Human-centered Systems Research Articles

Managerial Challenges in Implementing European Rail Traffic Management System, Remote Train Control, and Automatic Train Operation: A Literature Review

This paper explores the management of digitalization projects within the railway industry. It aims to increase and understand the opportunities presented by digitalization and automation in rail operations. Employing a scoping review methodology, this research investigates the execution of European Rail Traffic Management System (ERTMS), remote train control (RTC), and automatic train operation (ATO) projects spanning from 2005 to 2023, with a particular emphasis on metro automation, the remote control of freight and passenger trains, fully automated trains, and highly assisted driving. The refined selection process yielded 30 papers. The analysis of the retrieved papers identified managerial issues, with stakeholder management, change management, and organizational management emerging as recurring themes. Despite the increasing trend in publications, the limited representation managerial issues in ERTMS, RTC, and ATO projects in scientific research persists, with implications for the industry’s advancement. This research sheds light on the critical intersection of change management and digitalization within the railway industry by showing the impact of ERTMS, RTC, and ATO on organizational and scope dynamics. The need for human-centered systems is highlighted, showing the necessity of involving every echelon of the organization in the change management process. These findings provide insights for practitioners, researchers, and policymakers, emphasizing the need for understanding and addressing managerial aspects for successful and sustainable digitalization implementations.

The Four Smarts of Industry 4.0 and barriers for technology deployment: A TOE perspective

Despite ongoing debates, the influence of internal and external barriers on companies deploying the ’Four Smarts’ of Industry 4.0 (I4.0)—Smart Products, Smart Manufacturing, Smart Supply Chain, and Smart Working—remains unclear. This study uses the Technology-Organisation-Environment (TOE) framework to investigate the relationship between the implemented I4.0 technologies associated with the ‘Four Smarts’ and the presence of internal barriers and external barriers on distinct outcomes pursued by companies. Hierarchical regression models are employed on a survey conducted by the Brazilian National Confederation of Industry (CNI). Findings reveal that economic, infrastructure, and knowledge internal barriers are the main obstacles to deploying the ’Four Smarts’, while external barriers do not pose challenges in the deployment phase. In addition, no significant relationship was found between the ‘Four Smarts’ and operational performance. Future studies can investigate the synergy among the ‘Four Smarts’ to overcome such barriers and deploy human-centric systems towards Industry 5.0.

A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system

A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system

Human in the loop: revolutionizing industry 5.0 with design thinking and systems thinking

AbstractThis study delves into Industry 5.0's Human Centric Manufacturing and Systems (HCM and HCS), emphasizing worker welfare and sustainability. Industry 5.0 advocates a human-centric approach, built upon three foundational pillars safety, inclusivity, and empowerment. The paper highlights the successful integration of Design and Systems Thinking in HCM and proposes a workshop at MADE COMPETENCE CENTRE proving the effectiveness in raising awareness and promoting Human-Centric principles throughout the system life cycle and in achieving Human-Centric Systems (HCS).

A Path to Industry 5.0 Digital Twins for Human–Robot Collaboration by Bridging NEP+ and ROS

The integration of heterogeneous hardware and software components to construct human-centered systems for Industry 5.0, particularly human digital twins, presents considerable complexity. Our research addresses this challenge by pioneering a novel approach that harmonizes the techno-centered focus of the Robot Operating System (ROS) with the cross-platform advantages inherent in NEP+ (a human-centered development framework intended to assist users and developers with diverse backgrounds and resources in constructing interactive human–machine systems). We introduce the nep2ros ROS package, aiming to bridge these frameworks and foster a more interconnected and adaptable approach. This initiative can be used to facilitate diverse development scenarios beyond conventional robotics, underpinning a transformative shift in Industry 5.0 applications. Our assessment of NEP+ capabilities includes an evaluation of communication performance utilizing serialization formats like JavaScript Object Notation (JSON) and MessagePack. Additionally, we present a comparative analysis between the nep2ros package and existing solutions, illustrating its efficacy in linking the simulation environment (Unity) and ROS. Moreover, our research demonstrates NEP+’s applicability through an immersive human-in-the-loop collaborative assembly. These findings offer promising prospects for innovative integration possibilities across a broad spectrum of applications, transcending specific platforms or disciplines.

Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: A comparative study

Considering Saudi Arabia's strategy entails economic diversification and a reduction in dependency on oil, a change towards sustainable and efficient energy management practices is required. Effective energy management is critical for optimising energy consumption, lowering costs, and reducing the environmental footprint. Traditional energy management systems are frequently built on rule-based or deterministic techniques that fail to address the complexity and uncertainty inherent in home energy usage patterns and user behaviours. Fuzzy logic-based solutions have emerged in recent years, holding enormous promise for domestic energy management. Fuzzy logic excels at dealing with imprecise and uncertain input, making it ideal for modelling human-centric systems such as domestic energy management. This study presents a cutting-edge fuzzy-based smart energy management system adapted to Saudi residential buildings. Using fuzzy logic approaches, the system intends to optimize energy usage, reduce peak demand, and improve energy efficiency. It automatically adjusts energy-consuming devices such as air conditioning, lights, and appliances depending on user preferences, occupancy patterns, as well as real-time energy prices using fuzzy decision-making algorithms. The research not only substantiates the success of this strategy through a comparative study, but it also highlights its prospective benefits for Saudi Arabian residential constructions. It also covers the obstacles and potential research possibilities for adopting fuzzy-based smart energy management systems, exposing a promising road towards a more sustainable and safe energy future. The study's findings underscore the superiority of the comparative study alternative, revealing it as the most effective means to evaluate the fuzzy-based system's performance and unique advantages in relation to other established energy management approaches.

Advanced Framework for Integrating Risks into an Organizational Setting

The phrase "Industry 4.0" refers to the fourth wave of industrial change, which includes areas like smart cities that aren't often thought of as stand-alone industrial applications. Water power, steam power, and mechanization were all introduced during the start of the first industrial revolution. The second industrial revolution that followed was marked by the expansion of mass production and assembly lines made possible by the use of electricity. The third industrial revolution was sparked by the development of electronics, information technology, and automation. This was followed by the fourth industrial revolution, which was defined by the appearance of systems of cyber-physical. The goal of the scientific discipline of human performance is to increase a system's overall performance as well as the wellbeing of the people who are a part of it. A comprehensive search yielded a total of 336 scholarly papers, out of which 37 were examined using a human-centered system of work paradigm as described in the body of HFE literature. Within the frames of the macro- and microergonomics work system paradigms, difficulties related to technological growth were analyzed. We outline the essential components of an organizational maturity model using the study that was done. Within the unique context of the manufacturing industry's fast technological improvements, this model seeks to improve the overall performance of sociotechnical work system.

Designing the Operator of the Future: The Architecture of Human Digital Twin Systems

The central role that the human operator assumed in industrial design and development processes, driven by Industry 5.0 principles, has paved the way for the creation of new human-centric system architectures. This paper discusses the practical implications regarding the deployment of a Human Digital Twin-based system in the manufacturing environment. It provides a proof-of-concept of the architecture for the implementation of the twinning process, starting from the investigation of the most suitable hardware selection based on the desired outcomes to build up the human monitoring phase. We analyze three management decision-making levels to determine the scalability of the proposed architecture for strategical, technical, and operational managerial strategies. This research aims to propose some technological selection criteria, based on the main characteristics of the available technological acquisition devices, to determine the most suitable sensors for the creation of the physical twinning monitoring process.

Harnessing AI and analytics to enhance cybersecurity and privacy for collective intelligence systems.

Collective intelligence systems like Chat Generative Pre-Trained Transformer (ChatGPT) have emerged. They have brought both promise and peril to cybersecurity and privacy protection. This study introduces novel approaches to harness the power of artificial intelligence (AI) and big data analytics to enhance security and privacy in this new era. Contributions could explore topics such as: leveraging natural language processing (NLP) in ChatGPT-like systems to strengthen information security; evaluating privacy-enhancing technologies to maximize data utility while minimizing personal data exposure; modeling human behavior and agency to build secure and ethical human-centric systems; applying machine learning to detect threats and vulnerabilities in a data-driven manner; using analytics to preserve privacy in large datasets while enabling value creation; crafting AI techniques that operate in a trustworthy and explainable manner. This article advances the state-of-the-art at the intersection of cybersecurity, privacy, human factors, ethics, and cutting-edge AI, providing impactful solutions to emerging challenges. Our research presents a revolutionary approach to malware detection that leverages deep learning (DL) based methodologies to automatically learn features from raw data. Our approach involves constructing a grayscale image from a malware file and extracting features to minimize its size. This process affords us the ability to discern patterns that might remain hidden from other techniques, enabling us to utilize convolutional neural networks (CNNs) to learn from these grayscale images and a stacking ensemble to classify malware. The goal is to model a highly complex nonlinear function with parameters that can be optimized to achieve superior performance. To test our approach, we ran it on over 6,414 malware variants and 2,050 benign files from the MalImg collection, resulting in an impressive 99.86 percent validation accuracy for malware detection. Furthermore, we conducted a classification experiment on 15 malware families and 13 tests with varying parameters to compare our model to other comparable research. Our model outperformed most of the similar research with detection accuracy ranging from 47.07% to 99.81% and a significant increase in detection performance. Our results demonstrate the efficacy of our approach, which unlocks the hidden patterns that underlie complex systems, advancing the frontiers of computational security.

Insights from operationalizing, testing and evaluating the ENRICHER method: INSYTE-Cooley Research Lab Case Study

This paper uses the INSYTE-Cooley Research Lab (I-CRL) as a case study. The view of the I-CRL as a Complex Adaptive System is expressed. Preliminary results from the analyzed data present the ‘themes’ and underlying ‘codes’ from a subset of transcripts. The research methodology uses a meta-methodology of Participation Action Research (PAR) and within the PAR framework the research method is qualitative. Reflective Thematic Analysis (RTA) is used on the transcripts. The transcripts come from regular fortnightly meetings of the I-CRL and some informal meetings. These ‘themes’, ‘codes’ and what they identify are discussed. The study is a longitudinal study. The key outcomes are the creation of a Human-Centered Systems approach to the curation and digitization of cultural heritage both tangible and intangible. The knowledge gained shows a framework within which a new method called the ENRICHER method is being operationalized, tested and evaluated. Some of the findings, so far, show tension points between human centric and techno centric world views and the importance of understanding the curation processes as a forerunner to the formation of a human-machine symbiosis.

A Gender Classification And Age Detection Using Face Recognition

This study presents a novel approach to simultaneous gender and age recognition by including emotional context using a mask region-based convolution neural network (Mask R-CNN). The suggested approach makes exact gender and age categorization possible by using deep learning to examine facial traits and emotions. The model captures the complex relationship between emotional states and face characteristics by extending Mask R-CNN to include emotional signals. By enhancing overall system performance, this integration offers a more comprehensive comprehension of human behavior. The suggested method achieves state-of-the-art accuracy in gender and age recognition while capturing emotional nuances in facial expressions, as demonstrated by empirical data. This work advances the development of multimodal human-centric systems and has potential applications in a variety of domains, including surveillance, human-computer interaction, and tailored experience.

A human-centric system combining smartwatch and LiDAR data to assess the risk of musculoskeletal disorders and improve ergonomics of Industry 5.0 manufacturing workers

More than one in four workers reportedly suffer from back pain worldwide, leading to 264 million work days lost yearly. In the U.S. alone, it causes $50 billion in healthcare costs every year, up to $100 billion if including decreased productivity and lost wages. The upcoming Industry 5.0 revolution will introduce human-centric manufacturing systems where workers’ well-being comes first while safeguarding their rights: privacy, autonomy, and human dignity. This paper presents a privacy-preserving system based on artificial intelligence that tracks the posture of assembly/disassembly line workers while performing typical standardized tasks on repeat: connecting and separating parts; screwing and unscrewing using an electric screwdriver; tin soldering and unsoldering. The proposed solution assesses the upper-body (dominant arm, dominant shoulder, and trunk) and lower-body (legs) postures according to the ISO 11226 European standard, based on inertial data recorded by a smartwatch and using Laser imaging Detection and Ranging (LiDAR), respectively. These techniques preserve privacy as they collect data that cannot reveal the worker’s identity or sensitive information. Experiments showed that the system recognized a worker’s posture with a mean accuracy close to 98%. The system could help detect bad posture habits and reduce the chances of musculoskeletal disorders while preserving the workers’ privacy, in compliance with the upcoming Industry 5.0 paradigm.

Project APRED: A web-based data analytics platform for supporting community disaster resilience

In this paper, we introduce the Analysis Platform for Risk, Resilience, and Expenditure in Disasters (APRED)—a disaster-analytic platform developed for crisis practitioners and economic developers across the United States (US). APRED provides practitioners with a centralized platform for exploring disaster resilience and vulnerability profiles of all counties across the US. The platform comprises five sections including: (1) Disaster Resilience Index, (2) Business Vulnerability Index, (3) Disaster Declaration History, (4) County Profile, and (5) Storm History sections. We further describe our end-to-end human-centered design and engineering process that involved contextual inquiry, community-based participatory design, and rapid prototyping with the support of US Economic Development Administration representatives and regional economic developers across the US. Findings from our study revealed that distributed cognition, content heuristic, shareability, and human-centered systems are crucial considerations for developing data-intensive visualization platforms for resilience planning. We discuss the implications of these findings and inform future research on developing sociotechnical visualization platforms to support resilience planning.

Межрегиональные неравенства в контексте диспропорций развития малых и крупных городов России

The article examines the problems of development, disproportions and spatial inequality of Russian regions, the reasons for the economic and sociocultural stagnation of small regional cities, the lack of balance in the development of large cities and small settlements in the context of interregional inequality. An analysis of the study of problems and reasons for updating the quality of life of the population of small towns is presented, and the possibilities for their development are considered. It is emphasized that population growth in agglomera-tions does not always lead to better living conditions. The emphasis is laid on the human-centered system of assessment of this indicator, which is conditioned by cultural characteristics and value system. It is stressed that in modern Russia regional management uses a model of active social policy based on the inclusion of human capital in the process of overcoming the inequalities of territories. Conclusion emphasizes the fact that promot-ing the development of civic and creative potential of small town residents to preserve the historical and cultural heritage of small settlements is the most important task of the authorities in the context of Russia’s strategic geopolitics.

Assessment of the Crew On-Duty Status Based on the Dynamic Probabilistic Risk Platform

Shipping operations are high risk, complex, and uncertain activities. And a ship collision can have catastrophic consequences. The behavior and status of the crew on duty are key factors that affect the navigation safety of the ship. This paper proposes a dynamic probabilistic risk platform of Maritime Accident Dynamics Simulation (MADS) and Information, Decision, and Action in a Crew context cognitive model (IDAC). The platform simulates the cognitive and decision making processes of the crew on duty in maritime incidents and dynamically analyzes the reliability of human decisions. It can predict events and their probability values that may lead to decision failures in knowledge driven task failure scenarios. The effectiveness of the platform is verified through a case study of a ship collision. The results show that the method can simulate dynamic critical scenarios of ship collision avoidance and achieve intelligent decision-making in a human centered system.

The Connections Matter: Bi-Directional Learning in Program Evaluation and Practice-Oriented Research.

Intended for researchers and clinical leaders, this article suggests that embedded program evaluation is a good fit with the desired features of practice-oriented research. The systematic nature of evaluation that is built into the operational workflow of a practice setting may increase the diversity of methods available to explore processes and outcomes of interest. We propose a novel conceptual framework that uses a human-centered systems lens to foster such embedded evaluation in clinical routine. This approach emphasizes the evaluator-practitioner partnership to build confidence in the bi-directional learning of practice-based evidence with evidence-based practice. The iterative cycles inherent to design thinking are aimed at developing better evaluation questions. The attention to structure and context inherent to systems thinking is intended to support meaningful perspectives in the naturally complex world of health care. Importantly, the combined human-centered systems lens can create greater awareness of the influence of individual and systemic biases that exist in any endeavor or institution that involves people. Recommended tools and strategies include systems mapping, program theory development, and visual facilitation using a logic model to represent the complexity of mental health treatment for communication, shared understanding, and connection to the broader evidence base. To illustrate elements of the proposed conceptual framework, two case examples are drawn from routine outcome monitoring (ROM) and progress feedback. We conclude with questions for future collaboration and research that may strengthen the partnership of evaluators and practitioners as a community of learners in service of local and system-level improvement.

Geocoding Applications for Social Science to Improve Earthquake Early Warning

Geocoding is a spatial analysis method that uses address information (e.g., street address, intersection, census tract, zip code, etc.) to determine geographical coordinates (latitude and longitude). In recent decades, geocoding has gone beyond its primary use for census and demographic information to novel applications in disaster risk reduction, even to earthquake early warning. Here I demonstrate the usefulness of geocoding techniques to earthquake early warning systems as applied to case studies that relied on survey response data and crowd-sourced video footage. These datasets were initially collected to understand the efficacy of tests conducted on ShakeAlert®, the earthquake early warning system for the West Coast of the United States, and how people behave during earthquakes, respectively. Geocoding these data can improve our overall technical understanding of the system, demonstrate whether individuals take protective actions such as ‘Drop, Cover, and Hold On’, and spotlight community demographics that the system is reaching or unintentionally missing. The combination of these social science datasets with geocoding information deepens our knowledge of these fundamentally human-centered systems, including how to improve the distribution of alerts for people and individuals with access and functional needs. In the future, this work may help verify U.S. Geological Survey ‘Did You Feel It?’ responses and seismic intensity, especially in regions with sparse seismic networks.

Application and evolution of design in oral health: A systematic mapping study with an interactive evidence map.

There is increasing recognition of the value and capabilities of design in healthcare. Beyond the development of medical devices, design is increasingly being applied to intangible, complex and systemic healthcare problems. However, there is limited evidence on the use of design specifically in the field of oral health. This systematic mapping study aims to collate and catalogue evidence of design in oral health. A systematic search of academic databases and grey literature was performed. Duplicate results were removed, and publications relating to the same project were grouped. Reviewers from design and oral health independently screened a sample of the dataset. Projects of both relevance to oral health, and with input from a designer or clear implementation of a design methodology or approach were included. Projects were coded and plotted on a novel interactive evidence map. 119 design and oral health projects were included between 1973 and 2022. Interventional (n = 94, 79%), empirical (n = 46, 39%), methodological (n = 35, 29%) and theoretical (n = 7, 6%) design contributions were identified across the projects. The projects were categorized by four orders of design: first-graphics (n = 6, 5%), second-products (n = 41, 34%), third-interactions (n = 70, 59%), and fourth-systems (n = 2, 2%). Design was found in a diverse range of contexts in oral health; most commonly being relevant to general patients (n = 61, 51%), and for use in general dental practice (n = 56, 47%). Further design outcome categories (digital material; printed material; object; room or space; apparel; process; smart device; tangible interface; graphical interface; virtual reality; service; policy; system) and oral health themes (oral health literacy; oral care training; dental clinic design; dental instruments and equipment; personal oral care; dental appliance; clinician health and productivity; clinical information systems; informed consent; oral health promotion and prevention; oral care training; patient interactions and experience) were identified. The novel interactive evidence map of design in oral health created enables ongoing and open-ended multivariant documentation and analysis of the evidence, as well as identification of strategic opportunities. Future research and policy implications include; recognition and engagement with the full capabilities of design; integration of design experts; fostering inclusive engagement and collaboration; disentangling patient and public involvement; advancing human-centred systems approaches; adopting design-led approaches for policy-making.

A Bibliometric Analysis of Digital Twin in the Supply Chain

Digital twin is the digital representation of an entity, and it drives Industry 4.0. This paper presents a bibliometric analysis of digital twin in the supply chain to help researchers, industry practitioners, and academics to understand the trend, development, and focus of the areas of digital twin in the supply chain. This paper found several key clusters of research, including the designing of a digital twin model, integration of a digital twin model, application of digital twin in quality control, and digital twin in digitalization. In the embryonic stage of research, digital twin was tested in the production line with limited optimization. In the development stage, the importance of digital twin in Industry 4.0 was observed, as big data, machine learning, Industrial Internet of Things, blockchain, edge computing, and cloud-based systems complemented digital twin models. Digital twin was applied to improve sustainability in manufacturing and production logistics. In the current prosperity stage with high annual publications, the recent trends of this topic focus on the integration of deep learning, data models, and artificial intelligence for digitalization. This bibliometric analysis also found that the COVID-19 pandemic drove the start of the prosperity stage of digital twin research in the supply chain. Researchers in this field are slowly moving towards applying digital twin for human-centric systems and mass personalization to prepare to transit to Industry 5.0.

Analysis of Human Performance in Manufacturing an Detailed Framework on Industry 4.0

The phrase "Industry 4.0" refers to the fourth wave of industrial change, which includes areas like smart cities that aren't often thought of as stand-alone industrial applications. Waterpower, steam power, and mechanization were all introduced during the start of the first industrial revolution. The second industrial revolution that followed was marked by the expansion of mass production and assembly lines made possible by the use of electricity. The third industrial revolution was sparked by the development of electronics, information technology, and automation. This was followed by the fourth industrial revolution, which was defined by the appearance of systems of cyber-physical. The goal of the scientific discipline of human performance is to increase a system's overall performance as well as the wellbeing of the people who are a part of it. A comprehensive search yielded a total of 336 scholarly papers, out of which 37 were examined using a human-centered system of work paradigm as described in the body of HFE literature. Within the frames of the macro- and micro ergonomics work system paradigms, difficulties related to technological growth were analyzed. We outline the essential components of an organizational maturity model using the study that was done. Within the unique context of the manufacturing industry's fast technological improvements, this model seeks to improve the overall performance of sociotechnical work system.