AHFE Open AccessHuman Factors and Systems Interaction
Editors: Isabel L. Nunes
Topics: Human Systems Interaction
Publication Date: 2025
ISBN: 978-1-964867-68-7
DOI: 10.54941/ahfe1006003
Articles
A Lip Reading Recognition System Based on SimAM and TCN
Lip-reading recognition is a technology that converts the visual information of a speaker’s lip movements into corresponding textual content. It has broad applications in fields such as national defense, healthcare, and public safety, and holds significant academic value. In recent years, with the rapid advancement of deep learning, lip-reading technology has made notable progress, achieving numerous innovative and breakthrough results. This paper proposes a novel lip-reading recognition architecture that integrates a Residual Network (ResNet) with a Temporal Convolutional Network (TCN), and introduces a simple yet highly effective attention mechanism—Simple Attention Module (SimAM). The key components of the proposed approach are as follows: (1) Feature Extraction: ResNet is employed to extract spatial features from lip images. By introducing residual connections into conventional convolutional neural networks, ResNet effectively alleviates information loss and mitigates the vanishing gradient problem, allowing for more efficient utilization of deep-layer features. (2) SimAM: Traditional attention mechanisms often focus on enhancing features along either the spatial or channel dimension, limiting their ability to learn complex, multi-dimensional attention weights, and typically incurring high computational costs. To address these limitations, SimAM is incorporated. It leverages a spatial suppression mechanism to compute attention weights for each neuron, requiring no additional parameters, while simultaneously attending to both spatial and channel dimensions. (3) Temporal Modeling: TCN is adopted for sequence modeling, applying convolutional operations along the temporal axis. Unlike recurrent networks, TCN enables parallel computation, captures long-range dependencies effectively, and offers a simpler architecture with faster training and greater stability—particularly well-suited for large-scale lip-reading datasets. To validate the effectiveness of the proposed model, experiments were conducted on the largest publicly available lip-reading dataset, LRW, which features diverse pronunciation scenarios and a large number of samples. Comparative experiments with various state-of-the-art architectures demonstrate that the proposed model achieves significant improvements in both recognition accuracy and computational efficiency.
Yi Liu, Yuanyao Lu
Open Access
Article
Conference Proceedings
Developing Effective VR Training Simulations for Additive Manufacturing: A Modular Usability-Driven Design Approach
Hands-on training for additive manufacturing (AM) often faces hurdles like high costs, safety concerns, and limited accessibility. Virtual Reality (VR)-based simulated environments present viable solutions by offering immersive, resource-efficient, and flexible learning environments. This paper introduces a modular, usability-driven design framework for developing effective VR training simulations for Powder Bed Fusion (PBF) processes in AM. Aimed at educators and developers, the framework features distinct modules covering VR familiarization, foundational PBF concepts and safety protocols, guided operational steps, and unguided practice. Key design principles include smooth teleportation for navigation minimizing simulation sickness, clear visual and auditory guiding cues, and accessibility considerations. The framework was evaluated with 12 students using the Simulator Sickness Questionnaire (SSQ) and the System Usability Scale (SUS). Results showed no participant withdrawal due to simulation sickness. System usability was rated favorably on a five-point scale, with most SUS scores between 68 and 78, indicating good to excellent usability. Participants reported confidence (3.25 ±0.45) while using this VR training platform and they required minimal technical support (0.67 ±0.65). This framework provides practical guidance for creating engaging, safe, and effective VR training for additive manufacturing.
Jose Lopez, Eshwara Prasad Sridhar, Tazim Ahmed, Yiran Yang, Shuchisnigdha Deb
Open Access
Article
Conference Proceedings
Marionette-Inspired Interface: Bridging Traditional Puppetry and Modern Avatar Control
As virtual spaces evolve, avatars and robots are becoming significant proxies for human presence across various domains. However, current methods for avatar control, such as motion capture and image recognition, often involve high costs, complex setups, or limited portability. To address these challenges, this study introduces an innovative interface inspired by the manipulation techniques of traditional marionette puppetry, aimed at enhancing interpersonal communication through avatars.Marionette puppetry, which uses tools composed of a puppet and a controller, offers a unique mechanism for abstracting and simplifying human motion. By holding the controller above the puppet, the puppeteer can reproduce complex human movements using only their hands. For example, vertical controllers commonly used in regions like the Czech Republic and Germany enable a single hand to manipulate a puppet’s head and feet, simplifying intricate operations into intuitive gestures. This study focuses on this abstraction capability and adapts it to a digital interface, making avatar and robot control both graceful and user-friendly.The structure and techniques of marionette controllers vary widely depending on their cultural and regional origins. Previous studies have classified controllers into several types, such as vertical, horizontal, angled, and paddle designs, each with unique mechanisms and effects. While this research primarily explores vertical-style controllers, other styles hold potential for diverse applications in digital interfaces. By leveraging these culturally rich control mechanisms, the proposed system translates traditional artistry into cutting-edge technology, offering both simplicity and flexibility.A prototype device and simulated operation system were developed to evaluate the practicality of this concept. In user experiments, participants—including those with no prior experience—successfully operated the device, achieving results comparable to those of advanced existing systems. These findings suggest that marionette-inspired interfaces can democratize access to virtual spaces, offering intuitive and accessible tools for avatar manipulation.Despite these advantages, traditional marionette operation often requires years of practice to master, particularly for artistic expressions. To address this, we have to propose methods to reduce the learning curve while maintaining the expressive richness of traditional puppetry. Further experiments identified areas for improvement, such as enhancing responsiveness and expanding the range of possible movements. These insights will guide future iterations of the device and its associated software.Through this research, we aim to bridge the gap between traditional craftsmanship and modern technology, creating tools that are both innovative and culturally grounded. This study not only demonstrates the potential of marionette-inspired control interfaces but also highlights the broader implications of integrating traditional techniques with contemporary digital systems. The findings underscore the importance of designing intuitive and inclusive technologies that foster deeper engagement and connection in both personal and professional contexts.
Kazumi Inada, Sangtae Kim
Open Access
Article
Conference Proceedings
LightBUY - Developing Cloud Sales Design Specifications from the Ground Up
The sales service associated with cloud products represents a crucial component in the commercialization of the cloud computing sector. Although comprehensive analyses and clear definitions exist within the consumer-to-consumer (C2C) context, a standardized framework for the procurement of cloud computing solutions in the business-to-business (B2B) environment remains undeveloped. It is essential to address various user roles, accommodate intricate cloud computing scenarios, and enhance user efficiency in B2B contexts. This subject is inherently complex, and the substantial variations among cloud computing products necessitate rigorous requirements for high-level design architecture. Therefore, instead of focusing on a system design in its entirety, we are concentrating on the structural architecture with the goal of improving its executability, stability, and coherence. Additionally, we are aiming to provide an industry-standard reference for purchasing scenarios in the cloud computing domain.
Xinmiao Shen, Danlin Song
Open Access
Article
Conference Proceedings
Development of Color Universal Design Education System
Color is one of the most important elements in design. From an educational standpoint, while teaching and communicating with people who have different types of color vision wherein colors are difficult to distinguish is possible, no systematic educational method that allows them to experience such colors firsthand currently exists. Despite the fact that color is a crucial factor in design activities, individuals with different color vision face unique challenges that must be considered. Color vision is categorized into five main types, which include the typical vision and other types such as P-type and D-type. For example, individuals with certain types of color vision may struggle to distinguish commonly used colors like red and green.The concept of Color Universal Design (CUD) aims to create color schemes that can be used seamlessly by individuals with different types of color vision. Many related studies present considerations for designing with accessibility in mind, and these principles are widely applied in the field of design. For instance, selecting appropriate colors, adjusting contrast levels, and incorporating non-color-based design elements such as shapes are essential. This kind of practice is typically taught through in-house workshops and on-the-job training. However, the current study argues that to design effectively for individuals with different types of color vision, to experience their perspective as closely as possible is crucial. Furthermore, gaining such realistic experiences can foster greater awareness and understanding.This research systematizes the concept of CUD into a structured body of knowledge that facilitates understanding and practical application and develops educational tools. Specifically, a tool and framework utilizing cards with colors that are difficult to distinguish were developed along with a workshop design that employs glasses simulating different types of color vision. By using physical cards rather than solely relying on digital displays, this approach broadens the scope of educational and experiential opportunities.A testing workshop with collaborators was also included. During the workshop, participants were interviewed to identify issues and systemic shortcomings. Based on these findings, an ideal educational framework was proposed and key considerations for CUD-focused color education outlined. These insights are intended to inform future educational efforts for individuals with diverse types of color vision. This study assessed the educational effectiveness of the tools and methods.The ultimate goal is to create a tool that students and designers can use to apply CUD knowledge in their future design practices.
Yuki Aoki
Open Access
Article
Conference Proceedings
Realtime Video Underlay for Accessible Television Graphics
Realtime graphics in televised sports have become a staple of the genre. While impressive and effective, existing tools for integrating 3D graphics into televised playing areas are complicated and expensive, limiting their use to only a handful of large-scale television productions. Existing hardware-based solutions are costly and offer limited flexibility. This paper presents a software-based approach for implementing realtime sports graphics on consumer-grade equipment, potentially serving a range of stakeholders who would benefit from the technology but do not have the requisite infrastructure or financial means. Using a combination of open source and widely supported commercial tools, Realtime Video Underlay instead leverages a new approach to tracking physical objects in space and controlling realtime graphics by adapting concepts from augmented reality software and integrating with existing television production and streaming platforms. The results of this research show the potential of software-based methods as a key contender for accessible sports broadcasting for groups with limited resources or technical knowledge. The paper outlines the key components and design of the tool with a specific case study, identifies strengths and weaknesses, and provides an overview of the next version of the tool currently under development. By demonstrating that an inexpensive software-based approach to traditional hardware-based methods is feasible and effective, while offering countless opportunities for further development and expansion, Realtime Video Underlay paves the way for accessible, flexible, and impactful visualizations.
Gordon Carlson, Honesty Beaton
Open Access
Article
Conference Proceedings
The Impact of Cultural Values on Human-AI Collaboration in a Decision-Making Task
As artificial intelligence (AI) becomes increasingly part of everyday life including transportation, manufacturing, etc., it is important to understand how humans utilize AI to achieve an effective human-AI collaboration. Furthermore, it may be possible that one’s interaction with AI is influenced by differences in their cultural values. Currently, literature on cognition differences in cultural values that go beyond the Eastern and Western comparison is lacking. Consequently, the current study examined how behavior and performance in a decision-making (DM) task are influenced by differences in individual cultural values and the presence of an AI decision aide. To examine cultural values, we used Hofstede’s (1984) cultural dimensions: power distance, masculinity, long-term orientation, uncertainty avoidance, and collectivism. Participants completed a DM task consisting of local shapes (e.g., squares and diamonds) encompassed by a larger global shape. They were asked to determine if there were more squares or diamonds from the local shapes, while ignoring the global shape. The global shape matched (GC) or mismatched (GI) the local answer, or the global shape was absent (LO). Participants’ DM was aided by high (80%) or low (60%) accuracy AI. Results showed higher accuracy and faster response times in GC and GI compared to LO. Eye tracking data indicated fewer fixations and longer dwell times in GC and GI compared to LO. Taken together, this may indicate that the global shape, whether it matched or mismatched the correct answer, reduced perceptual demand by acting as a boundary to constrain visual attention. In relation to cultural dimensions, increases in collectivism and long-term orientation predicted decreases in performance only when there was no AI while increases in power distance predicted increases in performance when there was no AI and when AI was highly accurate. Overall, performance may be influenced by cultural values and an AI decision aide.The views expressed are those of the authors and do not necessarily reflect the official policy or position of the Department of the Air Force, the Department of Defense, or the U.S. government. No potential conflict of interest was reported by the authors. Distribution A. Approved for public release; distribution unlimited. AFRL-2025-0181; Cleared 15 Jan 2025.
Riley Schwanz, Tiffany Lui, Elizabeth Fox, Gene Alarcon, August Capiola, James Bliss, George Reis
Open Access
Article
Conference Proceedings
The Impact of Time Constraints on Moral Decision-Making during Human-AI Interaction
Human beings are nowadays increasingly collaborating with autonomous systems in a wide range of activities. As this collaboration has an impact on human decision-making and behavior, it is essential to advance research on Human-Artificial Intelligent (AI) interactions. AI systems are now even employed to support decision-making in sensitive areas such as medicine or defence and security, which can involve decisions with a moral dimension. Understanding better the consequences of the interaction in those contexts is crucial to ensure that both efficiency in the decisions made and ethical considerations are effectively addressed. While AI can improve the quality and speed of decisions and reduce mental workload, it also carries risks such as complacency, loss of situational awareness and skill decay, mainly because AI remains imperfect and errors may occur. These issues are more pronounced with higher AI autonomy, which has been linked to reduced accuracy and responsibility in human decision-making, especially in moral contexts. Task difficulty, such as time pressure, may exacerbate over-reliance on AI. However, these aspects have not yet been sufficiently explored. The present study aims to investigate whether task difficulty, induced by time pressure, could influence moral decision-making in a military population interacting with AI systems. To this end, we conducted an experiment with an ad hoc task in which participants took on the role of drone operators and were asked to decide whether to launch an attack (or not) based on factors such as the presence of enemies and potential risks to allies, civilians, and infrastructure. Participants completed morally and non-morally challenging scenarios under low (15 seconds) and high (4 seconds) time pressure, with and without AI assistance. We hypothesised that increased time pressure would lead to increased overreliance, which would lead participants to rely more on AI advice and influence moral decision-making.
Adriana Salatino, Arthur Prével, Emilie Caspar, Salvatore Lo Bue
Open Access
Article
Conference Proceedings
Is LLM a reliable risk detector? An evaluation of large language models in EMR-related medical incident detection
Medical institutions typically rely on manual analysis of adverse medical events, which requires significant human resources, time, and specialized knowledge and expertise. These requirements reduce the effectiveness of identifying potential risks. Can large language models (LLMs) leverage their powerful natural language processing capabilities to function as reliable risk detectors? In this pilot study, we aim to evaluate the effectiveness of LLMs in identifying electronic medical record system (EMR)-related medical incident risks. We first curated a dataset comprising 573 medical incident reports that had been manually analyzed. Then, using a few-shot prompting approach, we designed instructions to evaluate five LLMs, including GPT-4o, Claude 3.5 Sonnet, DeepSeek V3, Nova Pro, and Llama 3.1-405b. The results indicated that the best-performing LLMs could accurately extract more than half of the risk factors and generate reasonable explanations grounded in real-world case contexts. While general-purpose LLMs can provide some assistance, further optimization tailored to specific medical scenarios is necessary to enhance their capability in handling complex cases.
Siyuan Zhang, Xiuzhu Gu
Open Access
Article
Conference Proceedings
Knowledge of Results (KR) and Vigilance: Are Feedback Effects Due to Information or Motivation?
Vigilance is the mental capacity required to monitor for rare but critical signals in a sequence of non-signal events. Vigilance predominates in many safety-critical fields as well as everyday activities. Unfortunately, humans consistently fail at sustaining attention. Existing vigilance research has found that the provision of feedback in the form of knowledge of results (KR) positively impacts performance. However, the underlying mechanisms driving this performance enhancement remain unclear. The present study evaluated the impact of both informational and motivational dimensions of KR on vigilance task performance. A between-subject design manipulated KR on a simultaneous, cognitive vigilance task. One control, one informational feedback, one motivational feedback, and two neutral feedback conditions were employed in the design. Only those in the informational condition showed improved RTs compared to controls. These observed RT enhancements provide further support for the existing research regarding the effectiveness of KR as well as the Goal Setting Hypothesis. Our findings suggest that the effectiveness of KR is due to the information quality. The motivational component of KR is possibly a product of goal setting and not the primary mechanism driving KR’s effectiveness. This study has implications for training and the design of human-computer systems.
Yazmin Diaz, Peter Hancock
Open Access
Article
Conference Proceedings
Leveraging Digital Twins and Generative AI to Alleviate Loneliness Among Elderly Adults Living Alone Through Smart Flowerpot Design
Loneliness and Social Isolation from elder people present a critical factor in different diseases of aging, necessitating tailored intervention for individuals to effectively engaging with interventions and interacting to their stakeholders. A great deal of research efforts has been made to tackle the challenging issues related to loneliness and social isolations of elder people, mainly focusing on develop various personal and group interventions such as social robots, group/community activity interventions, serious games, and Chatbots. The key barrier to have long-term impacts from these interventions is to make interventions adaptable to individual’s personal needs, scenarios/environments and contexts. To overcome the barrier, how to design adaptable interventions and implement interventions adaptably to meet a person’s needs is a great research question. This research aims to address this research question by (1) applying adaptable design principles into smart intervention design of a smart flowerpot or pet plant, enabled by generative AI and reconfigurable design, (2) applying digital twin technology to create a user’s personal replica with only needed information to support personalized intervention adaption. The objective is to create a smart service system/platform based on Digital Twins and Generative AI to effectively design and deliver smart interventions, for and with elderly individuals and their stakeholders in a smart service ecosystem way. The smart flowerpot is purposely designed to make it like a normal pot flower like most families having them at home, its visitor/user can trim, water, move and care it. The home users typically visit their home flowerpots regularly if not daily. And if a flowerpot becomes very attractive to its users, it can become a pet plant befriending with its users. Some reports already indicates that pet plant can be a useful means for companying elder people at home and make flowerpot-based intervention easier to adapt. On the other hand, to turn an ordinary home flowerpot into a smart flowerpot, we design a special structure for a flowerpot, which not only support a normal flower plant to grew but also can smartly interact with its users/visitors/cares. Within the structure, we equip some IoT sensors to sense a visitor presence, and then connect to the visitor’s personal digital twin to gain better understanding of the visitor’s needs, and finally feed the user’s needs into generative AI and reconfigurable designed elements to provide personalised interventions such as coloured light, display patterns, background music and chats generated by generative AI, etc. This paper presents our research processes, methods and evaluations via a smart flowerpot design, development and testing. The qualitative evaluation shows that our proposed adaptable intervention design and implementation system enabled by digital twin and generative AI technologies has a great potential of bettering understanding of a user’s needs, user engagement with interventions and user experience. Thus, this research has the potential to herald a new era in healthcare for the aging population, fostering smooth smart service and improved quality of life for older adults.
Yuqing Zhang, Shengfeng Qin, Chenyu Ge
Open Access
Article
Conference Proceedings
The Benefits of Adopting Artificial Intelligence-Technologies in Mitigation Construction Risk in the South African Construction Industry
The South African construction industry (SACI) continues to face significant safety and operational efficiency challenges, leading to increased risks, accidents and project delays. This study explores how the adoption of AI-driven technologies can address these persistent issues, particularly in improving risk assessment and mitigation efforts within the industry. A quantitative approach was employed, gathering data through a detailed questionnaire targeting industry professionals including engineers, site managers, construction managers, health and safety officers and quantity surveyors. The analysis employed Mean Item Score and Exploratory Factor Analysis (EFA). The findings revealed that while the adoption of AI-driven technologies in mitigating construction risk in the SACI is still in its infancy, there is growing recognition of its value. The adoption of AI-driven technologies in the SACI will mitigate construction risk such as accidents, site accidents, skills shortages and operational issues currently plaquing the industry. Addressing these barriers will unlock the full potential of AI-driven solutions in transforming risk management and project outcomes. This study contributes to the growing body of research on the use of AI-driven technologies in the construction industry, providing crucial insights into its benefits. The findings will guide industry leaders and policymakers in shaping strategies that encourage the successful adoption of AI in managing construction risk.
Emmanuel Ayorinde, Libuseng Semakale, Fortune Aigbe
Open Access
Article
Conference Proceedings
Determinants of Quality Coping and Knowledge Acquisition in Professional Work and Academic Study Systemic Interaction
In Ghana, most universities have Graduate programs that are designed for working persons and delivered on weekdays’ evenings and/or weekends, using the hybrid system, which enables a “teaching-learning” roll-over between face-to-face and virtual platforms. Despite the usefulness of such hybrid platform, its influence in (re)orienting the mental modes of working students towards quality knowledge acquisition remains unexplored. It is thus evident that discussion on students’ simultaneous engaging in professional work and academic activities on the factors that determine the quality of their knowledge acquisition capabilities, and the coping mechanism they use are mainly disjointed, which situation manifest a knowledge gap. Thus, the purpose of this paper was to identify factors that determine the quality of professional workers’ coping capabilities and knowledge acquisition when they simultaneously engage in professional work and academic activities. This is motivated by the observation that professional workers’ simultaneous engagement in work and schooling can have an adverse effect on the quality of their educational experiences, stress levels and mental health, all of which can lengthen the degree of their completion time, as well as increase the likelihood of their dropping out. This observation has resulted in policymakers and managers of tertiary educational institutions looking at ways for educational structural redesign to provide flexible schooling options to professional workers who combine work with school so as to satisfy their anticipatory self-development and/or work experience enhancement without quitting their jobs. Building on the notion that an individual’s self-regulation system takes shape and gets transformed over lengthy periods of time, with its problems and potentials being understood only against its own history, the argument that an individual’s mode of mental mode may result in his/her (in)ability to accurately cope with the dynamics of work and study leading to the acquisition of quality knowledge is explored, as underlined by the following question; What are the determining factors used by professional workers for quality coping and knowledge acquisition in their professional work and academic study systemic interaction? This study is methodologically guided by the systemic structural activity theorization that the discovery of goals is essential to true activity that can be transformed into contradictions which may influence a person’s metal mode as well as expand into a qualitatively new organizational activity structure and systemic activity contexts. It was also encapsulated in the well-established knowledge that activities of individuals are realized by goal-directed actions, informed either by mental or motor conscious processes, and the notion that one cannot perform a complex motor task without significant mental effort and concentration. Thus, the principal component analytical approach is used to evaluate factors influencing the Work-Academic and coping interactive mechanisms used by the professional workers. This study is the first to be carried out in the education sector in Ghana and the findings will provide useful insight in the systemic design and structuring of hybridized teaching-learning systems (entailing a combination of face-to-face and virtual platforms) to enable quality transitions in the coping and wellness of working students towards knowledge acquisition
Mohammed Aminu Sanda
Open Access
Article
Conference Proceedings
Human Factors Methods in Developing AI and Machine Learning High-Risk Prediction Models in Obstetric Care
This study investigates the application of human factors (HF) methods to the development of artificial intelligence (AI) and machine learning (ML) models for high-risk obstetric (OB) care, focusing on integration within the Epic electronic health record (EHR) system across three hospital systems. A systematic scoping review of 39 AI/ML techniques revealed that none had achieved an acceptable level of clinician acceptance. To address this issue, we propose a human-centered design approach, emphasizing clinical decision-making, workflow alignment, and potential maternal morbidity. Our multi-phase strategy actively engages stakeholders, including OB care providers, to refine system prototypes while considering usability, explainability, trust, and cultural sensitivity. The research aims to establish a roadmap for the future development of high-risk maternal health prediction models.In the first phase (Aim 1), we identify system requirements through stakeholder engagement and literature review, uncovering significant pain points, including communication delays, poor information flow, and the lack of AI/ML tools in practice. The study highlights the importance of addressing these challenges to optimize clinical outcomes.The second phase (Aim 2) focuses on designing AI/ML architectures, aligning system features with HF principles and ensuring standardized data across clinics. Co-designing tools with OB providers resulted in solutions such as smart-SBAR for shift changes and patient-engagement strategies that enhance cultural sensitivity. Providers expressed a strong preference for decision-support tools that complement rather than replace clinical judgment, with transparency in AI outputs seen as critical to building trust.Aim 3, which is ongoing, involves the prototyping and testing of AI/ML tools within clinical workflows, focusing on usability and alignment with existing practices. Preliminary findings suggest that transparent, interpretable AI outputs, along with streamlined workflows, significantly improve clinician trust and utility.In the final phase (Aim 4), the study evaluates both formative and summative aspects of the AI/ML tools, assessing their impact on clinical decision-making, trust, and maternal health outcomes. The evaluation methodology involves assessing model performance, clinician feedback, and model accuracy in simulated environments.By incorporating human factors methods into the development of AI/ML tools for high-risk OB care, the study aims to improve clinician acceptance, enhance decision-making processes, and streamline workflows. These findings contribute to the broader adoption of AI/ML tools in maternal healthcare, with the potential to improve clinical outcomes and patient care efficiency.
Prithima Mosaly, Gregg Tracton, Medha Reddy Thummala, Karl Shieh, Alsion Stuebe
Open Access
Article
Conference Proceedings
ErgoTalks Unplugged: Digital Discourses and Ergonomic Practices Among Remote Knowledge Workers
The mass usage of remote work has precipitated a radical shift in ergonomic education from structured, expert-driven guidance to unstructured, user-generated web-based discourse. This study examines YouTube communities as fluid, informal learning spaces where remote workers actively experiment with ergonomics. Through thematic analysis of high-engagement YouTube videos, the study finds five main thematic pillars: adaptability and personalization, do-it-yourself (DIY) ergonomic solutions, holistic ergonomics emphasizing physical and psychological comfort, peer-generated engagement, and dynamic content relevance. It is apparent from the results that remote workers prefer practical, flexible, and budget-friendly ergonomic approaches over conventional procedures and frequently use everyday objects in innovative ways to optimize their comfort and work efficiency. Quantitative results indicate the best video length of about 20 minutes, finding a balance between depth of information and cognitive load on the audience in the best way. Theoretically, the research applies the Human Factors and Ergonomics (HFE) model to include flexible, peer-mediated informal learning modes. Managerially, the research points out the strategic benefits of integrating informal digital content into organizational ergonomic training programs to foster holistic employee well-being, enhance engagement, and sustain productivity in remote work settings.
Sunday Adewale Olaleye, Esther Olubunmi Olaleye
Open Access
Article
Conference Proceedings
Quality Tools Application in Examining Discomfort Issues at Mining Machinery Operators’ Workplaces
The significance of evaluating human factors issues encountered at mining machinery operation greatly exceeds the amount of available research, given that accidents in mining operations continue to be a recurring concern. This study included 97 Serbian mining machinery operators, who answered the questionnaire which examines injuries and discomfort issues at mining machinery operators’ workplaces. Descriptive statistics was conducted followed by quality tools: Pareto (ABC), Ishikawa, and control charts were performed. ABC analysis found that 41.7% of operators complained to back pain, mostly due to poor working conditions. Back pain is caused by repetitive movements, poor anthropometric adjustments, lack of training, environmental factors, and vibrations, according to the Ishikawa diagram. The attribute control chart shows that no points exceed the lower and upper limits. Thus, examined processes are controlled. A future research avenue is further data collection and expansion of the sample size, as well as the application of other quality tools.
Vesna Spasojevic Brkic, Martina Perišić, Nemanja Janev, Aleksandar Brkic
Open Access
Article
Conference Proceedings
Evaluation of Human Movement Smoothness and influence of signal processing techniques
Movement smoothness is a pivotal parameter for evaluating the quality of human motion, reflecting its fluidity and continuity. This parameter holds significant importance in fields such as industrial ergonomics, medical rehabilitation and sports performance optimization. Metrics such as Spectral Arc-Length (SPARC) and Log of Dimensionless Jerk (LDLJ) are commonly used to quantify smoothness, but the impact of signal segmentation on these measurements remains underexplored. This study investigates how segmenting motion signals influences smoothness assessments in different movement tasks.Objective:The primary aim of this research is to assess the effect of signal segmentation on movement smoothness, specifically comparing smoothness values derived from whole signal analysis versus segmented signal analysis. The study also examines how these effects differ across various movement tasks, such as walking and upper limb motion.Methods:Both synthetic and real-world motion signals were analyzed. Synthetic signals, modeled as sinusoidal and Gaussian profiles, simulate idealized movement behaviors, allowing for controlled examination of the segmentation effect. Real-world motion data were collected using motion and force sensors, representing natural human movements. SPARC and LDLJ metrics were calculated in MATLAB® to evaluate the smoothness of each signal, comparing the results obtained from whole and segmented signals.Results:The analysis reveals that signal segmentation significantly affects smoothness measurements. In periodic movements, segmenting the signal into individual steps leads to different smoothness values compared to analyzing the entire movement as a continuous cycle. These findings underscore that smoothness is context-dependent and influenced by the segmentation approach.Conclusions:This study demonstrates that movement smoothness is not only an inherent property of the movement itself, but it is also a measure influenced by signal processing techniques. The results highlight the importance of standardized segmentation methods for reliable smoothness evaluations. The study provides practical guidelines for using SPARC and LDLJ metrics in different contexts and suggests future research directions to refine smoothness assessment methodologies.
Elena Caselli, Andrea Vottero, Sandra Pieraccini, Stefano Pastorelli, Laura Gastaldi
Open Access
Article
Conference Proceedings
Individual characteristics using pen writing behavior: intra- and inter-individual variability perspectives
Several personal authentication technologies are currently available. Writing movements are consistent among individuals, and each person has unique writing habits. Therefore, this study aims to evaluate the intra and inter-individual variability in pen angles to determine whether writing motions can be used for personal authentication. Sixteen right-handed adults participated in this study. Each participant was asked to write a Japanese name consisting of four kanji characters while seated on a chair. This task was repeated five times. Three-dimensional coordinate data were recorded from both ends of the pen using a motion-capture system. Four pen angles were calculated from the collected data: the horizontal plane angle, sagittal plane angle, frontal plane angle, and three-dimensional tilt angle. The angles were analyzed at the beginning of the first stroke of each character and at characteristic movements specific to Japanese kanji writing, such as "tome (stop)", "hane (upward brushstroke)", and "harai (sweeping stroke)". The standard deviation of the five trials was used as an index of intra-individual variability, while the standard deviation of the mean across the participants was used as an index of inter-individual variability. At all analyzed sites, the sagittal angle exhibited smaller intra-individual variability (1.16-1.63°) and larger inter-individual variability (7.46-9.18°) than the other angles. These results suggest that the sagittal plane may be effective for personal identification. At characteristic moments of movement, the horizontal plane angle was larger than the angle at the beginning of writing for both intra variability (2.90-4.29°) and inter-individual variability (11.10-18.92°). This trend was also observed for the other three pen angles. The conditions of small intra-individual variability and large inter-individual variability are ideal for individual identification. However, these findings suggest that the characteristic movements may not be suitable for personal authentication. Further investigation is required to identify the optimal writing motions for authentication purposes.
Sugiyama Soichi, Noriyuki Kida
Open Access
Article
Conference Proceedings
An Assessment of The Benefits of Circular Economy Principles for Sustainable Construction Practices In South Africa
The concept of circular economy (CE) is a broad concept that presents a set of options for retaining resource value. Therefore, CE is seen as one of the solutions in adopting sustainable practices in the South African Construction Industry (SACI), as a shift from the traditional linear economy. The purpose of this study is to assess the benefits of the adoption of Circular Economy (CE) principles for sustainable construction practices in the SACI, examining the benefits of these principles to facilitate its adoption level in promoting sustainable construction practices. Data was gathered through a questionnaire survey instrument from participants using a purposive sampling technique. The methods deployed for analysing the data for the study were mean item score and exploratory factor analysis. The study found that while there is limited adaptability of CE within the SACI, its benefits trump the challenges associated with adopting of these sustainable construction practices. The holistic and conscious adoption and adaptability of CE in the SACI will result in economic, environmental, social, and industrial benefits, as well as boost South Africa’s global alignment to Sustainable Development Goals (SGGs). Enhancing the adoption and usage level of CE in the SACI will play a vital role in waste reduction, lower carbon footprint, cost savings, job creation, health benefits, skills development, innovation and technological adoption, which is essential for advancing South African economic growth and social development.
Emmanuel Ayorinde, Matlou Pheladi, Ntebo Ngcobo
Open Access
Article
Conference Proceedings