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Technology-enabled cognitive resilience: what can we learn from military operation to develop Operator 5.0 solutions?
Technology-enabled cognitive resilience: what can we learn from military operation to develop Operator 5.0 solutions?
The Operator 5.0 concept calls for the self-resilience of operators in Industry 5.0, including the cognitive aspect. Despite attempts to develop supporting technologies, achieved results are loosely connected without a comprehensive approach. Looking for novel expectations, this study seeks inspiration from a chaotic environment, where cognitive resilience is a firm standard: military operations. A systematic literature review in Scopus investigated how technology-enabled cognitive resilience is achieved in this context. Extracted details show vast technology support from field operations to control space, against the single or corporate effect of stressors from the work environment, context, content, or users themselves. These technologies generate indirect and direct influence from physical, mental, and cognitive aspects, creating a cognitive resilience effect. The concept of human-machine symbiosis is proposed, with a framework from technology development to resilience training, to inspire developers to define a broader scope, and engineers to facilitate comprehensive adaptation of Operator 5.0 solutions.
Post Date: 27 June 2024
Knowledge Graph-Based Framework to Support Human-Centered Collaborative Manufacturing in Industry 5.0
Knowledge Graph-Based Framework to Support Human-Centered Collaborative Manufacturing in Industry 5.0
The importance of highly monitored and analyzed processes, linked by information systems such as knowledge graphs, is growing. In addition, the integration of operators has become urgent due to their high costs and from a social point of view. An appropriate framework for implementing the Industry 5.0 approach requires effective data exchange in a highly complex manufacturing network to utilize resources and information. Furthermore, the continuous development of collaboration between human and machine actors is fundamental for industrial cyber-physical systems, as the workforce is one of the most agile and flexible manufacturing resources. This paper introduces the human-centric knowledge graph framework by adapting ontologies and standards to model the operator-related factors such as monitoring movements, working conditions, or collaborating with robots. It also presents graph-based data querying, visualization, and analysis through an industrial case study. The main contribution of this work is a knowledge graph-based framework that focuses on the work performed by the operator, including the evaluation of movements, collaboration with machines, ergonomics, and other conditions. In addition, the use of the framework is demonstrated in a complex use case based on an assembly line, with examples of resource allocation and comprehensive support in terms of the collaboration aspect between shop-floor workers.
Post Date: 17 April 2024
Extension of HAAS for the Management of Cognitive Load
Extension of HAAS for the Management of Cognitive Load
The rapid advancement of technology related to Industry 4.0 has brought about a paradigm shift in the way we interact with assets across various domains. This progress has led to the emergence of the concept of a Human Digital Twin (HDT), a virtual representation of an individual’s cognitive, psychological, and behavioral characteristics. The HDT has demonstrated potential as a strategic tool for enhancing productivity, safety, and collaboration within the framework of Industry 5.0. In response to this challenge, this paper outlines a process for tracking human cognitive load using the galvanic skin response as a physiological marker and proposes a novel method for managing cognitive load based on the extended Human Asset Administration Shell (HAAS). The proposed HAAS framework integrates real-time data streams from wearable sensors, user skills, contextual information, task specifics, and environmental and surrounding conditions to deliver a comprehensive understanding of an individual’s cognitive state, physical wellness, and skill set. Through the incorporation of skills set, physical, physiological, and psychological variables, and task parameters, the developed HAAS framework enables the identification, management, and development of individual capabilities, thereby facilitating individualized training and knowledge exchange. The applicability of the developed framework is proved by an experiments in the Operator 4.0 laboratory with the detailed HAAS parameters.
Post date: 30 January 2024
Extending factory digital Twins through human characterisation in Asset Administration Shell
Extending factory digital Twins through human characterisation in Asset Administration Shell
This paper extends the traditional factory digital twins by incorporating human characterisation in Asset Administration Shell (AAS). The extension lays the basis for human-centred control and management, as demonstrated by employing a prototype of the extended AAS in two proposed use cases. Referred to Industry 5.0, an accurate digital representation of humans as a basis of the data-based decision support to improve operators’ well-being and resilience. The AAS is extended to include dedicated digital models accommodating a set of properties to describe the human operators and its interactions with the surrounding shop-floor resources. Two reference use cases have been designed in the context of a complete lab-scale manufacturing system: equipment and devices have been modelled according to the AAS standard, exposing information via MQTT, and have been integrated with the proposed AAS definition of human operators. Operators have been equipped with wearable sensors and a dashboard providing them with feedback from the manufacturing environment and notifications about changes. As part of the extension process, some ethical and regulation concerns are discussed, highlighting that the extended AAS is mature enough to support the inclusion of human operators, but regulations struggle to keep up with technological advances.
Post date: 30 January 2024
Indoor Positioning-based Occupational Exposures Mapping and Operator Well-being Assessment in Manufacturing Environment
Indoor Positioning-based Occupational Exposures Mapping and Operator Well-being Assessment in Manufacturing Environment
This research was motivated by the need for detailed information about the spatial and contextualized distribution of occupational exposures, which can be used to improve the layout of the workspace. To achieve this goal, the study emphasizes the need for position-related information and contextualized data. To address these concerns, the study proposes the use of Indoor Positioning System (IPS) sensors that can be further developed to establish a set of metrics for measuring and evaluating occupational exposures. The proposed IPS-based sensor fusion framework, which combines various environmental parameters with position data, can provide valuable insights into the operator’s working environment. For this, we propose an indoor position-based comfort level indicator. By identifying areas of improvement, interventions can be implemented to enhance operator performance and overall health. The measurement unit installed on a manual material handling device in a real production environment and collected data using temperature, noise, and humidity sensors. The results demonstrated the applicability of the proposed comfort level indicator in a wire harness manufacturing setting, providing location-based information to enhance operator well-being. Overall, the proposed framework can be used as a tool to monitor the industrial environment, especially the well-being of shop floor operators.
Post date 12 October 2023
Assessing human worker performance by pattern mining of Kinect sensor skeleton data
Assessing human worker performance by pattern mining of Kinect sensor skeleton data
The human worker is an in-disposable factor in manufacturing processes. Traditional observation methods to assess their performance is time-consuming and expert-dependent, while it is still impossible to diagnose the detailed movement trajectory with the naked eye. Industry 4.0 technologies can innovate that process with smart sensors paired with data mining techniques for automated operation and develop a database of frequent movements for corporate reference and improvement. This paper proposes an approach to automatically assess worker performance with skeleton data by applying pattern mining methods and supervised learning algorithms. A use case is performed on an electrical assembly line to validate the approach, with the skeleton data collected by Kinect sensor v2. By using supervised learning, the movements of workers in each workstation can be segmented, and the line performance can be assessed. The work movement motifs can be recognized with pattern mining. The mined results can be used to further improve the production processes in terms of work procedures, movement symmetry, body utilization, and other ergonomics factors for both short and long-term human resource development. The promising result motivates further utilization of easy-to-adopt technology in Industry 5.0, which facilitates human-centric data-driven improvements.
Post date 12 September 2023
Current development on the Operator 4.0 and transition towards the Operator 5.0: A systematic literature review in light of Industry 5.0
Current development on the Operator 4.0 and transition towards the Operator 5.0: A systematic literature review in light of Industry 5.0
Technology-driven Industry 4.0 (I4.0) paradigm combined with human-centrism, sustainability, and resilience orientation, forms the Industry 5.0 (I5.0) paradigm, providing support for the workforce and enabling the Operator 4.0 (O4.0) approach. The I5.0 focuses can face unforeseen challenges, as the applicability and readiness of I4.0 solutions are still not well discussed in the literature. Therefore, structuring existing knowledge of O4.0 to prepare for the smooth transition toward Operator 5.0 (O5.0) is crucial. A systematic literature review is performed in the Scopus database, considering publications up to 31 December 2022. Bibliography Network Analysis (BNA), text mining techniques (i.e., Latent Dirichlet Allocation (LDA), BERTopic), and knowledge graph (KG) were deployed on the retrieved abstracts. The full-text examination is carried out over papers matched by LDA and BERTopic. From the BNA result of 279 relevant papers, clusters of active researchers and topics were found, while text-mining results revealed trending and missing research directions. The extracted details from the full text of 81 papers reflected the coverage and development levels of O4.0 types with the preparation for resilience, human-centrism, and sustainability. Achieved results suggest that though the O5.0 transition is inevitable, I4.0 technologies are not ready with sufficient human factor integration. Missing research orientations including integrated sustainability from the human perspective, or system resilience, concerning drivers and restrainers for technology adoption. To prepare for O5.0, discussed O4.0 drivers can help to shape the favorable conditions, and the restrainers should be mitigated before adopting human-centric technologies. Further study including grey literature is necessary to exploit more industrial and policy-making perspectives.
Post date 04 August 2023
Heart Rate Variability Measurement to Assess Acute Work-Content-Related Stress of Workers in Industrial Manufacturing Environment—A Systematic Scoping Review
Heart Rate Variability Measurement to Assess Acute Work-Content-Related Stress of Workers in Industrial Manufacturing Environment—A Systematic Scoping Review
Background: Human workers are indispensable in the human–cyber-physical system in the forthcoming Industry 5.0. As inappropriate work content induces stress and harmful effects on human performance, engineering applications search for a physiological indicator for monitoring the well-being state of workers during work; thus, the work content can be modified accordingly. The primary aim of this study is to assess whether heart rate variability (HRV) can be a valid and reliable indicator of acute work-content-related stress (AWCRS) in real time during industrial work. Second, we aim to provide a broader scope of HRV usage as a stress indicator in this context. Methods: A search was conducted in Scopus, IEEE Xplore, PubMed, and Web of Science between 1 January 2000 and 1 June 2022. Eligible articles are analyzed regarding study design, population, assessment of AWCRS, and its association with HRV. Results: A total of 14 studies met the inclusion criteria. No randomized control trial (RCT) was conducted to assess the association between AWCRS and HRV. Five observational studies were performed. Both AWCRS and HRV were measured in nine further studies, but their associations were not analyzed. Results suggest that HRV does not fully reflect the AWCRS during work, and it is problematic to measure the effect of AWCRS on HRV in the real manufacturing environment. The evidence is insufficient for a reliable conclusion about the HRV diagnostic role as an indicator of human worker status. Conclusion: This review is valuable in the Operator 4.0 paradigm, calling for more trials to validate the use of HRV to measure AWCRS on human workers.
Post date: 11 July 2023
The human-centric Industry 5.0 collaboration architecture
The human-centric Industry 5.0 collaboration architecture
While the primary focus of Industry 4.0 revolves around extensive digitalization, Industry 5.0, on the other hand, seeks to integrate innovative technologies with human actors, signifying an approach that is more value-driven than technology-centric. The key objectives of the Industry 5.0 paradigm, which were not central to Industry 4.0, underscore that production should not only be digitalized but also resilient, sustainable, and human-centric. This paper is focusing on the human-centric pillar of Industry 5.0. The proposed methodology addresses the need for a human-AI collaborative process design and innovation approach to support the development and deployment of advanced AI-driven co-creation and collaboration tools. The method aims to solve the problem of integrating various innovative agents (human, AI, IoT, robot) in a plant-level collaboration process through a generic semantic definition, utilizing a time event-driven process. It also encourages the development of AI techniques for human-in-the-loop optimization, incorporating cross-checking with alternative feedback loop models. Benefits of this methodology include the Industry 5.0 collaboration architecture (I5arc), which provides new adaptable, generic frameworks, concepts, and methodologies for modern knowledge creation and sharing to enhance plant collaboration processes.
The I5arc aims to investigate and establish a truly integrated human-AI collaboration model, equipped with methods and tools for human-AI driven co-creation.
Provide a framework for the co-execution of processes and activities, with humans remaining empowered and in control.
The framework primarily targets human-AI collaboration processes and activities in industrial plants, with potential applicability to other societal contexts.
Post date: 18 June 2023
Self-improving situation awareness for human–robot-collaboration using intelligent Digital Twin
Self-improving situation awareness for human–robot-collaboration using intelligent Digital Twin
The situation awareness, especially for collaborative robots, plays a crucial role when humans and machines work together in a human-centered, dynamic environment. Only when the humans understands how well the robot is aware of its environment can they build trust and delegate tasks that the robot can complete successfully. However, the state of situation awareness has not yet been described for collaborative robots. Furthermore, the improvement of situation awareness is now only described for humans but not for robots. In this paper, the authors propose a metric to measure the state of situation awareness. Furthermore, the models are adapted to the collaborative robot domain to systematically improve the situation awareness. The proposed metric and the improvement process of the situation awareness are evaluated using the mobile robot platform Robotino. The authors conduct extensive experiments and present the results in this paper to evaluate the effectiveness of the proposed approach. The results are compared with the existing research on the situation awareness, highlighting the advantages of our approach. Therefore, the approach is expected to significantly improve the performance of cobots in human–robot collaboration and enhance the communication and understanding between humans and machines.
Post date: 19 April 2023
Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0
Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0
One of the main challenges of Industry 4.0 is how advanced sensors and sensing technologies can be applied through the Internet of Things layers of existing manufacturing. This is the so-called Brownfield Industry 4.0, where the different types and ages of machines and processes need to be digitalized. Smart retrofitting is the umbrella term for solutions to show how we can digitalize manufacturing machines. This problem is critical in the case of solutions to support human workers. The Operator 4.0 concept shows how we can efficiently support workers on the shop floor. The key indicator is the readiness level of a company, and the main bottleneck is the technical knowledge of the employees. This study proposes an education framework and a related Operator 4.0 laboratory that prepares students for the development and application of Industry 5.0 technologies. The concept of intelligent space is proposed as a basis of the educational framework, which can solve the problem of monitoring the stochastic nature of operators in production processes. The components of the intelligent space are detailed through the layers of the IoT in the form of a case study conducted at the laboratory. The applicability of indoor positioning systems is described with the integration of machine-, operator- and environment-based sensor data to obtain real-time information from the shop floor. The digital twin of the laboratory is developed in a discrete event simulator, which integrates the data from the shop floor and can control the production based on the simulation results. The presented framework can be utilized to design education for the generation of Industry 5.0.
Post date: 03 January 2023
Human-centered knowledge graph-based design concept for collaborative manufacturing
Human-centered knowledge graph-based design concept for collaborative manufacturing
With the increasing importance of highly connected and monitored processes supported by industrial information systems, such as knowledge graphs, the integration of the operator has become urgent due to its high cost and is also to be appreciated from a social point of view. The facilitation of collaboration between humans and machines is a fundamental target for Industrial Cyber-Physical Systems, as the workforce is the most agile and flexible manufacturing resource. Furthermore, the design of such a framework requires effective systems to utilise resources and information. This paper aims to provide recommendations of ontologies and standards that can support monitoring work conditions, scheduling, planning and supporting the operator and the possibilities to formalise the classic work instructions to analyse the unique activities. The main contributions of the work are that it proposes a design work-frame of a knowledge graph where the work performed by the operator is in the scope, including the evaluation of movements, collaboration with machines, work steps, ergonomics and other conditions. The paper highlights that activity recognition technologies can enhance the utilisable data in a knowledge graph for a smart factory. With this approach, the future goal may be to automate the entire data collection and knowledge exploration processes, which can facilitate the support of the human-digital twin and the implementation of augmented reality technologies in the Industry 5.0 concept.
Post date: 25 October 2022
Intelligent Collaborative Manufacturing Space for Augmenting Human Workers in Semi-Automated Manufacturing Systems
Intelligent Collaborative Manufacturing Space for Augmenting Human Workers in Semi-Automated Manufacturing Systems
Manufacturing companies are facing two major trends affecting their business operations: "automatization" and "collaboration". Companies have realized that they still need humans on the shop floor beside the availability of high levels of automation solutions in the market. This realization has created a new Industrial Revolution known as "Industry 5.0". While the primary concern in Industry 4.0 is about achieving high levels of full automation, Industry 5.0 focuses on creating synergies between humans and autonomous machines in semi-automated manufacturing systems toward flexible, resilient, and sustainable systems. The critical element of human-automation synergies is a better understanding of the excellent cooperation between humans and making a better collaboration between humans and autonomous machines inspired by it. The proposed Intelligent Collaborative Manufacturing Space (ICMS) aims to create a framework for supporting collaborations based on smart sensor networks and data science techniques. Four main elements or sub-spaces characterize this "Intelligent Workspace": (i) the Working Space, (ii) the Monitoring Space, (iii) the Modelling Space, and (iv) the Decision Space. The ICMS is a framework envisioned for supporting the effective collaboration between humans and automated and semi-automated production assets based on activity recognition and prediction paired with machine learning optimization algorithms. A methodology for developing ICMSs is described in detail in this paper.
Post date: 25 October 2022
Trajectory Prediction of Moving Workers for Autonomous Mobile Robots on the Shop Floor
Trajectory Prediction of Moving Workers for Autonomous Mobile Robots on the Shop Floor
In partially automated manufacturing, humans work together with mobile robots. Trajectory prediction, i.e. predicting future positions of human workers, improves collaboration and coexistence between humans and robots on the shop floor. In this paper, we discuss the interrelated research questions of how human motion trajectories can be predicted and how mobile robots such as Autonomous Mobile Robots and Automated Guided Vehicles can take such predictions into account in their pathfinding and navigation. On the robot side, advanced D* pathfinding algorithms allow robots to take dynamic obstacles into account. For trajectory prediction, the position of human workers is determined by an Ultra-Wideband-based Real-Time Locating System. A trajectory prediction framework is introduced to support the implementation and use of pattern- and planning-based trajectory prediction algorithms. The evaluation is based on scenarios from the addressed problem area of manufacturing.
Post date: 25 October 2022
Hypergraph-based analysis and design of intelligent collaborative manufacturing space
Hypergraph-based analysis and design of intelligent collaborative manufacturing space
A method for hypergraph-based analysis and the design of manufacturing systems has been developed. The reason for its development is the need to integrate the human workforce into Industry 4.0 solutions. The proposed intelligent collaborative manufacturing space enhances collaboration between the operators as well as provides them with valuable information about their performance and the state of the production system. The design of these Operator 4.0 solutions requires a problem-specific description of manufacturing systems, the skills, and states of the operators, as well as of the sensors placed in the intelligent space for the simultaneous monitoring of the cooperative work. The design of this intelligent collaborative manufacturing space requires the systematic analysis of the critical sets of interacting elements. The proposal is that hypergraphs can efficiently represent these sets, moreover, studying the centrality and modularity of the resultant hypergraphs can support the formation of collaboration and interaction schemes and the formation of manufacturing cells. A fully reproducible illustrative example presents the applicability of this concept.
Post date: 06 September 2022
Heart Rate Variability Measurement to Assess Work-Related Stress of Physical Workers in Manufacturing Industries - Protocol for a Systematic Literature Review
Heart Rate Variability Measurement to Assess Work-Related Stress of Physical Workers in Manufacturing Industries - Protocol for a Systematic Literature Review
Although Industry 4.0 automation has replaced the human workforce by machines in industries, physical workers are indispensable in many facilities. One primary source of stress in the manufacturing workplace is inappropriate work content (e.g., unreasonable workload and work pace). With workers facing physical activities or machine operations, work-related stress can affect their performance and cause productivity, quality, and safety problems. Thus, industrial managers constantly seek ways to ease stress among their workforce, as Healthy Operator is proposed as an essential pillar of the Operator 4.0 concept. As the first step of stress reduction is to identify the stress (if possible, its early signs), heart rate variability (HRV) is widely measured, with different methods and technologies adopted to assess the stress levels of workers. Recent technological advancements have developed many non-invasive measurement techniques and systems, offering more convenience and flexibility than traditional clinical methods. Since human centricity is a strategic focus of the forthcoming Industry 5.0 initiative proposed by the European Union, these measurement practices should be disseminated and shared to foster better stress identification and reduction. A systematic literature review is needed to deliver a comprehensive update in the field. Besides synthesizing the relevant development, the review study aims to motivate industrial managers to adopt a similar approach and provide helpful guidance on what to expect with the Heart Rate Variability measurement for the workplace stress assessment. A detailed protocol for the systematic literature review is given.
Post date: 10July 2022
Architecture of a Human-Digital Twin as Common Interface for Operator 4.0 Applications
Architecture of a Human-Digital Twin as Common Interface for Operator 4.0 Applications
At collaborative workspaces, humans and robots share the shop floor and work closely together. Operator 4.0 is a wide research topic and its solutions aim at the creation of Human-centered Cyber-Physical Systems that improve operators’ capabilities. Such applications require a bi-directional flow of information and need data, models and simulations of machines as well as humans. To realize a common interface for information, the concept of Digital Twin is promising. This paper therefore discusses the adaption of conventional Digital Twin architectures and presents a derived Human-centered Digital Twin (H-DT) architecture designed for operators in production and intralogistics.
Post date: 26 November 2021
Trajectory Prediction of Humans in Factories and Warehouses with Real-Time Locating Systems
Trajectory Prediction of Humans in Factories and Warehouses with Real-Time Locating Systems
Flexible intralogistics systems use automated guided vehicles (AGV) to transport goods. In assembly and warehouses, AGVs and human workers often work side by side. For optimal navigation, AGVs must consider human movement and estimate future positions of workers. Using real-time locating systems (RTLS) to improve human-robot collaboration enables more energy-efficient and safer AGV wayfinding strategies. This paper gives a summary on the topics RTLS, AGV wayfinding and trajectory prediction and introduces the momentum-based approach to predicting future worker positions in factories and warehouses. The results show that ultra-wideband-based RTLS are very well suited for trajectory prediction in the production sector.
Post date: 26 November 2021
Worker movement diagram based stochastic model of open paced conveyors
Worker movement diagram based stochastic model of open paced conveyors
Human resources are still utilized in many manufacturing systems, so the development of these processes should also focus on the performance of the operators. The optimization of production systems requires accurate and reliable models. Due to the complexity and uncertainty of the human behavior, the modeling of the operators is a challenging task. Our goal is to develop a worker movement diagram based model that considers the stochastic nature of paced open conveyors. The problem is challenging as the simulator has to handle the open nature of the workstations, which means that the operators can work ahead or try to work off their backlog, and due to the increased flexibility of the moving patterns the possible crossings which could lead to the stopping of the conveyor should also be modeled. The risk of such micro-stoppings is calculated by Monte-Carlo simulation. The applicability of the simulator is demonstrated by a well-documented benchmark problem of a wire-harness production process.
Enabling Technologies for Operator 4.0: A Survey
Enabling Technologies for Operator 4.0: A Survey
The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The resultant Human-Cyber-Physical Systems (H-CPS) integrate the operators into flexible and multi-purpose manufacturing processes. The primary enabling factor of the resultant Operator 4.0 paradigm is the integration of advanced sensor and actuator technologies and communications solutions. This work provides an extensive overview of these technologies and highlights that the design of future workplaces should be based on the concept of intelligent space.
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