Högskolan i Skövde

Digital human modelling (DHM) tools enable engineers to assess ergonomic conditions and predict risk factors before workstation designs are finalized, supporting proactive approaches to workplace safety. Despite these advantages, DHM tools remain underutilized in industry compared with computer aided design (CAD) and other digital engineering tools. Key documented barriers of the adoption of DHM tools include steep learning curves, requirements for specialized ergonomics expertise, time-consuming manual setup of simulations, and limited documentation resources. Meanwhile, advances in artificial intelligence (AI), particularly large language models (LLMs), offer new possibilities for supporting engineering workflows through natural-language interaction and automated analysis. However, structured approaches for integrating AI into DHM tools are largely absent. This paper proposes a framework for integrating AI capabilities into DHM tools and presents a prototype implementation to assess feasibility. The framework defines four functional roles: Dialog (natural-language interaction), Expert (documentation-grounded guidance), Analyser (ergonomics data interpretation), and Executor (command translation to simulation operations). The prototype, connected to a commercial DHM tool, demonstrates how AI-based support can guide users through modelling steps, automate repetitive tasks, and identify critical ergonomic indicators, illustrating how such support could potentially lower the threshold for non-expert users, reduce manual effort, and contribute to more systematic design of workstations that support worker well-being. The current work focuses on the conceptual framework and technical feasibility; formal user validation remains as future work.

This study explores and demonstrates how driver posture prediction simulation can be combined with direct vision assessment in order to evaluate direct vision for drivers with diverse body size. A DHM tool is used to iteratively test seat and steering wheel configurations to find a comfortable posture for 58 different manikins, and then perform vision analysis of the external environment around the vehicle. The presented study should not be used as a basis for any vehicle design but should be seen as a demonstration of a proposed simulation process. The study indicates that when different simulation and assessment methods are combined a more comprehensive analysis of the driver ergonomics can be achieved during the vehicle design process. Even though the suggested approach is time consuming and will test a lot of non-acceptable configurations, it can give answers to what would happen if the size and position of the adjustment ranges would change.

Digital human modelling (DHM) tools are mainly used to perform proactive assessments of aspects related to physical ergonomics. It is less common to use DHM simulations to determine standard times for work tasks. An established way in industry to proactively determine task times is to use predetermined motion time systems (PMTS), which can, for certain descriptions of the work task, output a specific standard time. This standard time is meant to represent the time needed by an experienced average worker to perform the work task. This paper studies and suggests speed profile functionality that enables times of manual reach tasks simulated in a DHM tool to correspond to PMTS times in Methods-Time Measurement (MTM-1).

Human–robot collaboration solutions have gradually become popular, in which some tasks are performed by robots and others by humans. Designing such a production cell requires simultaneous consideration of human-centered factors, machine-focused mechanical design, and system engineering in the early planning stages. However, different objectives often conflict (e.g., speeding up a robot can improve productivity while compromising energy efficiency), and the same variables can affect multiple models and simulations simultaneously (e.g., a machine where humans and robots collaborate can influence both the operator’s working posture and the robot’s cycle time). Therefore, multidisciplinary tools and multi-level optimization are needed to model, simulate, and optimize elements such as production flows, robotics, and human operators to balance objectives related to cycle time, energy consumption, and worker well-being. In this paper, we formulate an approach that integrates different simulation tools and a bi-level optimization framework to balance worker well-being, cycle time, and energy consumption. We demonstrate this approach through a real industrial case of designing a work cell for elevator pipe assembly in a grain conveying system, where ABB RobotStudio is used for robotic simulation and IPS IMMA for human simulation. IBM ILOG CPLEX Optimization Studio is employed for the top-level task allocation optimization, and a set of results is presented based on data extracted from the lower-level robot-centered optimization. The results show that our approach can effectively balance different objectives by incorporating detailed information from different levels of the work cell design.

Ergonomics evaluation methods are crucial for assessing risks for work-related musculoskeletal disorders and ensuring operator well-being, productivity, and safety. Despite the increased use of digital twins and AI-supported tools in production system design and operation, ergonomics evaluations still primarily rely on observational techniques such as expert assessments or checklist-based tools like the Rapid Entire Body Assessment and the Rapid Upper Limb Assessment. These methods are time-consuming, imprecise, and prone to subjectivity arising from variability in the judgment of the ergonomists and ambiguity in scoring criteria. As an alternative, ergonomics evaluation methods based on using technologies for direct measurements can provide semi-automation of the assessments and offer greater objectivity and precision. This study investigates the capability of a computer vision-based motion capture approach to support direct measurement ergonomics evaluations and compares its results with those of an inertial measurement unit-based system in an industrial task. The comparison was conducted by studying output data of the two systems and by feeding the data into a direct measurement-based ergonomics evaluation method. A representative industrial assembly task involving upper-body movement and dynamic wrist activity was recorded simultaneously using a single monocular RGB camera and an IMU-based system. Both datasets were processed using two parallel workflows that followed the same structure to extract joint angles and segment positions over time. The comparison and evaluation of the results demonstrates that computer vision-based motion capture has the potential to provide human posture and motion data suitable for direct measurement ergonomics evaluations in industrial environments.

The shift towards electric vehicle production has introduced new manufacturing challenges, particularly in tasks that require operators to handle flexible components such as electrical wire harnesses and high-voltage cables. Assembly tasks such as picking, carrying, deforming, and mounting flexible components are usually performed by operators and can result in high force demands, affecting both operator well-being and production efficiency. Ensuring that these work demands do not exceed an operator’s physical capacity is essential for maintaining a sustainable work environment, improving worker well-being, and reducing risks of work-related musculoskeletal disorders. This paper addresses this challenge by simulating and evaluating a real-world use case at Volvo Cars AB, where operators manually install electrical wire harnesses in an automotive assembly station. The study integrates the Arm Force Field method within a DHM tool to compare forces demanded by the assembly task to force capacity of the operators. Additionally, RULA and REBA are used to evaluate postural risks during the assembly. The simulation estimates force demands for picking, carrying, deforming, and mounting the harness. By analysing the ratio between work demand and human capacity, this study provides insights into how DHM tools can assist engineers and ergonomists to proactively assess assembly work of flexible objects, in turn assisting workstation design and supporting sustainable manual assembly conditions.

To ensure a sustainable work life, work demands should not exceed the capacity of members of the workforce. Digital human modelling (DHM) tools can be used to consider ergonomics issues in computer simulated settings, supporting engineers and ergonomists to proactively find design solutions that fulfil well-being related criteria. For this, DHM tools need to be able to assess risks for musculoskeletal disorders (MSDs). One key risk factor for MSDs is force exertions. This load dose on the human body is influenced by aspects such as the magnitude of the force, the direction of the force, the frequency of force exertion, the duration of the force exertion, and the posture held during force exertion. This paper compares force capacities for three different work postures and six force directions given by two methods: the Assembly Specific Force Atlas and the Arm Force Field. The study takes a DHM tool user’s point of view, envisioning the methods being used to assess a design proposal of a work scenario being simulated in a DHM tool. The results show that the two methods, for some conditions, predict quite similar force capacities, while for other conditions there are larger differences. Reasons for these findings are discussed.

Ergonomics evaluation methods are typically used for assessing risks of work-related musculoskeletal disorders (WMSDs) and the physical well-being of workers. Traditionally, these methods rely on assessors observing workers performing tasks and assessing potential risks based on observational ergonomics evaluation methods like the Rapid Entire Body Assessment (REBA) and the Rapid Upper Limb Assessment (RULA). While observational methods provide a structured risk assessment framework, they often depend on subjective evaluations, leading to inconsistent assessments between different ergonomists.

This study examines the application of motion capture technology to enhance the objectivity and efficiency of ergonomics evaluations and to enable the use of direct measurement ergonomics evaluation methods. The study was conducted at a medium-sized enterprise assembly station, where a worker’s tasks were recorded using motion capture technology. The captured motions were input into the Digital Human Modelling (DHM) tool IPS IMMA, and ergonomic assessments were performed using RULA, REBA, and the Arm Force Field (AFF) method.

The study followed a process comprising three main stages: data collection, data processing, and ergonomics evaluation. The recorded data were processed into XML format, imported into IPS IMMA, and exported to the Ergonomics in Production Platform (EPP) for RULA and REBA evaluations and to a script for AFF evaluations. The integration of these methods improved the precision and reliability of ergonomics assessments by replacing subjective estimates with direct measurements. The findings demonstrate the potential of combining motion capture with DHM tools to enhance ergonomics evaluation and support decisionmaking in workstation design and automation.

Occupant packaging design is usually done using computer-aided design (CAD) and digital human modelling (DHM) tools. These tools help engineers and designers explore and identify vehicle cabin configurations that meet accommodation targets. However, studies indicate that current working methods are complicated and iterative, leading to time-consuming design procedures and reduced investigations of the solution space, in turn meaning that successful design solutions may not be discovered. This paper investigates potential advantages and challenges in using an automated simulation-based multi-objective optimization (SBMOO) method combined with a DHM tool to improve the occupant packaging design process. Specifically, the paper studies how SBMOO using a genetic algorithm can address challenges introduced by human anthropometric and postural variability in occupant packaging design. The investigation focuses on a fabricated design scenario involving the spatial location of the seat and steering wheel, as well as seat angle, taking into account ergonomics objectives and constraints for various end-users. The study indicates that the SBMOO-based method can improve effectiveness and aid designers in considering human variability in the occupant packaging design process.

This paper presents the development of statistical 3D body shape prediction models and how these models can be shared to be used by other researchers, software developers or organisations. In Digital human modelling (DHM) tools it is important that the generated manikin models are accurate and representative for different body sizes and shapes. By using both 3D body scan data and one-dimensional data, provided in tabular format, a prediction model was created that based on a few input variables predicts both additional missing one-dimensional data and body shape data, in the form of landmark coordinates in XYZ point cloud format as well as a body shape mesh model in OBJ format. The data is handled in a sequential process where three different prediction functions were defined using similar implementations of a conditional regression model. The developed statistical 3D body shape prediction model described in this paper is based on body scan data from the CAESAR anthropometric survey. The statistical 3D body shape prediction models, consisting of MATLAB scripts have been prepared, packaged and shared in an online repository on GitHub under the MIT License. Since the model is shared under an open license, the idea and intention are that it can be further developed by other researchers and organizations. This first version only generates a static body shape whereas future versions could include joint center prediction models and deformation patterns in relation to joint angles, to enable accurate body deformation during different postures and motions.