Advances in Digital Human Modeling II
Proceedings of the 9th International Digital Human Modeling Symposium, DHM 2025, July 29-31, 2025, Loughborough, UK
- 2025
- Book
- Editors
- Russell Marshall
- Steve Summerskill
- Gregor Harih
- Sofia Scataglini
- Book Series
- Lecture Notes in Networks and Systems
- Publisher
- Springer Nature Switzerland
About this book
This book reports on advances in human modeling techniques, covering cutting edge tool development together with practical application across a broad range of domains including: health and wellbeing, automotive, clothing, military, work environments, and inclusive design. Gathering together the proceedings of the 9th International Digital Human Modeling Symposium, held on July 29-31, 2025, in Loughborough, UK, the book contributes to a growing body of interdisciplinary, applied research, at the interface between computer science, ergonomics, engineering, design, health and technologies.
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Table of Contents
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Frontmatter
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Assessment of Manual Forces in Assembly of Flexible Objects by the Use of a Digital Human Modelling Tool—A Use Case
Aitor Iriondo Pascual, Dan Högberg, Mikael Lebram, Domenico Spensieri, Peter Mårdberg, Dan Lämkull, Erik EkstrandThis chapter delves into the challenges of assembling flexible objects, such as electrical wire harnesses in electric vehicles, and how these tasks can be physically demanding for operators. It presents a use case at Volvo Cars AB, where digital human modelling tools are used to simulate and evaluate the forces involved in manual assembly tasks. The study integrates the Arm Force Field method to compare the forces demanded by the assembly task with the force capacity of the operators. Additionally, it uses RULA and REBA methods to evaluate postural risks during the assembly. The simulation provides insights into how digital human modelling tools can assist in designing workstations that support operators and reduce physical strain. The results highlight the importance of considering both force and postural constraints in workstation design to ensure worker well-being and productivity. The chapter also discusses the limitations of the current methods and suggests areas for future research and development.AI Generated
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AbstractThe 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. -
Machine Learning for Ergonomic Workload Assessments: Handled Load Estimation Based on Kinematic Data—A Case Study
Markus Peters, Wolfgang Potthast, Sascha WischniewskiThis chapter explores the application of machine learning algorithms to estimate the weight loads of lifting boxes based on kinematic data collected from wearable IMU sensors. The study investigates the robustness and reliability of these models in discriminating different weight loads during lifting tasks. Key topics include the methodology of data acquisition and analysis, the performance of various machine learning models, and the identification of crucial features for accurate load classification. The results demonstrate that machine learning models, particularly SVM and KNN, can achieve over 90% accuracy in classifying weight loads, highlighting the potential of this approach for ergonomic workplace assessments. The study also discusses the limitations and future directions for practical implementation, suggesting that further research with larger and more diverse datasets is needed to validate the approach for real-world applications.AI Generated
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AbstractWork-related musculoskeletal disorders are often associated hazardous body postures and repetitive tasks. To ensure good working conditions, workplaces must be ergonomically designed. Risk assessments are used to identify inadequate workplace design. Sometimes this can a time-consuming process when analyzing the movements of workers and handled loads. In particular, it is currently difficult to assess the external forces acting upon workers. The use of wearable sensor technology for ergonomic assessment could assist ergonomists in this task in the near future. The emerging field of machine learning opens up new possibilities for data processing. A case study was conducted to investigate the extent to which external forces, which normally require complex measurement technology, can be estimated from kinematic data alone. For this purpose, a full-body kinematic (17 portable IMU sensors) was used. A subject performed 1125 lifts with 5 different heavy boxes (0–20 kg). The support vector machine classifier showed an average accuracy of over 90% for the test data set. The speed of the forearms towards the body, upper body movement and the flexion of the wrist provided the most important information for the classification models. The results indicate that weight classification under standardized conditions can be performed only on the basis of kinematic data and an individual model. However, large data sets are needed to generalize the results. -
Task Time Estimations in an Optimization-Based Digital Human Modelling Tool—A Case Study
Peter Mårdberg, Dan Högberg, Johan S. Carlson, Dan Lämkull, Kristina Wärmefjord, Rikard SöderbergThis study delves into the comparison of task time estimations for an assembly task in the automotive industry, using an optimization-based digital human modeling tool and real-world industry standards. The research focuses on the discrepancies between the time computed by the DHM tool and the time determined by a company specialist, highlighting the reasons behind these differences. Key areas of exploration include the use of MTM-1 data for motion time estimation, the impact of different PMTS methods, and the influence of anthropometric variations on task times. The study also discusses the potential improvements in the DHM tool, such as better trajectory modification and more accurate motion profiles. The results show a significant deviation between the DHM tool's time estimation and the company's standard, attributed to differences in PMTS methods and the subjective nature of trajectory creation. The analysis suggests that further development and validation of manikin motion profiles are necessary to enhance the accuracy and trustworthiness of digital human modeling tools in task time estimation.AI Generated
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AbstractOptimization-based digital human modelling (DHM) can compute manikin motions for unique work tasks, requiring no motion capturing or motion data manipulation to simulate new work tasks. Also, optimization-based DHM can consider prevailing force and torque exertions, e.g. pushing or twisting, in the motion computations. This makes optimization-based DHM well suited for assessing workstation designs early in virtual development phases. When using optimization-based DHM to simulate work tasks and determine task times in settings such as manual assembly, it is crucial that the manikin motion durations can be set to comply with predetermined motion time systems (PMTS) data. As part of realizing this objective, this study compares assembly times generated by an optimization-based DHM tool, where durations of discrete manikin motions are determined based on PMTS data, against an industrial use case with known assembly times, determined according to the company standard. The comparison aims to identify the differences between the times generated by the DHM tool and the times determined in accordance with the company standard, understand why they occur and how they potentially can be addressed. The findings support establishing a road map for future research and development for improving task time estimations in optimization-based DHM. -
Rough Estimation of Reaching Posture to Visualize Accessibility-Related Injury Risk
Natsuki Miyata, Rei Yamamoto, Yusuke MaedaThis chapter delves into the development of a system to visualize and assess reachability-related injury risks in home environments, focusing on both children and adults. The study introduces a machine learning-based method to determine initial postures for digital human models, which are then used to generate final postures through optimization. The system evaluates reachability risks by considering various factors such as hand-to-target distance, foot contact, collisions, joint range of motion, and mechanical stability. The effectiveness of the proposed method is demonstrated through a case study that examines risk assessment and dimensional improvements in indoor safety products. The study also explores the impact of environmental adjustments, such as the use of a movable stool, on reachability risks for children. Furthermore, the system's applicability to product design is showcased through an investigation of stove guard heights and their effectiveness in preventing burns for young children. The results highlight the potential of the proposed system to support environmental safety measures and improve product design for injury prevention.AI Generated
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AbstractThis study aims to develop a system that quantitatively evaluates and visualizes indoor areas prone to accidents from the perspective of accessibility by analyzing the reach postures of a digital human model. To obtain a feasible final posture using an optimization method, it is crucial to provide an appropriate initial posture. However, because reach postures vary dynamically depending on location, these postures exhibit discontinuous mechanical modes. Therefore, this study introduces a machine learning-based approach for estimating initial postures and quantitatively evaluates the preventive effects of existing accident prevention products, thereby verifying the effectiveness of the proposed system. -
Evaluating Digital Human Modelling for Workers with Osteoarthritis
Elizabeth Oyemike, Yee Mey Goh, Rebecca GrantThis chapter delves into the prevalence of osteoarthritis (OA) among UK workers, particularly in the manufacturing sector, and its impact on their mobility and ability to perform tasks. The study focuses on evaluating digital human modelling (DHM) to simulate and assess the ergonomic challenges faced by workers with OA in different manufacturing scenarios. Four digital human models representing control and OA men and women were created, with adjusted joint motion ranges to mimic the limitations imposed by OA. Three scenarios were defined to represent varying levels of manufacturing automation: manual, mechanical, and automated. The Ovako Working posture Analysing System (OWAS) was used to generate ergonomic assessment reports, highlighting the differences in postural risk between the models. The results indicate that workers with OA, particularly women, experience higher levels of discomfort and difficulty in performing tasks, with specific steps in each scenario posing significant challenges. The chapter concludes by emphasizing the importance of considering worker health and mobility in the initial stages of manufacturing design to avoid costly reconfigurations and occupational health concerns. It also suggests future applications that could explore a wider range of percentile populations and integrate motion capture techniques for more realistic testing and validation.AI Generated
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AbstractThis study explores the use of Digital Human Modelling (DHM) with Siemens’ Process Simulate to enhance inclusive design in manufacturing, aligning with Industry 5.0’s focus on human-centric and adaptive systems. Traditional user testing often poses financial and physical challenges, particularly for individuals with disabilities. By simulating users with and without joint impairments (e.g., osteoarthritis), this research investigates the potential of DHM to minimize physical testing and proactively identify ergonomic risks.The project involved three manufacturing scenarios: manual (using a screwdriver), mechanical (using a nail gun), and automated (robot-assisted assembly). Ergonomic assessment (OWAS) was employed to evaluate the safety and comfort of different user models. Findings revealed that manual tasks posed higher risks to users with limited joint mobility, while automated scenarios significantly reduced these risks. It was also found that women experience greater discomfort throughout the programmed tasks, made worse when joint flexion is limited, like Osteoarthritis cases. This indicates that DHM helps to represent diverse user needs, informing the universal design of safer, more accessible workplaces, ahead of physical build and setup. -
Perceived Cognitive Workload and Body Posture: Emerging Factors Affecting Work Performance of Office Chair Assembly Operator/Worker
Nurul Izzah Abd Rahman, Ahmad Farez Aqmar AzharThis study delves into the critical factors affecting the performance of office chair assembly operators, with a focus on cognitive workload and body posture. Through the use of the NASA Task Load Index (NASA-TLX) and the Rapid Upper Limb Assessment (RULA), the research identifies the significant impact of task complexity and production frequency on operator performance. The findings reveal that while high-frequency production days may reduce perceived cognitive and physical demands, they also increase frustration levels. Moreover, the study highlights the prevalence of musculoskeletal discomfort among operators, particularly in the wrists/hands, upper back, and hips, underscoring the need for targeted ergonomic interventions. The research concludes that a holistic approach, integrating ergonomic workstation redesigns, task rotation strategies, and cognitive support tools, is essential for optimizing worker well-being and productivity in the office chair assembly industry.AI Generated
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AbstractThis study investigates the cognitive workload and body posture of operators in the manufacturing of office chairs, with a focus on understanding the relationship between cognitive demands, physical strain, and work performance in an assembly line setting. A multi-faceted approach was adopted, integrating subjective assessments, objective ergonomic evaluations, and performance metrics. The study aims to measure anthropometric dimensions, assess cognitive workload and task performance, conduct a Rapid Upper Limb Assessment (RULA), and explore the relationship between perceived cognitive workload, RULA scores, and task performance. It is hypothesized that increased cognitive workload and task complexity negatively impact task performance in office chair assembly, while poor posture further reduces performance and increases physical discomfort. The experiment involves nine office chair assembly operators and utilizes validated self-reported scales such as NASA-TLX and the Nordic Musculoskeletal Questionnaire (NMQ), along with observational techniques including RULA analysis conducted manually and via CATIA V5 software. Findings revealed a paradox where high-frequency production days were associated with lower perceived cognitive demands (from 58 to 49) and physical demands (from 63 to 56) but higher frustration levels (from 38 to 45) under conditions of increased workload. The RULA analysis highlighted ergonomic risks, leading to recommendations for ergonomic interventions. In conclusion, this study offers valuable insights into the cognitive and physical challenges faced by assembly line workers, advocating for task rotation, ergonomic interventions, and cognitive support tools. Therefore, it aligns with the SDG 8 by addressing factors that influence productive employment and decent work conditions in manufacturing. -
Minimum Clearance Distance Prediction in Manual Collision Avoidance Reaching Tasks: Perceived-Risk-Based Motion Versus Steering Dynamics Model
Juan Baus, Estela Perez Luque, Maurice Lamb, James YangThis study delves into the intricate world of human motion prediction, specifically focusing on manual collision avoidance reaching tasks. By comparing the Steering Dynamics Model (SDM) and an optimization-based approach that incorporates perceived risk, the research sheds light on the critical role of cognitive factors in accurate path planning. The study involves experimental data collection using an IMU-based motion capture system, with participants performing reaching tasks while avoiding obstacles. The SDM, inspired by dynamical systems principles, simulates goal-directed behavior and obstacle avoidance through attractor-repeller dynamics. On the other hand, the optimization-based approach uses an upper extremity model to represent the subject's dominant hand, formulating the reaching motion as a nonlinear optimization problem. The results reveal that both methods produce different predictions while capturing common aspects of the reaching motion. Notably, the optimization-based approach with perceived risk more closely matches experimental data, highlighting the importance of cognitive factors in human motion. The study concludes that while both methods have their strengths and limitations, incorporating perceived risk into motion prediction models can significantly enhance their accuracy and realism. This research offers valuable insights for professionals in robotics, virtual reality, and ergonomics, providing a deeper understanding of human motion prediction and its practical applications.AI Generated
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AbstractHuman 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. -
Learning Multi-mode Musculoskeletal Motion for Natural Walking and Running Gaits
Steve Chen, Neethan Ratnakumar, Xianlian ZhouThis chapter explores the development of a unified framework for learning multi-mode musculoskeletal locomotion, focusing on walking and running gaits. The study introduces a novel approach using Feature-wise Linear Modulation (FiLM) layers to integrate mode-specific designs within a single controller, enabling seamless transitions between walking and running. Key topics include the structure of the control framework, the use of reinforcement learning for training, and the implementation of reward functions to achieve natural gait patterns. The results demonstrate that the model can generate biomechanically plausible gaits that closely resemble experimental data, with smooth transitions between walking and running. The chapter also discusses the advantages of this approach over traditional methods, highlighting its potential for applications in robotics, biomechanics, and animation.AI Generated
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AbstractTraditional musculoskeletal control policies primarily focus on individual gait patterns, with limited success in developing a single model capable of handling multiple locomotion modes, such as walking and running. While prior work has explored learning different gaits within a shared framework, existing approaches typically train independent models rather than a unified, adaptable policy. In this paper, we propose a multi-mode controller learning framework that leverages Feature-wise Linear Modulation (FiLM) to integrate mode-specific dynamics into a unified controller. By incorporating FiLM layers into the control policy, we enable efficient adaptation between locomotion modes while maintaining network simplicity. To further enhance generalizability and robustness, we incorporate skeleton scaling and muscle condition randomization, which broaden the model’s state-space exploration and significantly improve transition stability. Additionally, we introduce a novel reward design that regulates stride characteristics, facilitating the learning of natural walking and running behaviors across varying speeds. Our results show that the proposed framework produces coordinated, natural-looking gaits and smooth transitions between modes, demonstrating the effectiveness of FiLM in unified musculoskeletal locomotion control. -
Kinematics of the Wrist Movement
Esteban Pena-Pitarch, Anas Al Omar, Inaki Alcelay Larrion, Pau Catala, Hector SanzThis chapter delves into the complex world of wrist kinematics, focusing on the movements of the eight carpal bones and their relative motions. The authors present a detailed model using the Denavit-Hartenberg approach to simulate these movements, highlighting the challenges posed by the wrist's inherent instability. The chapter explores two primary types of wrist movements: Flexion/Extension and Radial/Ulnar Deviation, providing insights into the roles of various ligaments and bones in these motions. It also discusses specific instabilities like Dorsal Intercalated Segment Instability (DISI) and Volar Intercalated Segment Instability (VISI), emphasizing the importance of understanding these conditions for surgical restoration. The results section outlines the proposed model, which includes both prismatic and revolute joints to mimic the wrist's complex movements. The conclusions highlight the potential of this model for future applications, including the incorporation of ligaments and dynamics. This chapter is a must-read for anyone seeking a deeper understanding of wrist biomechanics and the simulation of wrist movements.AI Generated
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AbstractThe need to create a virtual model of the upper extremity, and more specifically of the hand and wrist, arose from the need to better understand how the wrist bones move relative to each other, especially when surgical reconstruction of wrist bone movements is required for subsequent rehabilitation. A hand model with 29 degrees of freedom (DOF) was previously proposed, including forward and inverse kinematics for all fingers, along with a realistic virtual simulation. However, the model did not include the wrist from the perspective of relative movement between the two rows of carpal bones. Recent studies in the literature have highlighted the importance of the relative movement between the two rows of wrist bones.The objective is develop a new wrist model that incorporates the movement of the two rows and eight bones of the wrist.A new coordinate system was established at the distal end of the radius near the scaphoid. Using the Denavit-Hartenberg convention, forward and inverse kinematics were applied to model wrist motion. Additionally, ten ligaments were incorporated to impose movement constraints that directly affect fingertip positions.As a results a new virtual human hand model with improved accuracy was developed. This enhanced hand model, which includes wrist movements, provides a more accurate representation of the human hand. New degrees of freedom were added to the original 29 DOF model. -
Improving Anthropometric Datasets: Comparison of Machine Learning and Statistical Approaches to Fill Data Gaps
Alexander Ackermann, Sascha WischniewskiThis chapter delves into the comparison of machine learning and statistical approaches to address data gaps in anthropometric datasets, crucial for digital human modeling in ergonomic design. The study evaluates linear regression, support vector machines, gradient boosting trees, and k-nearest neighbors models using datasets from the Study of Health in Pomerania (SHIP) and the 2012 US Army Anthropometric Survey (ANSUR II). Key findings reveal that support vector machines with a linear kernel and linear regression outperformed other models, yet their root mean square error (RMSE) values often exceeded acceptable thresholds. The chapter also explores the impact of dataset characteristics, such as population homogeneity and gender-specific differences, on model performance. It concludes that more sophisticated modeling approaches are needed to reliably fill data gaps in anthropometric datasets, paving the way for future research in this area.AI Generated
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AbstractDigital Human Models (DHM) support virtual ergonomic design and evaluation, but require accurate scaling of physical dimensions based on anthropometric datasets. However, data gaps in anthropometric datasets can pose challenges and limit their usability. This study examined and compared the performance and feasibility of linear regression and several machine learning models (support vector machines (SVM), k-nearest neighbors (KNN), and gradient boosting (GB)) for approximating missing anthropometric data. Two datasets were used: the Study of Health in Pomerania (SHIP) from Germany, which represents a working population, and the ANSUR II dataset, which includes US military personnel. Model training was performed for eleven anthropometric dimensions (i.e., evaluation parameters), using the remaining anthropometric dimensions as features. To improve the comparability between the two datasets, virtual datasets were generated using a synthesis algorithm to harmonize the number of subjects in both datasets. Model performance was assessed using the root mean square error (RMSE) and compared to allowable error (AE) thresholds defined in ISO 20685-1. Results revealed that SVM and linear regression outperformed KNN and GB. However, except for one anthropometric dimension within the ANSUR II dataset, RMSE values exceeded AE thresholds, suggesting that the investigated models were not sufficient to fill data gaps. Further analysis showed that model performance was influenced by dataset characteristics, with the military dataset yielding lower errors, likely due to its relatively greater homogeneity. Future research will focus on more sophisticated modeling approaches and broader anthropometric dataset integration to enhance compliance with ISO standards and generalizability of results. -
The Use of Digital Human Models for Passenger Simulation in Automated Vehicles
Miriam Schäffer, Wolfram RemlingerThe chapter delves into the transformative potential of automated driving technology, emphasizing its role in enhancing road safety, optimizing traffic flow, and reducing emissions. It explores the challenges posed by non-driving related activities (NDRA) and take-over procedures, particularly the variability of driver states and body movements. The text introduces the concept of digital human models (DHMs) as a solution for systematically observing and validating these scenarios, highlighting their importance for the development and validation of automated driving in SAE Level 4. It also discusses the HoMoTo model, which structures the take-over procedure into distinct phases, and the need for a standardized description format for human body postures and movements. The chapter concludes with a vision for the future of automated driving, emphasizing the importance of digital verification of NDRA and take-over scenarios to ensure occupant safety and meet regulatory standards.AI Generated
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AbstractAutomated driving (SAE Level 3 and Level 4) allows drivers to engage in non-driving activities (NDRA). However, current vehicle interiors and safety measures are designed for a specific driving posture, while NDRA introduce varied body postures. Additionally, transitioning from an NDRA back to driving adds further complexity. Testing all possible scenarios in real vehicles is costly and time-consuming, making digital validation using human models essential. This contribution outlines, how DHMs enable systematic monitoring of posture transitions, movement, and timing in NDRA and take-over scenarios. To fully validate automated driving, integrating these models with digital crash dummies and simulation structures is beneficial. This approach may also extend beyond vehicles to assess drivers’ cognitive states. Based on the existing HoMoTo model, a structured scenario description supports this effort. In order to pursue this research objective, a project idea was drafted to advance simulation-based testing and is presented in this contribution. Similar to the SOTIF standard (ISO 21448), this initiative aims to help manufacturers validate NDRA and driver take-overs through simulations. -
Digital Human Modelling in Fusion Power Plants: An Early Look
Nitesh Bhatia, James Boock, Stanislas J. P. Pamela, Andrew Davis, Robert AkersThis chapter delves into the application of Digital Human Modelling (DHM) in fusion power plants, focusing on key areas such as control room design, maintenance planning, emergency response, and inclusive design. It highlights the potential of DHM to enhance safety, operational efficiency, and inclusivity in these complex, high-stakes environments. The text explores the use of DHM in simulating human interactions with systems, tools, and workspaces, and its role in improving decision-making and risk management. It also discusses the challenges and limitations of current DHM frameworks, including the need for better cognitive and behavioural simulations. The chapter concludes by emphasizing the importance of integrating DHM with digital twin environments and the potential of advancements in machine learning, artificial intelligence, and immersive technologies to enhance DHM capabilities. Additionally, it underscores the necessity of early and comprehensive integration of DHM into the design lifecycle of fusion power plants to ensure a resilient, human-centered, and safe fusion infrastructure.AI Generated
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AbstractLarge scale commercial and research fusion power plants, like the UK’s STEP Prototype and France’s ITER, are a key move toward making clean, sustainable energy to meet the world’s growing needs. These complex systems rely on highly interdependent, complicated systems with numerous variables that must be carefully designed, operated, maintained, and decommissioned. Digital Human Modelling (DHM) based simulations emerge as a transformative tool to address human factors throughout the facility lifecycle, promising enhanced safety, operational efficiency, and human-machine collaboration within the fusion sector. This paper explores scope of DHM technology in five critical fusion engineering domains: (1) Construction and Assembly of Fusion Power Plants; (2) Control Room Design, optimising ergonomic layouts to reduce cognitive and physical strain during high-pressure operations; (3) Maintenance, Planning and Remote Handling, supporting intuitive teleoperation systems for effective human-robot collaboration in hazardous areas; (4) What-If Scenarios, simulating emergencies like evacuations or equipment failures to refine safety protocols and operator response; (5) Inclusive Design and Accessibility Assurance, making certain that critical zones and pathways serve a variety of populations, including those with physical disabilities. -
Applications of Digital Human Modelling: Applying EU Direct Vision Regulations to North American HGV Designs
Steve Summerskill, Russell MarshallThis chapter delves into the application of the EU's UNECE 167 direct vision regulations to North American Heavy Goods Vehicle (HGV) designs, focusing on the use of digital human modelling to assess and visualize blind spots. The study compares the direct vision performance of three North American HGVs—Peterbilt 389, International LT, and Lion 6—against EU vehicles, highlighting significant differences in vehicle designs and their impact on road safety. The chapter explores the methodology of using digital human modelling to define driver eye points, calculate visible volumes, and correlate them with VRU (Vulnerable Road User) distances. It also discusses the implications of the findings for the further application of UNECE 167 in North America, including potential modifications to the standard and the need for additional VRU simulations. The results reveal considerable blind spots in North American HGVs, particularly to the front and near side of the vehicles, which can hide cyclists and pedestrians from the driver's view. The chapter concludes with a discussion on the value of digital human modelling in analyzing accident scenarios and simulating driver vision, emphasizing the need for further work to establish a larger sample of North American vehicles and explore key variables such as driver eye location and assessment volume design.AI Generated
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AbstractHeavy Goods Vehicles (HGVs) present a significant risk to Vulnerable Road Users (VRUs), including cyclists and pedestrians, due to extensive blind spots. While overall road fatalities in the EU and UK have declined by 22% from 2010 to 2023, the number of HGV-related VRU fatalities remains disproportionately high. This study evaluates the direct vision capabilities of North American HGVs using the UNECE 167 regulation, a standard developed to enhance direct vision through digital human modelling (DHM) and volumetric analysis. The research examines the blind spots of selected North American HGVs using CAD-based assessment methods. Three vehicles, a Peterbilt 389, an International LT, and a LION 6 electric truck, were analysed using 3D scanning and DHM techniques to assess visibility. Results indicate that while North American cab-over-engine designs generally provide better visibility, significant blind spots remain, particularly in front of and beside the vehicles. The Peterbilt exhibited the largest frontal blind spot, while the International LT showed poor nearside visibility. The LION 6 demonstrated good frontal vision but performed poorly in nearside visibility, similar to tall EU trucks. Findings highlight the need for adaptations of UNECE 167 to better fit North American vehicle designs and regulations. Additionally, the study underscores the value of DHM systems in improving vehicle safety analysis. Ongoing research aims to expand the vehicle sample to refine blind spot measurement and propose design enhancements for improved VRU safety. -
Enhancing Soldier Readiness: Biomechanical Evaluation of the U.S. Army Combat Fitness Test Using Santos Digital Human Modeling
Karim Abdel-Malek, Rajan Bhatt, Laura Frey Law, Chris Murhphy, Bahaa MohammadThe chapter delves into the application of the Santos digital human twin platform to evaluate the U.S. Army Combat Fitness Test (ACFT), focusing on several key areas. Firstly, it explores the platform's advanced biomechanical modeling capabilities, including kinematics and dynamics, strength and fatigue prediction, and computational physiology. Secondly, the text discusses the integration of AI and cognitive models to induce behavior and predict musculoskeletal injuries. Thirdly, it highlights the platform's rigorous validation processes and its application to real-world military tasks. Lastly, the chapter presents the findings of a comparative analysis between the ACFT and common soldier tasks, providing insights into the physical demands of these tasks. By reading the full chapter, professionals will gain a comprehensive understanding of how the Santos platform can enhance soldier readiness and improve the design of military fitness tests.AI Generated
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AbstractSANTOS is a physics-based human digital twin platform developed to support human modeling and simulation (M&S) for applications in human systems integration, product development, and human performance. Central to the platform are two digital human models, Santos and Sophia, designed to simulate human physiology and motion with a high degree of fidelity. The system builds on over two decades of research and is intended to provide a computational alternative to physical testing for evaluating individual performance and designs, early in the cycle. SANTOS employs Human Predictive Dynamics (HPD), an optimization-based approach that predicts human motion without explicitly solving differential equations of motion. This methodology accounts for mass, inertia, joint velocities and accelerations, external forces, muscle strength, fatigue, and other physiological parameters, producing realistic movement trajectories under task-specific constraints. The musculoskeletal model incorporates 215 degrees of freedom and integrates biomechanics, physiological modeling, artificial intelligence components, and extensive empirical data derived from military and industrial sources. The platform’s utility is demonstrated through its application in assessing performance on the Army Combat Fitness Test (ACFT), among other military use cases. This paper presents the mathematical framework of the HPD approach and discusses its implications for enhancing Soldier readiness through predictive human performance modeling. -
The Effect of Saddle Height on Hip and Knee Kinematics Using a 4D Scanning Device in Cycling Biomechanics: A Pilot Study
Sofia Scataglini, Ruben De Block, Stien Mannaert, Yana Callaerts, Kiara De Backer, Steven TruijenThis pilot study delves into the intricate relationship between saddle height and hip and knee kinematics in cycling, utilizing cutting-edge 4D scanning technology. The research focuses on four key areas: the impact of saddle height on knee and hip joint angles, the comparison between preferred and formula-based saddle heights, the symmetry of lower-limb biomechanics between the right and left legs, and the practical applications of these findings for cyclists. The study reveals that adjusting saddle height significantly affects knee extension range of motion, with an average reduction of 6.34° in the left knee when using a formula-based approach. Furthermore, the research underscores the importance of personalized saddle adjustments to enhance cycling performance and reduce the risk of overuse injuries. By comparing preferred and formula-based saddle heights, the study provides valuable insights into optimizing cycling biomechanics and efficiency. The use of 4D scanning technology allows for precise data collection and analysis, offering a comprehensive understanding of how saddle height adjustments influence lower-limb kinematics during cycling.AI Generated
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AbstractCycling biomechanics are critical for optimizing performance and preventing injury. This study investigates how saddle height (SH) adjustments affect hip and knee joint angles in cycling, with implications for performance and injury prevention. The primary goal is to quantify changes in joint angles during the pedal stroke based on SH variations. A secondary aim is to assess the consistency of these changes across different body types and their impact on cycling efficiency and injury risk. Thirteen cyclists (7 female, 6 male) participated in biomechanical assessments on a stationary bike, with measurements taken at their preferred SH and a formula-based SH. Joint angles at the hip and knee were recorded during a full pedal stroke under both conditions. Preliminary results show that formula-based SH adjustments reduced knee extension angles (left: 6.34°, right: 8.04°) compared to the preferred SH. This suggests altered lower-limb mechanics that could affect cycling efficiency and joint loading. These findings emphasize the importance of precise SH adjustments in optimizing bike fitting, enhancing performance, and minimizing injury risks across diverse body types. -
Human-Robot Collaboration: An ANFIS-Based Model for Ergonomic Risk and Stress Evaluation
Emine Bozkus, Ella-Mae Hubbard, Claire GuoThis chapter delves into the complexities of human-robot collaboration (HRC) and introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based model for evaluating ergonomic risks and stress. The study highlights the limitations of traditional ergonomic assessment methods, such as RULA and OWAS, which often overlook the dynamic and cognitive aspects of HRC. By integrating objective biomechanical measurements with subjective assessments like the NASA Task Load Index (NASA-TLX) and Visual Analog Scale (VAS), the proposed ANFIS model offers a more comprehensive and adaptive risk evaluation. The experimental setup involves participants performing assembly tasks both manually and in collaboration with a UR10e cobot, with data collected from RGB-D cameras and subjective questionnaires. The results demonstrate significant improvements in physical ergonomics with cobot assistance, but also reveal increased cognitive workload and stress, underscoring the need for a holistic assessment approach. The ANFIS model's performance is evaluated using metrics like RMSE and MAE, showing strong predictive accuracy. The chapter concludes by discussing the implications of these findings for the future of HRC, emphasizing the importance of integrating both physical and psychological factors to create safer and more efficient collaborative environments.AI Generated
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AbstractHuman-robot collaboration (HRC) in industrial settings increases productivity but introduces ergonomic and psychological stress risks. This paper presents a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model integrating fuzzy logic and artificial neural networks (ANN) to evaluate ergonomic risk and stress in HRC. The model processes imprecise human data (e.g., posture, fatigue indicators, cognitive load) and uses the ANN to learn complex relationships between task factors, particularly in stress-inducing scenarios. An experimental study utilizing a UR10e collaborative robot and RGB-D camera compare manual and collaborative task phases, measuring stress levels through NASA-RTLX and Visual Analogue Scale (VAS). The model’s performance is validated against traditional methods like RULA. This approach offers a robust tool for real-time risk assessment in HRC workstations, advancing worker-centric design and adaptive HRC systems. -
Comparison Between Observational Method, Wearable Inertial Measurement System and 4D Stereophotogrammetry for Ergonomics Risk Assessment: A Case Study
Eugenia Fontinovo, Estela Perez Luque, Alessandra Papetti, Dan Högberg, Lars Hanson, Steven Truijen, Sofia ScatagliniThis study delves into the world of ergonomics risk assessment, comparing three distinct methods: the traditional observational approach, wearable inertial measurement systems (IMUs), and 4D stereophotogrammetry. The research focuses on a 'pick-and-place' task, evaluating the Rapid Upper Limb Assessment (RULA) scores derived from each method. Key findings reveal that while all methods capture key movement phases, discrepancies in RULA scores of up to 2 points exist, with variations influenced by the task and the method used. The study highlights the importance of selecting an appropriate assessment method based on task characteristics, environmental constraints, and available resources. It also suggests that integrating observational methods with motion capture technologies can enhance the accuracy and comprehensiveness of musculoskeletal risk assessments. The results indicate that the right body side, predominantly involved in the task, exhibits higher ergonomic risk, with RULA scores reaching the maximum value of 7. The study concludes by emphasizing the need for further research to understand the factors contributing to agreement or divergence among these methods, ultimately aiming to create safer, healthier, and more productive work environments.AI Generated
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AbstractIndustry 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.
- Title
- Advances in Digital Human Modeling II
- Editors
-
Russell Marshall
Steve Summerskill
Gregor Harih
Sofia Scataglini
- Copyright Year
- 2025
- Publisher
- Springer Nature Switzerland
- Electronic ISBN
- 978-3-032-00839-8
- Print ISBN
- 978-3-032-00838-1
- DOI
- https://doi.org/10.1007/978-3-032-00839-8
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