Högskolan i Skövde

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.

Predicting human upper extremity reaching motion in 3D space can support adaptive interactions with computer-controlled systems (robots and virtual avatars), and applications in ergonomics and rehabilitation. This study compares two predictive approaches: an optimization-based method (OPM) and a proposed heuristic method (SFM) that integrates steering dynamics path planning, an adaptive velocity model, and inverse kinematics. Both methods were validated against motion capture data from ten participants performing four reach tasks. Predictions and inter-subject variability were evaluated for path, velocity, and upper extremity joint configuration using root mean square error and dynamic time warping. Results show that SFM more accurately predicts spatial path and velocity, whereas OPM achieves greater precision in joint angle estimation. As input, OPM requires the initial and end posturesand the task duration, while SFM needs the initial posture, initial and target end-effector positions, and initial and estimated peak velocity. These results highlight trade-offs between accuracy and behavioral variability when selecting motion prediction methods.

Accurately predicting driving postures and positions is crucial for occupant packaging design to accommodate diverse drivers. However, this is challenging due to individual variability and limited access to user data. Digital human modelling (DHM) tools enable posture prediction in virtual environments. This paper presents a case study comparing two driving prediction methods: a statistical prediction method (SPM) and an optimisation prediction method (OPM). Both were evaluated using data from two car models with different seat heights, involving 199 participants whose seat, eye-point, and steering wheel positions were measured. Results showed SPM was more accurate for vertical positioning, whereas OPM for fore-aft positioning. The effectiveness of each method varied by car model, with SPM aligning better in the higher-seated vehicle and OPM performing better in the lower-seated vehicle. These findings highlight the practical, context-specific performance of posture prediction methods. Methodological insights guide the improvement of DHM tool use in occupant packaging.

Industry 5.0 places worker’s wellbeing at the center of the production process, prioritizing healthy and safety job conditions. Requirements to achieve occupational wellbeing are reducing risks for Work-related Musculoskeletal Disorders (WMSDs) and improving industry workstations. The traditional ergonomics risk assessments are based on human observational evaluation and the results are influenced by observers’ competence. Nowadays, advanced technologies such as motion capture systems are implemented to objectively monitor an operator’s movements over time. By providing real-time, data-driven insights into human movement and posture, systems offer the potential to reduce workplace injuries, enhance productivity, and promote long-term worker health. The purpose of the present study is to evaluate and compare three different approaches for assessing the quantitative biomechanical risk of an industrial task using the RULA method: the observational method, a wearable inertial measurement system, and a 4D stereophotogrammetry. The experiment involves one participant (female, 30 years old) performing a “pick-and-place” worker’s task in a controlled laboratory environment. RULA scores vary across the three approaches, with discrepancies primarily due to differences in how each system captures and measures joint angles. While this preliminary study provides valuable initial insights, the limitation of involving a single participant must be critically acknowledged. Future research will aim to include a larger sample size and conduct statistical analyses. The identification of benefits and limitations of each approach enables researchers, ergonomists, and industry stakeholders to critically select and integrate technology to support the worker’s safety, optimizing human wellbeing and overall system performance.

Product development (PD) increasingly relies on digital tools to support the process of exploring, generating, and evaluating product design proposals. Ergonomics plays a critical role in ensuring that product designs align with human capabilities and needs. Digital human modelling (DHM) tools can simulate human-product interactions and assess ergonomics virtually, before physical prototypes exist. In vehicle design, DHM tools are frequently applied in occupant packaging activities, supporting the design of vehicle interiors that accommodate a diverse user population. Still, although commonly used in industry, DHM tools have various limitations. One challenge is their limited ability to predict human postures and motions with sufficient accuracy. This inaccuracy is the result of current simulation procedures and the prediction models used. To compensate for this, DHM tool users often require significant manual adjustments to produce realistic postures, making the process time-consuming, subjective, and difficult to reproduce. Moreover, the simulation procedures themselves can be complex and inefficient, reducing their accessibility and usefulness in iterative design work. These limitations often lead to costly and time-consuming validation activities involving real users.

This thesis addresses these challenges by developing and evaluating methods and models to enhance the functionality and accuracy of posture and motion predictions in DHM tools. The main contributions are: (1) identifying current practices and challenges in industry when applying DHM tools for ergonomics in PD, (2) developing methods that increase the functionality of DHM tools through improved simulations methods, and (3) developing and evaluating posture and motion prediction models that support more reliable and efficient virtual ergonomics assessments. Collectively, the findings support a more proactive, systematic, and human-centred approach to ergonomics in PD processes.

Produktutveckling förlitar sig alltmer på digitala verktyg för att stödja processen med att utforska, generera och utvärdera produktdesignförslag. Ergonomi spelar en avgörande roll för att säkerställa att produktdesignen är anpassad till människans förmågor och behov. Digitala verktyg för mänsklig modellering (digital human modelling - DHM) kan simulera interaktioner mellan människa och produkt och bedöma ergonomin virtuellt, innan det finns fysiska prototyper. Inom fordonsdesign används DHM-verktyg ofta i aktiviteter som rör förar- och passagerarergonomi, för att stödja utformningen av fordonsinteriörer som passar en mångfald av användare. Dock, även om DHM-verktyg ofta används i industrin, så finns begränsningar av olika slag. En begränsning är DHM-verktygens förmåga att förutsäga mänskliga kroppsställningar och -rörelser med tillräcklig noggrannhet. Denna brist på noggrannhet beror på de nuvarande simuleringsförfarandena och de prediktionsmodeller som används. För att kompensera för detta behöver användare av DHM-verktyg ofta göra betydande manuella justeringar för att åstadkomma realistiska kroppsställningar, vilket gör processen tidskrävande, subjektiv och svår att reproducera. Dessutom kan simuleringsförfarandena i sig vara komplexa och ineffektiva, vilket minskar deras tillgänglighet och användbarhet i iterativt designarbete. Dessa begränsningar leder ofta till kostsamma och tidskrävande valideringsaktiviteter som involverar verkliga användare.

Avhandlingen behandlar dessa utmaningar genom att utveckla och utvärdera metoder och modeller för att förbättra funktionaliteten och noggrannheten av prediktioner av kroppsställningar och -rörelser i DHM-verktyg. De viktigaste bidragen är: (1) identifiering av rådande praktik och utmaningar i industrin vid användning av DHM-verktyg för ergonomi i produktutveckling, (2) utveckling av metoder som ökar funktionaliteten hos DHM-verktyg genom förbättrade simuleringsmetoder, och (3) utveckling och utvärdering av prediktionsmodeller för kroppsställningar och -rörelser som stöder mer tillförlitliga och effektiva virtuella ergonomiska bedömningar. Sammantaget stöder resultaten ett mer proaktivt, systematiskt och människocentrerat förhållningssätt för beaktande av ergonomi i produktutvecklingsprocesser.

This paper presents a process for enhancing driver posture prediction in digital human modelling (DHM) tools by integrating real-world data from driver posture studies. The process leverages key data points such as seat position, steering wheel position, and eye point coordinates to position manikins and extract joint angles for ergonomic analysis. A test study involving 49 drivers was conducted, revealing significant variations in joint angles across different statures. These findings were used to develop stature-based strategies that demonstrated improved predictive accuracy for short and tall stature groups compared to the existing strategy in the DHM tool IPS IMMA. While the results highlight the potential benefits of this approach, limitations such as refined manikin body meshes, seat property considerations, and broader vehicle model validation are recommended. Overall, this method offers a promising solution for addressing incomplete datasets in occupant packaging studies, contributing to the development of more ergonomic and safer vehicle designs.

Human motion prediction for tasks involving obstacle avoidance is critical for digital human modeling, robotics, and ergonomics. This study compares two approaches for predicting minimum clearance distance during upper extremity reaching tasks: an expanded 3D space version of the Steering Dynamics Model (SDM) and a perceived risk-based optimization motion prediction. The optimization-based method integrates biomechanical constraints and Bayesian Decision Theory to model perceived risk, while the SDM predicts emergent paths based on attractor-repeller dynamics. Both methods were tested using experimental data from fifteen participants, who performed reaching tasks around a 3D obstacle recorded with an IMU-based motion capture system. Results show that both methods improve minimum clearance distance predictions compared to a purely artificial sphere obstacle avoidance constraints approach. The SDM provides a computationally efficient alternative to the optimization-based approach while maintaining accuracy. However, the optimization-based method with perceived risk more closely aligns with experimental data, demonstrating the importance of cognitive modeling. The point cloud obstacle representation proved effective in both approaches. Future work should explore parameter tuning, subject-specific adaptations, and additional cognitive modeling techniques to enhance accuracy. These findings improve digital human simulations and real-time human-robot interaction models by integrating biomechanical and cognitive factors in motion prediction.

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.

The human musculoskeletal system’s inherent redundancies allow for infinite potential configurations for any given task. While sometimes seen as a problem for cognitive control systems, motor redundancy also fosters adaptability, learning, and resilience, making it essential for effective motor functioning (Latash, 2012). While many features of human motion and pose production have been identified, it remains unclear how cognitive systems quickly identify and enact motions given the scale of challenges introduced by motor redundancy. This study introduces an inverse kinematics solver, the Forward and Backward Reaching Inverse Kinematics solver (FABRIK) (Aristidou et al., 2016; Lamb et al., 2022). FABRIK uses a novel and lightweight approach to overcoming degree of freedom redundancy in multi-joint systems and may provide insights into human motor control. Initial validations of FABRIK for predicting human motion and pose data, demonstrate strong alignment with recorded data and are comparable to more computationally intensive state-of-the-art methods. We consider the implications of this relatively simple inverse kinematics solver for understanding how cognitive systems might deal with the challenges of motion planning in real time.