Operator 4.0 solutions

Industry 5.0 laboratory

Developed Human-centered solutions

Virtual Operator*

We are using the HP Reverb VR3000 G2 VR headset to improve operator efficiency. The VR technology help to optimize the decision-making process for smarter operators. Thanks to that technology, a combination of interactive virtual reality and advanced simulations of realistic scenarios is provided.

Healthy Operator*

Polar OH 1 wearable sensor is used to get precise heart rate from the operator during production. Our research focuses on integrating this monitored data (physical workload - exercise activity and cognitive workload - mental effort during the work-shift) data with indoor positioning information. The health-related metrics can be used to plan and schedule the work shifts, rest breaks, and operator overtime.

To further maintain the healthy status of workers, digital biomarkers are used to detect their strengths and mental limitation, thus the work content (workload, work pace, etc.) can be adjusted accordingly. In our approach, the Polar OH1 wearable sensor is deployed to monitor the heart rate of the operator during his production shift. By analyzing the Heart Rate Variability, his physiological changes can be under close observation. The aim is to design the physical workload (production activity) and cognitive workload (mental effort) to personally suit the ability of the worker.

In combination with data from the indoor positioning system, customized service can be brought to the place where the worker is in a timely manner. Better allocation of production resources can also be made, that fits to his personal work plan (such as work shifts, rest breaks, overtime). Our latest paper in this topic: Heart Rate Variability Measurement to Assess Work-Related Stress of Physical Workers in Manufacturing Industries - Protocol for a Systematic Literature Review

Collaborative Operator*

CoBots (Collaborative Robots) relives tedious, non-ergonomic and vulnerable tasks from operators. We applied a UR3 Collaborative Robot arm to support the operator during the assembly process. Based on the Intelligent Space, we could predict better co-working interaction to improve the smart operator productivity. Our latest paper in this topic: Workspace design for human-machine collaboration monitoring

Analytical Operator*

A Digital Twin is developed based on the Siemens Tecnomatix Plant Simulation platform to discover helpful information and predict relevant events for continuous improvement and provide greater visibility of KPIs and real-time alerts based on predictive analytics. The Real-time Locating System is used to get location and other sensor-based information in real-time based on the Intelligent Space concept. Our lates paper in this topic: Integration of real-time locating systems into digital twins

Digital model of the Operator

We aim to create models about the operators on the shop floor, using semantic technologies such as ontologies or knowledge graphs. We also utilise the operator activity or skill-related industrial standards and description methods such as ISA-95, B2MML, or AutomationML. A model that accurately describes the operator can facilitate the design of human-machine collaboration and a more ergonomic or safe work environment.

The development trend of system integration in the manufacturing industry is to achieve standardization. ISA-95 is one of the essential standards in enterprise-control system integration and serves as a highly utilised basis for design Industry 4.0. The Personnel model contains information about the Class type of person in the enterprise, such as a production manager or operator; the Property as seniority, position, or division; and Qualification such as a particular task or position of the Personnel. Our latest paper in this topic: Human-centered knowledge graph-based design concept for collaborative manufacturing

Operator comfort level

The operator's health and comfort level have a significant impact on his performance. Our goal is to determine how environmentally stressful the various sections of the shop floor are for the operator. We employ a mobile measuring device that delivers location data for environmental factors to do this. Our lates paper in this topic: Estimation of the operator comfort level and the layout information based on sensor fusion techniques

Cognitive operator*

Our research focuses on cognitive operators—individuals tasked with making decisions in demanding environments. We're exploring how Galvanic Skin Response (GSR) and Heart Rate Variability (HRV) signals can be used to monitor cognitive load, an individual's mental processing capacity at any given moment. By employing the Practiwork system, we assess manual dexterity and hand-foot coordination under various difficulty levels, intentionally inducing cognitive load to gain deeper insights into its influence on operator performance.

Maintenance Operator 4.0

Maintenance Operator 4.0 revolutionizes the maintenance landscape by addressing the historical lack of data insights for operators, hindering technological progress. Industry 4.0 and IoT technologies usher in a new era of high-level maintenance analytics. To enhance maintenance processes, operators must elevate their physical, sensing, and cognitive capabilities.

Virtual Reality (VR), Augmented Reality (AR), and head-mounted devices play a pivotal role in facilitating learning and decision-making for operators in the realm of novel maintenance processes. Smart devices leverage IoT to reduce uncertainties by providing real-time insights into machinery performance, enabling proactive maintenance approaches. The synergy between operators and Cyber-Physical Systems (CPS) fosters seamless collaboration and information exchange. The physical workplace requires a strategic redesign to accommodate human-machine symbiosis, creating an environment where operators and intelligent technologies collaborate seamlessly. In essence, Maintenance Operator 4.0 envisions a future where technology empowers operators, facilitates informed decision-making, and transforms maintenance into proactive, data-driven endeavors. Maintenance Operator 4.0 is properly equipped to assess and prioritize maintenance needs based on potential risks. Hence, it aligns seamlessly with the principles of risk-based maintenance.

Video-based monitoring solutions

Based on open-source packages with Python language and Computer Vision library and tools, we developed video-based monitoring solutions, which can be applied in industrial work environments. To ensure compliance with GDPR, only the movements and actions of the operator of interest, within a predefined area, are recorded anonymously. One great example is the combination of transferred learning with Google MediaPipe for skeleton detection and Optical Flow tools in OpenCV, to generate a monochrome picture of the recognized skeleton with the lines indicate the movement intensity of the body parts. Similarly, Yolo model can be used to estimate which objects the monitored operator needs to pick up, and how much physical load affects the hand, based on the object's weight and the duration of holding it.

The Operator 4.0 solutions are based on Prof. Dr David Romero et al., who first defined the Operator 4.0 paradigm. Their project is "The Operator 4.0 - Towards Socially Sustainable Factories of the Future", which is the basics of all of the further researches and applications besides this topic.

* Romero, David, et al. "Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies." proceedings of the international conference on computers and industrial engineering (CIE46), Tianjin, China. 2016. - https://bit.ly/3pOi3gm

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