Högskolan i Skövde

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.

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.

This paper presents an interview study aiming to understand the state of the art of how ergonomics designers work in the vehicle development process within the Swedish automotive industry. Ten ergonomic designers from seven different companies participated in the interview study. Results report the ergonomics designers' objectives, workflow, tools, challenges, and ideal work performance tool. We identify four main gaps and research directions that can enhance the current challenges: human behavior predictions, simulation tool usability, ergonomics evaluations, and integration between systems.

Recently, exoskeletons have been gaining popularity in many industries, primarily for supporting manual assembly tasks. Due to the relative novelty of exoskeleton technologies, knowledge about the consequences of using these devices at workstations is still developing. Digital human modelling (DHM) and ergonomic evaluation tools may be of particular use in this context. However, there are no standard integrations of DHM and ergonomic assessment tools for assessing exoskeletons. This paper proposes a general method for evaluating the ergonomic effects of introducing an exoskeleton in a production context using DHM simulation tools combined with a modified existing ergonomic assessment framework. More specifically, we propose adapting the Assembly Specific Force Atlas tool to evaluate exoskeletons by increasing the risk level threshold proportionally to the amount of torque that the exoskeleton reduces in the glenohumeral joint. We illustrate this adaptation in a DHM tool. We believe the proposed methodology and the corresponding workflow can be helpful for decision-makers and stakeholders when considering implementing exoskeletons in a production environment.

Recent developments in commercial virtual reality (VR) hardware with embedded eye-tracking create tremendous opportunities for human subjects researchers. Accessible eye-tracking in VR opens new opportunities for highly controlled experimental setups in which participants can engage novel 3D digital environments. However, because VR embedded eye-tracking differs from the majority of historical eye-tracking research, in both providing for relatively unconstrained movement and stimulus presentation distances, there is a need for greater discussion around methods for implementation and validation of VR based eye-tracking tools. The aim of this paper is to provide a practical introduction to the challenges of, and methods for, 3D gaze-tracking in VR with a focus on best practices for results validation and reporting. Specifically, first, we identify and define challenges and methods for collecting and analyzing 3D eye-tracking data in VR. Then, we introduce a validation pilot study with a focus on factors related to 3D gaze tracking. The pilot study provides both a reference data point for a common commercial hardware/software platform (HTC Vive Pro Eye) and illustrates the proposed methods. One outcome of this study was the observation that accuracy and precision of collected data may depend on stimulus distance, which has consequences for studies where stimuli is presented on varying distances. We also conclude that vergence is a potentially problematic basis for estimating gaze depth in VR and should be used with caution as the field move towards a more established method for 3D eye-tracking.

Simulation technologies are widely used in industry as they enable efficient creation, testing, and optimization of the design of products and production systems in virtual worlds, rather than creating,testing, and optimizing prototypes in the physical world. In an industrial production context, simulation of productivity and ergonomics helps companies to find and realize optimized solutions that uphold profitability, output, quality, and worker well-being in their production facilities. However, these two types of simulations are typically carried out using separate software, used by different users, with different objectives. This easily causes silo effects, leading to slow development processes and sub-optimal solutions. This paper reports on research related to the realization of an optimization framework that enables the concurrent optimization of aspects relating to both ergonomics and productivity. The framework is meant to facilitate the inclusion of Ergonomics 4.0 in the Industry 4.0 revolution.

DHM tools have been widely used to analyze and improve vehicle occupant packaging and interior design in the automotive industry. However, these tools still present some limitations for this application. Accurately characterizing seated posture is crucial for ergonomic and safety evaluations. Current human posture and motion predictions in DHM tools are not accurate enough for the precise nature of vehicle interior design, typically requiring manual adjustments from DHM users to get more accurate driving and passenger simulations. Manual adjustment processes can be time-consuming, tedious, and subjective, easily causing non-repeatable simulation results. These limitations create the need to validate the simulation results with real-world studies, which increases the cost and time in the vehicle development process. Working with multiple Swedish automotive companies, we have begun to identify and specify the limitations of DHM tools relating to driver and passenger posture predictions given predefined vehicle geometry points/coordinates and specific human body parts relationships. Two general issues frame the core limitations. First, human kinematic models used in DHM tools are based on biomechanics models that do not provide definitions of these models in relation to vehicle geometries. Second, vehicle designers follow standards and regulations to obtain key human reference points in seated occupant locations. However, these reference points can fail to capture the range of human variability. This paper describes the relationship between a seated reference point and a biomechanical hip joint for driving simulations. The lack of standardized connection between occupant packaging guidelines and the biomechanical knowledge of humans creates a limitation for ergonomics designers and DHM users. We assess previous studies addressing hip joint estimation from different fields to establish the key aspects that might affect the relationship between standard vehicle geometry points and the hip joint. Then we suggest a procedure for standardizing points in human models within DHM tools. A better understanding of this problem may contribute to achieving closer to reality driving posture simulations and facilitating communication of ergonomics requirements to the design team within the product development process.

Work-related musculoskeletal disorders (WMSDs) constitute a large part of work absences among industry workers, together with all the health and economic problems that it carries. Exoskeletons developed for overhead operations can potentially be a solution to reduce risks for WMSDs. However, some companies are still hesitant to implement exoskeletons in their workplace, since the effects of using exoskeletons are still not fully proved. Digital human modeling (DHM) could help with this dilemma by facilitating studies of the viability of the exoskeletons for specific work tasks. This paper proposes a DHM based framework to implement the study of upper body exoskeletons focused on overhead assembly operations. The framework emphasizes the kinematics and forces interaction between the human and the exoskeleton. 

OCCUPATIONAL APPLICATIONS

Worker well-being and overall system performance are important elements in the design of production lines. However, studies of industry practice show that current design tools are unable to consider concurrently both productivity aspects (e.g., line balancing and cycle time) and worker well-being related aspects (e.g., the risk of musculoskeletal disorders). Current practice also fails to account for anthropometric diversity in the workforce and does not use the potential of multi-objective simulation-based optimization techniques. Accordingly, a framework consisting of a workflow and a digital tool was designed to assist in the proactive design of workstations to accommodate worker well-being and productivity. This framework uses state-of-the-art optimization techniques to make it easier and quicker for designers to find successful workplace design solutions. A case study to demonstrate the framework is provided

TECHNICAL ABSTRACT

Rationale: Simulation technologies are used widely in industry as they enable efficient creation, testing, and optimization of the design of products and production systems in virtual worlds. Simulations of productivity and ergonomics help companies to find optimized solutions that maintain profitability, output, quality, and worker well-being. However, these two types of simulations are typically carried out using separate tools, by persons with different roles, with different objectives. Silo effects can result, leading to slow development processes and suboptimal solutions.

Purpose: This research is related to the realization of a framework that enables the concurrent optimization of worker well-being and productivity. The framework demonstrates how digital human modeling can contribute to Ergonomics 4.0 and support a human factors centered approach in Industry 4.0. The framework also facilitates consideration of anthropometric diversity in the user group.

Methods: Design and creation methodology was used to create a framework that was applied to a case study, formulated together with industry partners, to demonstrate the functionality of the noted framework.

Results: The framework workflow has three parts: (1) Problem definition and creation of the optimization model; (2) Optimization process; and (3) Presentation and selection of results. The case study shows how the framework was used to find a workstation design optimized for both productivity and worker well-being for a diverse group of workers.

Conclusions: The framework presented allows for multi-objective optimizations of both worker well-being and productivity and was successfully applied in a welding gun use case.