Challenges in interaction modelling with digital human models – A systematic literature review of interaction modelling approaches

Abstract

Digital human models (DHM) allow for a proactive ergonomic assessment of products by applying different models describing the user-product interaction. In engineering design, DHM tools are currently not established as computer-aided ergonomics tools, since (among other reasons) the interaction models are either cumbersome to use, unstandardised, time-demanding or not trustworthy. To understand the challenges in interaction modelling, we conducted a systematic literature review with the aim of identification, classification and examination of existing interaction models. A schematic user-product interaction model for DHM is proposed, abstracting existing models and unifying the corresponding terminology. Additionally, nine general approaches to proactive interaction modelling were identified by classifying the reviewed interaction models. The approaches are discussed regarding their scope, limitations, strength and weaknesses. Ultimately, the literature review revealed that prevalent interaction models cannot be considered unconditionally suitable for engineering design since none of them offer a satisfactory combination of genuine proactivity and universal validity.Practitioner summary: This contribution presents a systematic literature review conducted to identify, classify and examine existing proactive interaction modelling approaches for digital human models in engineering design. Ultimately, the literature review revealed that prevalent interaction models cannot be considered unconditionally suitable for engineering design since none of them offer a satisfactory combination of genuine proactivity and universal validity.

Computer Aided Ergonomics: Evaluation Study of a Interaction Model for Digital Human Models

“…Engineering design lacks data-driven methods and tools for predictive and versatile applicable interaction modelling, in order to analyse digital product model states virtually and proactively using DHM, in terms of a computer aided ergonomics tool (Wolf et al, 2020). In order to address this research gap we developed and initially evaluated a phenomenological predictive interaction model.…”

“…We found, that existing interaction models either address specific use cases in engineering and industrial design, like vehicle interior design (Jung et al, 2009;Wirsching, 2019) or sports equipment (Miehling et al, 2015), or are utilized for occupational design tasks, like assembly line or workplace design (Bauer et al, 2019;Hanson et al, 2019). This is due to the fact, that all methods of interaction modelling (no matter if using statistical, machine learning or optimization based approaches) rely on some sort of experimental data in order to predict human behaviour validly (Wolf et al, 2020). In the case of occupational design tasks, working task catalogues, like the MTM-system (Kuo and Wang, 2009), exist, which enable the development of versatile applicable interaction models.…”

Virtual and real assessment of a wide antral ablated region in atrial fibrillation patients using the hot balloon system

“…CAE means computer-aided engineering, and it is capable of simulating a wide variety of physical phenomena in both the industrial world and the biomedical engineering. 4 In this analysis, real parts were expressed as a divided mesh division. By setting each parameter for each mesh, it can simulate various phenomena by using mechanical equations.…”

A posture prediction method for ergonomic assessment of user-product interactions while grasping using musculoskeletal human models

“…Beside the common approach of motion simulation based on subject tests, there are already some procedures for motion prediction without prior experimental collection of specific motion data. This saves the time-consuming, expensive and possibly dangerous execution and recording of motion sequences with subjects [14]. Farahani et al [8] present an approach by using constrained optimization with limitations of the DHM's locomotor apparatus for a predictive motion generation.…”