Individualized vs. Population-based Musculoskeletal Simulation for Medical and Product Engineering
Miehling J (2022)
Publication Language: English
Publication Type: Conference contribution, Abstract of lecture
Publication year: 2022
Event location: Porto
Abstract
INDIVIDUALIZED VS. POPULATION-BASED MUSCULOSKELETAL SIMULATION FOR MEDICAL AND PRODUCT ENGINEERING
Jörg Miehling
Engineering Design, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
Background
Musculoskeletal simulations hold high potentials by revealing the processes and inner strain conditions of the human body for a wide range of areas. The computed physiological parameters can give insights for the engineering of medical and rehabilitation technology, exoskeletons, mobility products and sports equipment [1]. For reliable and physiological simulation results, a musculoskeletal model suitable for the application as well as a way to measure or predict the human motion and if applicable the interaction with the environment or product are necessary [2].
The conventional approach uses observations from optical marker tracking. This is still the gold standard for motion measurement. However, it requires preparation, data acquisition and post-processing effort. More importantly, often just anthropometric scaling is performed based on the marker data to adapt a generic model to a specific person. Other crucial factors such as muscle strength or mobility are regularly disregarded.
Recent Advances
Patient-specific models, however, mostly rely on 3D MRI/CT data. Using parameters extractable from the imaging data, a generic model is adapted to the specific patient, usually in a restricted area of interest [3]. These models are usable for observational and predictive simulations, but only in very specific situations, such as for surgery planning. In most other applications, it is not feasible to access the necessary data, not least due to ethical issues as well as the time and cost involved.
Population-based approaches bridge the gap towards predictive simulations even further. Modelling relies on empirical population data to adapt a generic model to a desired statistical representative of the population.
Based on this direction, a procedure to create consistent groups of musculoskeletal models across different domains (e.g. range of motion, strength) was set up [4]. Such a model group was used in the ergonomic optimization of bicycles and skiffs [2]. These examples as well as the ergonomic optimization of trikes [5] indicate the strength of predictive musculoskeletal simulations for (bio)engineering applications.
In order to increase data consistency between computer-aided-design applications and human models, we work on a computer-aided-ergonomics tool integrating user, product/environment and interaction models by a feature-based description of physical interactions [6, 7].
Current research also focuses on a co-simulation model integrating a musculoskeletal model with exoskeleton and power tool models to optimize support systems [8].
Future directions
Our goal is to provide methods for the assessment of human behavior in order to support (bio)engineering processes. We work on tracking methods integrating multimodal motion measurement data of novel sensor technology leading to more efficient and accessible workflows. We individualize musculoskeletal human models in multiple domains considering subject-specific kinematic and dynamic movement capabilities. Beyond, the extension of human models with sensorimotor regulation offers potentials to deepen the understanding of bodily processes in order to increase the insights of biomechanical simulation results. Moreover, we address a methodology integrating finite element and musculoskeletal models as well as the phenomenological characterization of human behavior for proactive simulation of user-product-interactions.
References
1. Wartzack et al., Proc. ICED19, 3989-3998, 2019.
2. Miehling, VDI, 445, 2018.
3. Scherb et al., CMBBE Conf., 2021.
4. Miehling, Comput. Methods. Biomech. Biomed. Eng., 22(15):1209-1218, 2019.
5. Meißner et al., Konstruktion, accepted.
6. Wolf et al., IISE Trans. Occup. Ergon. Hum. Factors, 2021.
7. Wolf et al., Ergonomics, 63(11):1442-1458, 2020.
8. Molz et al., CMBBE Conf., 2021.
Acknowledgements
This work was (partly) supported by the Deutsche Forschungs-gemeinschaft (DFG, German Research Foundation) under Grant SFB 1483–Project-ID 442419336. This work was (partly) supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grants WA 2913/31-1, WA 2913/41-1 and MI 2608/2-1. The author greatfully acknowledges Prof. Sandro Wartzack, the group members and all collaborators, who contributed to this work.
Jörg Miehling is currently principal investigator and research division leader at the Chair of Engineering Design, FAU Erlangen-Nürnberg, Germany. He completed his doctorate in 2018. His research focuses on musculoskeletal modelling and simulation, computer-aided-ergonomics and user-centered-design. He successfully obtained research grants from the German Research Foundation (DFG), including two projects in a Collaborative Research Centre (CRC/SFB), one of the most competitive and prestigious funding sources in Germany. He is author of more than 50 publications in peer-reviewed journals, book chapters and contributions to international and national conferences.
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How to cite
APA:
Miehling, J. (2022, June). Individualized vs. Population-based Musculoskeletal Simulation for Medical and Product Engineering. Paper presentation at 27th Congress of the European Society of Biomechanics, Porto, PT.
MLA:
Miehling, Jörg. "Individualized vs. Population-based Musculoskeletal Simulation for Medical and Product Engineering." Presented at 27th Congress of the European Society of Biomechanics, Porto 2022.
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