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.

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.

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.

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.

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.

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.

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.

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. 

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. 

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. 

Industrial development is evolving towards an increased focus on human-machine collaboration, exemplified by the emergence of Industry 5.0. This new industrial paradigm prioritizes solutions centered around humans and emphasizes resilience and sustainability. A critical aspect of this collaboration is the need for robots to possess more advanced cognitive abilities, allowing for safe co-working environments with humans. Advancements in technology have enabled various methods for monitoring human behavior. Analyzing these behavioral patterns enhances efficiency and collaboration. 

My thesis introduces a setting where monitoring the operator and the robot is possible. It is achieved through a camera, an indoor positioning system, and, in the future, wearable sensors. Indicators, for example, the utilization of participants or waiting times, can represent the collaboration.

Measurements were taken with a specific focus, and the results were analyzed comprehensively—the thesis aimed to establish a connection between human activities and collaborative robots while considering human needs. The process used to achieve this objective is demonstrated through two complex games, and the design of experiments, which were then used to create and do more comprehensive experiments, is also presented.

To plan production and estimate the costs of the production process, manufacturing companies use so-called process or base activity times. These activity times are often estimated or based on field measurements and are dependent on the subjectivity of the engineer who estimates the time effort of the processes. The main subject of the thesis is the development of a data collection and processing system that fits into the Operator 4.0 concept and can be adapted to a brownfield Industry 4.0 manufacturing systems.

Due to the stochastic nature of the problem - different workplace layouts, a wide spectrum of production processes, different work environments, different work habits, additional stress due to measurement and data collection - the objectives include the creation of a generally applicable flexible system.

After reviewing the applicable technologies with a view to making them as adaptable as possible, I concluded that visual information gathering is the easiest way to monitor existing production systems, because it allows both workers and machines to be observed and evaluated.

Optical flow-based displacement detection, clustering of displaced points, tracking of displaced objects with object tracking algorithms and machine learning-based technologies are the most suitable for modelling workers activity and for qualitative and quantitative performance evaluation.

In this study, the analysis of the manufacturing of a product in a one-operator and a two-operator work model at the investigated workplace is presented. By processing the data, the activity performed by the workers and their performance can be measured using indicators assigned to job roles. In addition to human performance, machine utilisation, number of pieces produced and cycle times can also be measured using the framework.

The advent of collaborative robots has made it technically feasible for humans and machines to work together in a shared workspace. A fundamental spirit of the fifth industrial revolution is to develop human-centred solutions and processes. This is necessary because full automation has its limits, both economically and technologically.

The aim of my research is to develop a discrete event simulation-based toolkit to efficiently and purposefully investigate the efficiency and feasibility of these collaborative processes. In the developed simulation model, I use key indicators to investigate collaborative and decoupled manufacturing between humans and machines. The simulation will make it possible to investigate more cost-effective improvements without interrupting real production. My work will show how to reduce the resource requirements of manufacturing and maximise the utilisation of robotic arms and workers. For a specific automotive supplier, I will prepare a case study for a specific workflow, where I will evaluate the workload of a given collaborative robot and operators.

Industrial development is increasingly focusing on human-machine collaboration. As Industry 5.0 is being introduced, more human-centered solutions are presented. The key elements of Industry 5.0 are resilience and sustainability, and it is also more human-centric, concentrating on collaboration between people and machines. Collaborative work requires that the robots have more complex cognitive abilities. These abilities allow humans and robots to work together safely near each other.

However, safety is only one part of human-robot collaboration. Scheduling the tasks of the operator and robot creates an environment where collaboration is safely achievable. Sharing activities and defining and scheduling these are complex tasks requiring monitoring and recognizing the operator’s actions. Thanks to technological advancements, several methods exist to monitor human behavior, such as cameras, indoor positioning systems, and wearable sensors. The patterns can be analyzed, and the result can be used the enhance efficiency and collaboration. 

This work presents a workspace where monitoring the operator and robot is achievable using a camera, an indoor positioning system, and wearable sensors. Targeted measurements were made, and the results were analyzed. The developed methodology and optimization tools are presented in a complex demonstration game, and finally, will be detailed on how they can be applied in a real industrial environment.

The human-machine collaboration is coming to the forefront of industrial development. As the concept of Industry 5.0 is being introduced, more human-centered solutions are proposed, as opposed to the more machine-centric solutions in Industry 4.0. The main focus of Industry 5.0 is flexibility, sustainability, and the mentioned human-machine col- laboration. To create a more human-centric workplace, the workers’ behavior should be monitored. Technological advances have allowed new methods to monitor human behavior in the past years. For example, people’s motions can be tracked using the help of camera systems or indoor positioning systems. The patterns can be analyzed, and the result can be used the improve efficiency and collaboration.

However, this has several shortcomings. The goal is to prepare the collaborative robot based on human activities so that they serve people.

This work presents a workspace where the operator and robot can be observed using a camera and an indoor positioning system during their collaboration. The developed monitoring space and tools are presented in a complex demonstration game, and finally, it will be detailed on how they can be applied in a real industrial environment.