Despite advancements in automating observation-based ergonomics assessments, most automations still rely on a single underlying construct (e.g. posture). This fragmented approach, however, derogates from the integrated nature of traditional assessment methods. The few studies that consider multiple aspects do so through indirect or manual procedures. This paper studies a novel approach of automating ergonomics assessment, making use of the well-established European Assembly Worksheet (EAWS), incorporating both posture and load manipulation. To this end, it comparatively examines observation-based and automated calculations, using paper-based documents for the former, and inertial measurement units and smart glasses for the latter. The comparative results show a strong, significant correlation between automated and observation-based EAWS calculations (r = 0.74; p < 0.05 for an assembly task, r = 0.82; p < 0.01 for a palletising task). Additional subjective survey results deviate from the EAWS calculations, but are complementary in identifying effects not detected by it.

Extended Reality (XR) and Artificial Intelligence (AI) are increasingly converging within cyber–physical infrastructures, including digital twins, the Spatial Web, and smart-city systems. These environments require new frameworks for understanding how human performance emerges through sustained interaction with immersive interfaces and adaptive computational agents. This paper introduces the TAXI–XI-CAP framework, a two-layer model that links psychobiological mechanisms of XR–AI interaction to higher-level, experimentally testable capability constructs. The TAXI layer defines 42 mechanisms spanning perception, cognition, physiology, sensorimotor control, and social coordination, while XI-CAP organizes these into capability patterns such as remote dexterity, distributed cognition, and adaptive workload regulation. Derived through a theory-guided synthesis across XR, neuroscience, and human–automation interaction, the framework models performance as emerging from interacting mechanisms under real-world constraints. A validation-oriented research agenda is proposed, emphasizing mechanism-level measurement, capability-level evaluation, and longitudinal testing. The TAXI–XI-CAP framework provides a structured basis for hypothesis generation, comparative analysis, and empirical validation of XR–AI systems, supporting the development of reliable, scalable, and human-centered Extended Intelligence infrastructures.

Implementing efficient warning systems (e.g., visual, audio, or vibration alerts) is critical in human-robot collaboration (HRC) to enhance safety. Augmented reality (AR) is a promising technology for delivering such warnings. However, several studies have yet to fully investigate the effects of AR-based warning systems on safety and trust HRC. In this work, we investigate the effects of an AR-based warning system on both safety and trust using objective and subjective measures as well as physiological response as stress indicators. Twenty-six people participated in our within-participant experiment. Our results suggest that while the AR-based warning system improves the objective measure of safety, participants did not explicitly feel that their environment was safer with the system. Nevertheless, they demonstrated a higher level of trust in the AR system. However, this positive effect on trust was not reflected in any measure of physiological response, such as heart rate variability or electrodermal activity. Further studies should explore the long-term effects of AR-based warning systems on both safety and stress reduction.

The growing use of Unmanned Aerial Vehicles (UAVs) in agriculture has made data collection more efficient and cost-effective, enabling the development of advanced solutions to enhance agricultural productivity. In this work, we present a dataset of intercropped durian (durio zibethinus) and papaya (carica papaya) trees, extracted from aerial images captured by a UAV. A total of 311 UAV-captured images were collected over a mixed plantation, where durian trees, being taller and broader, contrast visually with the shorter and more slender papaya trees. The trees were subsequently labeled into two categories based on type and visual characteristics: durian trees (3327 images) and papaya trees (2872 images). This dataset serves as a valuable resource for multiple tree species classification, spatial pattern analysis, and decision-making in precision agriculture using machine learning and deep learning methods. Insights derived from this dataset can support improved orchard management, including crop inventory, health monitoring, and optimization of intercropping strategies.

Accurate assessment of human movement and behavior is essential in fields such as ergonomics, rehabilitation, and human-robot interaction. This paper presents PhysioSense, an open-source framework for synchronized multi-modal data acquisition and management. Built on the Lab Streaming Layer (LSL), PhysioSense integrates heterogeneous data streams from kinematic, dynamic, and physiological sensors in real time, ensuring millisecond-level synchronization. Unlike general-purpose tools such as LabVIEW, OpenSignals, or ROS, PhysioSense is specifically tailored to human-centric research, offering a streamlined interface for sensor configuration, recording, visualization, and data export. The framework’s modular design supports extensibility and reproducibility, making it suitable for a range of experimental setups. Two case studies—an ergonomics analysis and a drilling task assessment—demonstrate the framework’s capabilities in real-world scenarios. PhysioSense addresses key challenges in multi-sensor integration and paves the way for more accessible and scalable movement analysis in both research and applied settings.

This paper focuses on improving the ergonomics of industrial workers. It addresses the critical implications of poor ergonomics, which can lead to musculoskeletal disorders over time. A novel methodology for a path-planning algorithm designed for human–robot collaboration was introduced to tackle this challenge. The algorithm’s essential contribution lies in determining the most ergonomic path for a robot to guide a human’s hand during task execution, facilitating a transition toward an optimized body configuration. The algorithm effectively charts the ergonomic path by adopting a Cartesian path-planning approach and employing the cell decomposition method. The methodology was implemented on a dataset of ten individuals, representing a diverse group of male and female subjects aged between 20 and 35, with one participant being left-handed. The algorithm was applied to three different activities: “stacking an item,” “taking an object from a shelf,” and “assembling an object by sitting over a table.” The results demonstrated a significant improvement in the REBA score (as a measure of ergonomics condition) of the individuals after applying the algorithm. This outcome reinforces the efficacy of the methodology in enhancing the ergonomics of industrial workers. Furthermore, the study compared the performance of A* with three heuristic functions against Dijkstra’s algorithm, aiming to identify the most effective approach for achieving optimal ergonomic paths in human–robot collaboration. The findings revealed that A* with a specific heuristic function surpassed Dijkstra’s algorithm, underscoring its superiority in this context. The findings highlight the potential for optimizing human–robot collaboration and offer practical implications for designing more efficient industrial work environments.

Robot-assisted physical rehabilitation holds the potential to increase the efficiency and accessibility of therapy by assisting therapists in delivering consistent training over extended periods and reducing the workload on human therapists. In this work, we propose a system architecture that integrates social interaction into existing robot-assisted physical rehabilitation frameworks with the potential to enhance user comprehension and engagement. The theoretical basis for designing the proposed architecture is based on previous design recommendations for single-robot systems and expanded to multi-robot systems. We provide an example of how to implement the system architecture with root systems where investigate different techniques to generate dynamic speech and gestures for the socially assistive robot, which helps reduce monotony in the user experience. Furthermore, a pre-participation evaluation identified Interest-Enjoyment as the most effective motivational dimension that Pepper could potentially enhance compared to other dimensions. The architectural proposal is expected to serve as a foundation for further implementation and development of socially interactive rehabilitation systems to create a more interactive therapeutic experience that could contribute to current rehabilitation practices.

In the evolving field of industrial automation, operator awareness of robot actions and intentions is critical for safety and efficiency, especially when working in close proximity to robots. From the robot-to-human communication angle, a collaborative robot (cobot) is expected to express its internal states and monitor task progress. Various traditional communication modalities (e.g., tower light, external screen, LED ring, and sound) often fall short of conveying nuanced information, while a flexible display curved around the cobot arm using organic light-emitting diode (OLED) technology provides a potential advantage. Integrated seamlessly with the robot, this interface enhances interaction by displaying text and video, enriching communication, and positively influencing the human–robot collaboration experience. In this work, we investigate a novel integrated flexible OLED display technology used as a robotic skin-interface to improve robot-to-human communication in a real industrial setting at Volkswagen (VW), following a user-centric Double-Diamond co-design process. We first conducted a co-design workshop with six operator representatives to collect their ideas and expectations on how the robot should communicate with them. The gathered information was used to design an interface for a collaborative human-robot interaction task in motor assembly. The interface was implemented in a workcell and validated qualitatively with a small group of operators (n = 9) and quantitatively with a large group (n = 42). The validation results showed that using flexible OLED technology could improve the operators’ attitude toward the robot, increase their intention to use the robot, enhance perceived enjoyment, social influence, and trust, and reduce their anxiety.

Considering social-technical factors during the design and implementation of collaborative robots (cobots) is important to ensure their successful integration into industrial workspaces and the well-being of the operators such as ergonomics. In this work, we present a co-creation study in developing an interaction interface for a human–robot collaboration (HRC) system involving SME factory employees. Employing a qualitative research method, the co-creation activities in this study sought employees’ input on preferred use cases and collaboration methods with robots. The gathered qualitative data was used to design the HRC interaction interface, aligning it with operators’ needs and preferences. The developed system was fully functional, underwent technical validation, and received feedback from the factory operators. Our study emphasizes the importance of involving employees in the design of HRC interaction interfaces, which can result in HRC systems that meet their needs and preferences. Such customized systems have the potential to enhance the acceptance of robots in industrial settings. The study contributes to the field by demonstrating a participatory approach to designing an HRC interaction interface for a robot in a real industrial setting, where no use cases or interaction modalities were pre-defined.

Robot-to-human communication is important for mutual understanding during human-robot collaboration. Most of the current collaborative robots (cobots) are designed with low levels of anthropomorphism. Therefore, the ability of cobots to express human-like communication is limited. In this work, we present an open-source platform named Antropo to increase the level of anthropomorphism of Franka Emika-a widely used collaborative robot arm. The Antropo platform includes three modules: a camera module for expressing eye gaze, a light module for visual feedback, and a sound module for acoustic feedback. These modules can be rapidly prototyped through 3D printers, laser-cutters, and off-the-shelf components available at a low cost. The Antropo platform can be easily installed on the Franka Emika robot. The added communication channels can be synchronised with the robot's motions to enhance mutual understanding. All hardware CAD design files and software files are released. The platform can be used to study human-like behaviours of cobots and the effects of these behaviours on different aspects of human-robot collaboration. We demonstrate the Antropo platform in an assembly task in which the Franka Emika robot expresses various human-like communicative behaviours via the added communication channels. We also present two industrial applications in which the Antropo platform was customised for the Universal Robots UR16e.

The factory of the future is steering away from conventional assembly line production with sequential conveyor technology, towards flexible assembly lines, where products dynamically move between work-cells. Flexible assembly lines are significantly more complex to plan compared to sequential lines. Therefore there is an increased need for autonomously generating flexible robot-centered assembly plans. The novel Autonomous Constraint Generation (ACG) method presented here will generate a dynamic assembly plan starting from an initial assembly sequence, which is easier to program. Using a physics simulator, variations of the work-cell configurations from the initial sequence are evaluated and assembly constraints are autonomously deduced. Based on that the method can generate a complete assembly graph that is specific to the robot and work-cell in which it was initially programmed, taking into account both part and robot collisions. A major advantage is that it scales only linearly with the number of parts in the assembly. The method is compared to previous research by applying it to the Cranfield Benchmark problem. Results show a 93% reduction in planning time compared to using Reinforcement Learning Search. Furthermore, it is more accurate compared to generating the assembly graph from human interaction. Finally, applying the method to a real life industrial use case proves that a valid assembly graph is generated within reasonable time for industry.

Musculoskeletal disorders caused by poor work posture are a serious concern in the industry since they lead to absenteeism and medical leave from work. In the context of human–robot collaboration, this issue can be mitigated if collaborative robots support human workers to perform their tasks more ergonomically. In this work, we propose a method to optimize human posture during human–robot collaboration using the Particle Swarm Optimization (PSO) algorithm. Our approach involves assigning an appropriate location to the robot’s end-effector to minimize the distance between the optimized posture of the human and their current posture in the working space. To measure human posture, we use the Rapid Entire Body Assessment score (REBA) calculated from body joint angles captured by a Kinect camera. To validate the effectiveness of our proposed method, we conducted a user study with 20 participants in a virtual reality environment. The PSO algorithm could position the robot end-effector to the optimal position close to real time. Our results showed that our method could improve ergonomics by 66%, indicating its potential for use in human–robot collaborative applications.

Expressing social cues through different communication channels plays an important role in mutual understanding, in both human-human and human-robot collaborations. A few studies investigated the effects of zoomorphic and anthropomorphic social cues expressed by industrial robot arms on robot-to-human communication. In this work, we investigate the role of multi-modal social cues by combining the robot's head-like gestures with light and sound modalities in two studies. The first study found that multi-modal social cues have positive effects on people's perception of the robot, perceived enjoyment, and intention to use. The second study found that a combination of human-like gestures with light and/or sound modalities could lead to a higher understandability of the robot's social cues. These findings suggest the use of multi-modal social cues for robots in industrial settings. However, the possible negative impacts when implementing these social cues should be considered e.g. overtrust, and distraction.

Mental fatigue (MF) is likely to occur in the industrial working population. However, the link between MF and industrial work performance has not been investigated, nor how this interacts with a passive lower back exoskeleton used during industrial work. Therefore, to elucidate its potential effect(s), this study investigated the accuracy of work performance and movement duration through a dual task paradigm and compared results between mentally fatigued volunteers and controls, with and without the exoskeleton. No main effects of MF and the exoskeleton were found. However, when mentally fatigued and wearing the exoskeleton, movement duration significantly increased compared to the baseline condition (βMF:Exo = 0.17, p = .02, ω2 = .03), suggesting an important interaction between the exoskeleton and one's psychobiological state. Importantly, presented data indicate a negative effect on production efficiency through increased performance time. Further research into the cognitive aspects of industrial work performance and human-exoskeleton interaction is therefore warranted.

Work-related musculoskeletal disorders (MSD) are one of the major cause of injuries and absenteeism at work. These lead to important cost in the manufacturing industry. Human-robot collaboration can help decreasing this issue by appropriately distributing the tasks and decreasing the workload of the factory worker. This paper proposes a novel generic task allocation approach based on hierarchical finite-state machines for human-robot assembly tasks. The developed framework decomposes first the main task into sub-tasks modelled as state machines. Based on capabilities considerations, workload, and performance estimations, the task allocator assigns the sub-task to human or robot agent. The algorithm was validated on the assembly of a crusher unit of a smoothie machine using the collaborative Franka Emika Panda robot and showed promising results in terms of productivity thanks to task parallelization, with improvement of more than 30% of the total assembly time with respect to a collaborative scenario, where the agents perform the tasks sequentially.

Human-robot collaboration is becoming increasingly popular in the manufacturing industry, opening the door to a large range of applications by combining the complementary skills of the human worker and the robot. Collaborative robots are also a solution to decrease the operator workload and indirectly reduce the risk of occupational injuries such as musculoskeletal disorders (MSDs). The latter represents one of the major causes of absenteeism at work. Thanks to the development of human tracking devices, it is possible to monitor the operator, analyze the postures, and assess the associated MSD risk. In this paper, we present a novel ergonomics optimization framework that performs postural optimization based on the virtual element method. A feedback interface is developed whereby the user is informed about non-ergonomic postures and an improved body pose is proposed. The workpiece position controller module acts on the cobot end-effector and indirectly on the co-manipulated part in such a way that the operator’s posture is improved. The framework was validated by a user study performed on a human-robot collaboration task whereby the subject polishes a part hold by the robot. The conducted study of the user’s perception and REBA scores showed promising results. Note to Practitioners —This paper is motivated by the problem of non-ergonomic posture of workers in hybrid workcells. The proposed approach makes use of virtual elements (springs and dampers) to build a mechanical model of the human body posture and perform postural optimization. The obtained body joint angles are fed into two modules of the framework. First, a graphical interface displays the current pose of the user and proposes to him the improved posture. Second, a controller adapts the pose of the workpiece hold by the collaborative robot. This is realized by computing a displacement vector between the wrist current and optimized positions. The use of such a framework was demonstrated on a collaborative polishing task whereby the robot adjusts the position of the workpiece. After a user study test with 10 participants, joint data were collected and the REBA scores of different subtasks were measured and compared. The results from these preliminary experiments showed that the proposed approach improves the human body postures and offers a promising solution to enhance ergonomics by the robot assistance in case of robotic workcells. The conducted survey also shows an overall positive subject’s perception of the system.

Objective Over the course of the twenty-first century, work-related musculoskeletal disorders are still persisting among blue collar workers. At present, no epidemiological overview exists. Therefore, a systematic review and meta-analysis was performed on the epidemiology of work-related musculoskeletal disorders (WMSD) within Europe’s secondary industries. Methods Five databases were screened, yielding 34 studies for the qualitative analysis and 17 for the quantitative analysis. Twelve subgroups of WMSDs were obtained for the meta-analysis by means of predefined inclusion criteria: back (overall), upper back, lower back, neck, shoulder, neck/shoulder, elbow, wrist/hand, leg (overall), hip, knee, and ankle/feet. Results The most prevalent WMSDs were located at the back (overall), shoulder/neck, neck, shoulder, lower back and wrist WMSDs with mean 12-month prevalence values of 60, 54, 51, 50, 47, and 42%, respectively. The food industry was in the majority of subgroups the most prominent researched sector and was frequently associated with high prevalence values of WMSDs. Incidence ratios of upper limb WMSDs ranged between 0.04 and 0.26. Incidence ratios could not be calculated for other anatomical regions due to the lack of sufficient articles. Conclusion WMSDs are still highly present among blue collar workers. Relatively high prevalence values and low incidence ratios indicate a limited onset of WMSDs with however long-term complaints.

The present study tries to decrease the risk of work-related musculoskeletal disorders for industry workers by proposing a generic algorithm that recommends an optimal ergonomic posture for accomplishing tasks in an industrial environment. In the case of a dangerous ergonomic pose, the optimization algorithm starts by heuristically changing it to a more ergonomic one. Each recommended posture's feasibility is tested with an inverse kinematic method that can predict the worker's behavior for accomplishing a task. This iterative optimization procedure continues until the optimal ergonomic pose for the worker is achieved. The algorithm's validity is tested in thirteen cases, people with different gender (50 percent male, 50 percent female) aged between 20 and 35, and different height and body morphologies. According to studies, there is a connection between musculoskeletal disorders and the wrong posture for accomplishing tasks in industries. We suggest an optimization algorithm that can indicate the worker the optimal ergonomic pose by considering task constraints in real-time.

Objective Over the course of the twenty-first century, work-related musculoskeletal disorders are still persisting among blue collar workers. At present, no epidemiological overview exists. Therefore, a systematic review and meta-analysis was performed on the epidemiology of work-related musculoskeletal disorders (WMSD) within Europe’s secondary industries. Methods Five databases were screened, yielding 34 studies for the qualitative analysis and 17 for the quantitative analysis. Twelve subgroups of WMSDs were obtained for the meta-analysis by means of predefined inclusion criteria: back (overall), upper back, lower back, neck, shoulder, neck/shoulder, elbow, wrist/hand, leg (overall), hip, knee, and ankle/feet. Results The most prevalent WMSDs were located at the back (overall), shoulder/neck, neck, shoulder, lower back and wrist WMSDs with mean 12-month prevalence values of 60, 54, 51, 50, 47, and 42%, respectively. The food industry was in the majority of subgroups the most prominent researched sector and was frequently associated with high prevalence values of WMSDs. Incidence ratios of upper limb WMSDs ranged between 0.04 and 0.26. Incidence ratios could not be calculated for other anatomical regions due to the lack of sufficient articles. Conclusion WMSDs are still highly present among blue collar workers. Relatively high prevalence values and low incidence ratios indicate a limited onset of WMSDs with however long-term complaints.

—Robotic manipulators that can coexist with hu- mans need formal safety guarantees. Current solutions cannot handle both input and state constraints, reduce the robot capabilities, or are computationally too expensive. To tackle these drawbacks, we analyzed the Trajectory-Based Explicit Reference Governor, which can address both input and state constraints, and does not require any online optimization. We present the methodology for a generic robot arm and show results for the Franka Panda manipulator. The proposed control scheme is able to steer the robot arm to the desired end-effector position, or an admissible approximation, in the presence of limited joint ranges, actuator saturations, and static obstacles.