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Wechsler I, Wolf A, Shanbhag J, Leyendecker S, Eskofier BM, Koelewijn AD, Wartzack S, Miehling J. Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review. Front Bioeng Biotechnol 2024; 12:1386874. [PMID: 38919383 PMCID: PMC11196827 DOI: 10.3389/fbioe.2024.1386874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/21/2024] [Indexed: 06/27/2024] Open
Abstract
Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other's weaknesses.
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Affiliation(s)
- Iris Wechsler
- Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Alexander Wolf
- Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Julian Shanbhag
- Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sigrid Leyendecker
- Institute of Applied Dynamics, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Anne D. Koelewijn
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sandro Wartzack
- Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jörg Miehling
- Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Fox AS. The quest for dynamic consistency: a comparison of OpenSim tools for residual reduction in simulations of human running. ROYAL SOCIETY OPEN SCIENCE 2024; 11:231909. [PMID: 38699555 PMCID: PMC11061640 DOI: 10.1098/rsos.231909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 02/08/2024] [Accepted: 03/19/2024] [Indexed: 05/05/2024]
Abstract
Using synchronous kinematic and kinetic data in simulations of human running typically leads to dynamic inconsistencies. Minimizing residual forces and moments is subsequently important to ensure plausible model outputs. A variety of approaches suitable for residual reduction are available in OpenSim; however, a detailed comparison is yet to be conducted. This study compared OpenSim tools applicable for residual reduction in simulations of human running. Multiple approaches (i.e. Residual Reduction Algorithm, MocoTrack, AddBiomechanics) designed to reduce residual forces and moments were examined using an existing dataset of treadmill running at 5.0 ms-1. The computational time, residual forces and moments, and joint kinematics and kinetics from each approach were compared. A computational cost to residual reduction trade-off was identified, where lower residuals were achieved using approaches with longer computational times. The AddBiomechanics and MocoTrack approaches produced variable lower and upper body kinematics, respectively, versus the remaining approaches. Joint kinetics were similar between approaches; however, MocoTrack generated noisier upper limb joint torque signals. MocoTrack was the best-performing approach for reducing residuals to near-zero levels, at the cost of longer computational times. This study provides OpenSim users with evidence to inform decision-making at the residual reduction step of their workflow.
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Affiliation(s)
- Aaron S. Fox
- School of Exercise and Nutrition Sciences, Deakin University, Victoria3216, Australia
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Dumas R, Jacquelin E. Stiffness of a wobbling mass models analysed by a smooth orthogonal decomposition of the skin movement relative to the underlying bone. J Biomech 2017; 62:47-52. [DOI: 10.1016/j.jbiomech.2017.06.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2017] [Revised: 05/17/2017] [Accepted: 06/05/2017] [Indexed: 10/19/2022]
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