Solution approachIn this paper, a general inverse kinematics trajectory control algorithm for serial robot systems (articulated arm and SCARA) is presented. These algorithms systematically avoid the problem of uncontrolled singularity of inverse kinematics. The combination of similar rotation matrices working consistently in the (world) reference coordinate system as well as the normalized vector orientation – introduced in this work – offer a number of advantages. They not only allow a doubtless parameter assignment without danger of confusion of the two length parameters required for each DH matrices. It becomes possible to specify a generally valid algorithm of inverse kinematics for trajectory control.The solution to be published here does not require any additional location coordinates for each moving robot element, it consistently references one and the same (world) reference coordinate system. Any vector position and its orientation in 3D-Space can be compared directly - an essential prerequisite for the snapping algorithms of the inverse kinematics also disclosed here and the additional option of an integrated traversability of the robot on the gantry system. The consistency of the coordinate system enables perspective representation in imaging processes.The vector input values required for this are directly available – in the (world) reference coordinate system (CRS) – for each point of the kinematic chain.For hardware control, kinematic angular values of the robot arm movement are output via a motion protocol. A specially developed simulation visualizes the moving robot silhouette and the path to be traced in a freely selectable perspective.

Norbert L. Brodtmann, Daniel Schilberg

Conference Proceedings

Effects of System Reliability on Workload and Performance in Image Recognition Tasks

Autonomy has found wide-ranging applications, yet its imperfect nature necessitates human oversight and intervention. Investigating autonomy's impact on the operator is pivotal for enhancing human-machine system performance and safety. This study analyzes the effects of autonomous system reliability on operator task performance and mental workload in the context of vehicle type recognition. Experimental findings reveal that autonomy with 90% reliability significantly reduces task completion time and lessens subjective workload. Autonomy with 70% reliability supports the participants, while 50% reliability hampers them, although insignificantly. The reliability threshold for autonomy to have no effect on the participants is around 55%. Autonomy reliability's influence on the operator lies in altering task completion strategies — an all-or-none approach that accelerates task processing speed without improving overall response accuracy. The experiment yielded insights applicable to the design of assistive autonomous systems and the allocation of human-machine functions in real-world tasks.

Xiaodong Xu, Liang Ma, Yun Zhang, Cheng Xu

Conference Proceedings

Enhancing Pedestrian Comprehension through a Bio-Motion eHMI Design for Autonomous Vehicles

Autonomous vehicles are transforming the transportation industry. In conventional traffic environments, human drivers convey intentions to other pedestrians using gestures and facial expressions. Yet, these traditional interactions, vital for safety, are conspicuously absent in autonomous vehicles, leading to comprehension difficulties and heightened street-crossing risks. While current External Human-Machine Interfaces (eHMIs) aim to mitigate this communication void, they often demand prior familiarization and fall short of intuitiveness, complicating the universal interpretation of a vehicle's intent. To address this, we've developed a novel eHMI for autonomous vehicles, capitalizing on biological motion features. These features, represented by moving dots, capture the movement of key joints in fundamental animal behaviors, such as halting and yielding. Drawing from leopards' skeletal and motion patterns, our bio-motion eHMI integrates animal communication metaphors, like 'please let me pass' and 'I will yield,' to enhance clarity in vehicle-pedestrian interactions. We investigate whether integrating these animal-inspired biological motion patterns into autonomous vehicles can bolster pedestrian comprehension of vehicle intent and movement, ultimately fostering safer street-crossing behaviors. 32 Chinese participants engaged in the experiment online, observing video clips that demonstrated vehicular movements via our eHMI. Subsequently, they answered multiple-choice questions assessing their understanding of the vehicle's movement and intent. The results show that the Bio-Motion eHMI significantly outperforms both Text eHMI and Non-display in interpreting vehicle movement. Moreover, both Bio-Motion eHMI and Text eHMI excel over Non-display in discerning vehicle intent. Impressively, the bio-motion eHMI not only stands out in accuracy concerning vehicle intent and movement but also garners superior subjective preferences compared to other interfaces. In conclusion, our biologically-inspired motion-centric eHMI presents a natural conduit for vehicle-to-pedestrian communication, ensuring swift and precise comprehension of vehicle intentions. This pioneering approach has the potential to revolutionize external vehicle interfaces, marking a new chapter in inclusive design within the autonomous vehicle realm.

Ruisi Shi, Xing Chen, Ruolin Huang, Jingyu Zhang

Conference Proceedings

Privacy Concern and Acceptability of Driver Monitoring System

Driver monitoring systems (DMS) are designed to track drivers’ attention status, accumulate real-time data, and intervene when symptoms of fatigue or distraction are observed, thereby enhancing driving safety [1]. In vehicles equipped with partial driving automation [2], the driver’s role necessitates constant attention to road conditions and monitoring of dynamic driving task (DDT). These vehicles usually feature an advanced driver-assistance system (ADAS), but due to limited understanding of ADAS functionalities, drivers might develop an overreliance on these systems. This could potentially lead to misuse or distractions [3]. Consequently, DMS assists in preventing traffic accidents by supporting drivers in their responsibilities, which includes providing responses in instances of driver negligence.The effectiveness of a DMS is directly related to the user’s willingness to share data such as facial images and vehicle behavior. Moreover, it’s often seen that privacy concerns inversely affect the readiness of users to disclose personal information [4]. Unlike technological domain, users’ perspective regarding DMS utilization has been under-explored. This study aims to gather insights into Chinese drivers’ attitudes towards DMS privacy issues and their willingness to adopt this technology. These findings will help evaluate the future prospects of DMS implementation. To facilitate this goal, the existing DMS systems are categorized into four types based on their primary features: facial image-based DMS, electroencephalogram signals-based DMS, electrocardiogram signals-based DMS, and vehicle behavior-based DMS.A one-way between-subjects design was conducted to investigate the influence of various DMS types on psychological perception and behavioral intention using an online survey (N = 486). Each participant was randomly assigned to one of four DMS type conditions. The questionnaire commenced with a succinct introduction to the relevant DMS type, including its name, functions, and methods of data collection. Subsequently, participants were asked to express their agreement or disagreement with 19 items across seven dimensions (data sensitivity, collection concern, secondary use, perceived insecurity, perceived usefulness, trust, and behavioral intention) about the involved DMS on a Likert scale ranging from 1 (totally disagree) to 7 (totally agree). The questionnaire ended up with demographic questions.All demographic variables did not differ significantly among different DMS type conditions. An exploratory factor analysis was conducted on the 19 items, revealing that three factors emerged, named “privacy concern,” “general acceptance,” and “data sensitivity.” Privacy concern is composed of the predetermined factor collection concern, secondary use, and perceived insecurity; general acceptance is composed of perceived usefulness, trust, and behavioral intention. Subsequently, we examined if DMS types influenced participants’ ratings on privacy concern, general acceptance, and data sensitivity. These analyses yielded no significant effects for DMS types on privacy concern and data sensitivity. Regarding general acceptance, participants displayed a positive attitude and significantly preferred vehicle behavior-based DMS. Further, we investigated the effect of DMS types on predetermined factors. The results showed that there was no significant effect for DMS types on collection concern, secondary use, and perceived insecurity. Participants believed that vehicle behavior-based DMS was more useful and trustworthy. Regression analysis indicated that data sensitivity was a positive explanatory variable for general acceptance, however, the privacy concern was a negative one. This study examined data sensitivity, privacy concern, and general acceptance of various DMS among drivers, and explored the factors influencing general acceptance. It was observed that Chinese drivers, in general, hold a favorable view of DMS and express a degree of willingness to use them. They are less worried about privacy and data insecurity. Further exploration is necessary to ascertain the readiness to use DMS in real-world scenarios.References:1. Dong, Y., Hu, Z., Uchimura, K., Murayama, N.: Driver inattention monitoring system for intelligent vehicles: A review. IEEE Trans. Intell. Transport. Syst. 12(2), 596–614 (2011)2. SAE International: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. Society of Automotive Engineering, USA (2021)3. de Winter, J. C. F., Petermeijer, S. M., Abbink, D. A.: Shared control versus traded control in driving: A debate around automation pitfalls. Ergonomics (in press) 1–43 (2022)4. Hoffman, D., Novak, T. P., & Peralta, M.: Building consumer trust online. Commun. ACM. 42(4), 80–85 (1999)

Yueying Chu, Zihui Yuan, Peng Liu

Conference Proceedings

Exploring the effects of speech speed and environmental noise on human and machine performance in civil air traffic control communication tasks

Considering the relative strengths of humans and machines may not be static, this study investigates the effects of speech speed and environmental noise on human and machine performance in the context of civil air traffic control communication. 32 participants were recruited to perform route selection, parameter setting and radio adjustment according to the voice commands from the control tower. Their performance was evaluated with respect to varying levels of speech speed, environmental noise and time pressure. Additionally, human performance was compared to that of a machine (i.e. a voice recognition software). The experimental results showed that both speech speed and environmental noise had significant effects on human performance in terms of recognition accuracy and operation accuracy. Humans excel in situations with high noise and low speech speed, while machines outperform humans when dealing with high speech speed and low noise. The findings demonstrate that a static human-machine function allocation method may not always yield optimal results. Suggestions are provided on how to develop a dynamic allocation method.

Manrong She, Yi Hu, Zhizhong Li

Conference Proceedings

Application of Emerging Technologies to Promote Sustainable Workforce in Construction

The construction industry has been one of the most hazardous and waste-generating industries in the United States for decades, due to the unique nature of work and high degree of organizational complexity on jobsites. A number of citations against OSHA (Occupational Safety and Health Administration) 29 CFR (Code of Federal Regulations) 1926 Safety and Health Regulations for Construction, primarily in sections that address fall protection and safety training in construction, appear in OSHA’s annual top 10 list of most frequently cited violations consistently. Innovative, science-based, and technology-driven solutions become more and more utilized in the construction industry. Examples of these solutions include: situated learning approach to improve the effectiveness of training, wearable technology to enhance personal protection, remote-controlled drones to perform various functions specially to improve site security, prevention through design concept to minimize risks, total worker health initiative to advance worker well-being, etc. It is imperative that safety, health, and environmental professionals should attempt to clearly understand the impact of these emerging technologies on construction safety and health, and be able to apply scientific principles to anticipate, identify, analyze, and control workplace hazards within the construction industry. Specifically, the pros and cons of each solution need to be examined and compared in order to identify effective methods to promote sustainable workforce and improve safety and health in construction.

Lu Yuan

Conference Proceedings

Manufacturing Cell Working in a Cyber-Physical Environment Powered by a Microgrid System

In recent years, automated manufacturing systems have increased, new technologies such as the Internet of Things have emerged, and computer systems need to be more robust.This work shares the experiences obtained using a manufacturing cell that works in a cyber-physical environment powered by a micro grid system. The components that create the cyber-physical system and how they interact are mentioned. Some equipment used are CNC machines, industrial robots, conveyors, an automatic storage and retrieval system, and a plc.Throughout the article, the methodology used to analyze user opinions is shown. Subsequently, suggestions are included so that these systems can be replicated or implemented in other institutions.The conclusions mention the advantages and disadvantages presented during the use of this type of automated manufacturing system. Finally, recommendations are given for future work.

Hector Rafael Morano-okuno, Donovan Esqueda Merino, Emmanuel Garcia-moran, Luis Villagomez-guerrero

Conference Proceedings

Bibliometric Analysis of Research on Persuasive Technology in the Past Decade: A Web of Science-Based Study

In recent years, research in persuasion technology has matured and captured the attention of numerous experts and scholars. In this article, we analyze 1,714 papers from the Web of Science Core Collection database using CiteSpace software, systematically review the theoretical development, hot topics, and research frontiers of persuasion research, and propose future research directions. We summarize the contributions of three authors who have excelled in the field of persuasion and their significant achievements through the co- authorship knowledge graph. Additionally, we concentrate on four major areas of persuasive technology application: exercise and health, sustainability, education, and business, by filtering and categorizing keywords. We also identify research frontiers in persuasive technology through burst keyword analysis. We discovered that the foundational theory of persuasion is well-established and has been widely applied across various domains. Particularly, research in the domains of exercise and health has emerged as a significant area deserving of further investigation. Furthermore, recent developments in persuasion research have centered on exploring the influencing factors of persuasion, encompassing intrinsic aspects that represent user perspectives and extrinsic elements reflecting the impact of the Internet. Therefore, we encourage researchers to delve deeper into these aspects.

Mingzhu Li, Ying Ni

Conference Proceedings

Technicians' attitudes to report unsafe practices in aircraft maintenance

In the civil aviation sector, improper aircraft maintenance is a major contributing factor to significant aviation accidents and incidents. Numerous tasks still heavily depend on human hands-on intervention and are frequently prone to human error. A previous study found that several technicians are reluctant to report an unsafe practice that may violate the current safety guidelines imposed by aircraft maintenance organizations. This study systematically examines factors that influence the willingness of an aircraft maintenance technician (AMT) to report unsafe maintenance practices. Sixty-two AMTs actively practicing aircraft maintenance were interviewed to identify the main factors influencing their desire to report unsafe practices. The study revealed that many respondents chose not to report the violation despite their awareness of unsafe practices. The main factor is the workplace culture, in which the work culture and management style conspire to prevent employees from speaking up for fear of being reprimanded. Peer pressure inside the team is another factor cited in the report. Other common reasons include damage to relationships and retaliation. Many respondents did not personally experience retaliation in the workplace, but this fear of retribution dominates their working attitude. The findings of this study support the view in the literature that maintenance organizations should promote an employee-centric environment in which technicians can report unsafe practices. As part of promoting a safety-conscious work culture, workers should be encouraged to speak up regarding any unsafe maintenance practices, especially those that could lead to near misses or adverse incidents. Further research is necessary to determine cultural factors that affect the technician's safety report commitment.

Steven Tze Fung Lam, Alan Chan

Conference Proceedings

The impact of AI on business ecosystem development: pro and contra

Nowadays the rapid advancements in artificial intelligence (AI) have significantly transformed various industries, including the business ecosystem (Agrawal, A., Gans, J., & Goldfarb, A., 2022). This study aims to examine the multifaceted impact of AI on business ecosystem development, considering both the positive and negative aspects mostly focused on developed countries.The positive effects of AI implementation on the business ecosystem are manifold. AI-powered technologies enhance productivity and efficiency, automate repetitive tasks, and optimize resource allocation(Floridi, L., 2019). Furthermore, AI algorithms enable businesses to gain valuable insights from large volumes of data, leading to improved decision-making processes and the identification of new market trends (Martin, R., & McCrae, D., 2020). However, along with the promising prospects, there are notable concerns surrounding the implementation of AI in the business ecosystem. Ethical issues, such as privacy infringement and data security, arise due to the vast amounts of sensitive information processed by AI systems. (Davenport, T. H., & Ronanki, R. (2018). Furthermore, the concentration of power in AI technologies within a few dominant players can lead to challenges related to market competition and access to AI-driven solutions.This study combines a comprehensive review of existing literature with case studies and expert interviews to provide a balanced assessment of the impact of AI on business ecosystem development. By analyzing real-world examples and industry cases, this research aims to shed light on the practical implications of AI implementation and identify strategies to mitigate potential risks and challenges.The findings of this study will contribute to the ongoing discussions surrounding the integration of AI technologies in the business ecosystem. The results will be of interest to policymakers, business leaders, and researchers, providing valuable insights into harnessing the potential benefits of AI while addressing the associated concerns.

Olga Shvetsova

Conference Proceedings

Breaking the Silence: Unveiling the Power of Compassionate Leadership on Employee Silence

The following paper analyzes the effect of compassionate leadership behavior (CLB) on the phenomenon of employee silence in the organizational context. Applying a quantitative approach, the study employs structural equation modeling (SEM) to examine data collected from a n=138 sample of employees across different industries. The findings indicate a significant negative relationship between compassionate leadership behavior and employee silence, suggesting that higher levels of compassionate leadership behavior are associated with decreased instances of employee silence, especially when it comes to quiescent and acquiescent silence. These results indicate that leaders displaying compassionate leadership behavior can reduce silence caused by fear and even have the ability to break silence due to resignation. Additionally, a statistically significant positive association is observed between compassionate leadership behavior and psychological safety, highlighting the role of compassionate leaders in fostering a supportive work environment where employees feel psychologically safe. These findings underscore the importance of compassionate leadership in cultivating a climate that promotes psychological safety within organizations. Lastly, a positive covariation was found between compassionate leader behavior and servant leadership. The analysis conducted using Amos highlighted the correlations between the variables of Servant Leadership and Leader-Member Exchange, as well as between CLB and Leader-Member Exchange, thereby enhancing the overall model. Since this study is the first one connecting both research streams of compassion and silence, this research contributes to the existing literature by providing novel insights into the potential of compassionate leadership to address employee silence and enhance psychological safety in the workplace. The findings have practical implications for leaders and practitioners aiming to create environments encouraging open communication and employee engagement.

Vinzenz Krause, Célia Rousset, Ina Steinmueller

Conference Proceedings

Quantitative Assessment of Eddy Current Inspection Technician Skills

In eddy current testing, it is desirable to keep sensor perpendicular to test surface, but it is difficult to automatically determine the sensor posture at inspection points with complex geometry, and the non-destructive testing technician manually operates the sensor. In such cases, it is necessary to ensure skill of the technicians as they are part of the non-destructive testing system. We are conducting research to establish a skills training method to efficiently develop non-destructive testing technicians. The behavior of licensed and unlicensed subjects was measured while inspecting defects around bolt holes. There was a clear statistical difference between the sequences of licensed and unlicensed subjects.ts will be established, and a prototype real-time skill teaching system will be built to verify the validity of the proposed method.

Daigo Kosaka, Masahiro Hoshiba, Hiroyuki Nakamoto

Conference Proceedings

From Engagement to Immersion: A Self-Determination Theory and Approach to Gamified Cultural Tourism

In a rapidly growing economy, contemporary tourists are increasingly drawn to unique cultural encounters; this highlights the significance of innovative approaches in promoting cultural tourism. This paper, which relies on self-determination theory, adopts a mixedmethods approach, amalgamating text mining with quantitative and qualitative methodologies, to dissect the interplay of gamification in enhancing engagement within immersive cultural tourism, with a concentrated lens on the “Wizarding World of Harry Potter”. Immersive experiences, intricately crafted from profound narratives, engender deep-seated connections between participants and the embedded tales. Concurrently, the strategic deployment of gamification, while leveraging game mechanics, acts as a potent catalyst in bolstering engagement levels, serving as a conduit to heightened immersion. Rooted in motivational psychology, the tenets of self-determination theory emerge as indispensable when applied to game mechanics, fostering a richer, more holistic engagement and experience.This harmonious confluence of immersive narratives, gamification techniques, and self-determination principles not only augments engagement but, as underscored in this study, propels it toward deeper immersion, satisfying the intricate psychological cravings of tourists. This research provides an illustrative case study that contributes to the ongoing academic and industry discourse through a detailed analysis of selected immersive cultural tourism exemplars. In doing so, the paper paves the way for a more synchronized trajectory in cultural tourism, emphasizing the transition "From Engagement to Immersion" and underscoring the pivotal role of self-determination theory in gamified cultural tourism endeavours.

Lusha Huang, Yuxiang Wang

Conference Proceedings

Investigation of a home office environment and lifestyles of workers that affect their perceived comfort in work-from-home

This study aimed to clarify the living environment at home that is comfortable and less burdensome for workers, targeting work-from-home, which has increased rapidly since the Corona disaster. To this end, a questionnaire survey was conducted on workers' satisfaction, stress reaction, and work engagement, as well as their work status, living space, and living conditions. By analysing the relationships among these factors, the study aims to comprehensively investigate the living environment of workers from home, including not only their physical condition and facilities but also their daily rhythm and relationship with their roommates, and to examine measures for creating a more favourable living environment for workers from home. The work environment during work from home was examined from various perspectives based on the framework of the SHEL model: software (work content, lifestyle, etc.), hardware (furniture, equipment, etc.), environment (indoor environment), and liveware (relationships with family members who live together). Multiple regression analysis was used to analyse the effects of each explanatory variable related to the living environment on the objective variables (satisfaction, work engagement, and stress reaction) related to comfort while working at home. The results suggest that job autonomy and interruptions due to household chores significantly impact the comfort level of work-from-home.

Toshihisa Doi

Conference Proceedings

A Framework for Metro Spatial Regional Cultural Information Design based on Passengers’ Needs for Cognition

The purpose of this study is to explore a theoretical framework for metro spatial and regional cultural information design based on passengers' need for cognitions. Through the analysis and evaluation of the regional cultural information design in the existing subway system, we find that many subway systems do not make full use of regional cultural resources, and cannot meet the passengers' need for cognitions for relevant regional cultural information in the process of subway travel. Through the correlation study between subway passengers' need for cognitions and regional cultural information, we find that regional cultural information plays a special role in catering to and activating passengers' need for cognitions. Meanwhile, through the investigation, we find that subway passengers' demand for regional cultural information is getting higher and higher, so we put forward a framework for subway regional cultural information design based on passengers' need for cognitions. However, we are also aware that there are still some challenges and room for improvement in practical applications. For example, the mining of regional cultural information needs to be more in-depth, and the selection of information transmission methods and channels needs to be more flexible and diversified to meet the needs and preferences of different passengers. To sum up, this study effectively improves passengers' cognition and understanding of regional culture and enables the application of regional culture information in subway space through the design framework of regional culture information based on passengers' need for cognitions. This research results will help the subway system to provide more comprehensive and rich regional cultural information, and improve passengers' travel experience and satisfaction with the subway system. Future studies can further explore more refined and personalized regional cultural information design methods, express more in-depth regional cultural connotations, and serve more accurate user groups.

Wenyi Xu, Jinbo Xu, Zhipeng Zhang

Conference Proceedings

Generative AI Wearable Assistant for Simulated Reach-Back Support

This research investigates the development of a generative AI wearable assistant designed to provide simulated reach-back support for maintenance and troubleshooting applications. Reach-back support refers to accessing expertise remotely to assist individuals in challenging situations. In various domains such as healthcare, emergency response, and technical troubleshooting, reaching out to subject matter experts for real-time guidance can be crucial. Leveraging the capabilities of generative AI, we aim to create a wearable hardware and software device that serves as an assistant that simulates expert knowledge and provides personalized, context-aware (via object detection and a natural language interface) assistance. This poster presents preliminary findings from efforts to demonstrate the technical feasibility of this concept through the design, fabrication, and demonstration of an initial wearable prototype. Future research will seek to develop a deep learning model trained on extensive domain-specific data to generate relevant and accurate responses for maintenance and troubleshooting of specific equipment and systems. The wearable assistant incorporates speech recognition, natural language understanding, speech synthesis, and image-based object detection technologies for seamless communication and contextualization of reach-back requests. The findings from this research have the potential to enhance decision-making, problem-solving, and support capabilities in various professional and emergency scenarios where access to real-time expertise is limited.

Michael Jenkins, Calvin Leather, Richard Stone, Sean Kelly

Conference Proceedings

The cultural center in San Juan del Río, Querétaro: Strengthening and recovering identity

The proposal is based on the design and construction of a cultural center, focused on promoting an ecological culture aimed at the local population whose conditions are difficult due to social disintegration caused mainly by low security and cultural interest in the population. Adhering to the guidelines and standards established in the Construction Regulations of Mexico City and the regulations of the municipality, this proposal consists of the construction and design of 2 elliptical-shaped bodies with 3 levels of construction, on an irregularly shaped property. and substantially flat topography.The main objective is the recovery of the identity of the inhabitants of San Juan del Rio, Querétaro, as well as creating an environmental and sustainable awareness of the region since the lack of awareness and environmental information is reflected in the ecological reserve spaces that it has the region where a great deterioration can be observed; as well as illegal activities on the part of young people and adults, giving a wrong example to generational young people, in addition to the fact that the lack of social awareness has been an important factor for the current situation of global warming since this world problem is eradicated by changing from culture at the social core.

Selene Margarita Vazquez Soto, Jessica Garcia, Miguel angel cruz domínguez Hernández

Conference Proceedings

Beyond The Lockdown: Investigating the Impact of COVID On Technology Adoption In The Construction Industry

The coronavirus pandemic significantly impacted every aspect of human life and endeavour. It caused deep disruptions in every sector due to the various measures enacted to curb its spread. One of the many measures was the lockdown and work-from-home measures. These measures, among other disruptions, informed the adoption of various technologies. Through a quantitative approach, this study studied the impacts of the Covid 19 pandemic by identifying the various technologies and innovations adopted in the construction industry due to the coronavirus pandemic. A well-structured questionnaire was randomly distributed to construction professionals in South Africa. The results reveal that the adopted technologies due to the COVID-19 impacts can be classified into “construction technologies” and “smart building technologies”. Also, the study ranked the various technologies adopted based on their significance.

Samuel Adekunle, Clinton Aigbavboa, Opeoluwa Akinradewo, Peter Adekunle, Osamudiamen Otasowie

Conference Proceedings