Influence of body segment parameter estimation on calculated ground reaction forces in highly dynamic movements

Fritz, Julian; Kröll, Josef; Schwameder, Hermann

doi:10.1016/j.jbiomech.2018.12.008

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…The deviation between the estimated and measured GRFs in the medial-lateral direction during the simulated jump was large ( Figure 6C ), probably because the GRFs in this direction were relatively small and were covered by errors. Previous studies of Data-driven GRF estimation to the take-off ( Kaps et al, 1997 ; Logar and Munih, 2015 ; Fritz et al, 2019 ; Nam et al, 2023 ) share the same theoretical basis as ours, that is, the inverse dynamics analysis for multibody models. The computational accuracy of this type of method depends on the quality of the raw kinematics data and how well the model is able to track the kinematics.…”

Section: Discussionsupporting

confidence: 64%

“…Measuring devices such as force plate installed on the ski jumping platform or insole pressure sensors were once used, but never popularized due to their high cost ( Virmavirta and Koml, 1993a ; b ; Virmavirta et al, 2001 ). Later studies focused more on data-driven GRF estimation methods ( Fritz et al, 2019 ; Nam et al, 2023 ). Although these studies have revealed the correlation of take-off kinematics and dynamics with athletes’ performance, the activity and function of lower extremity muscle during take-off have not yet been fully elucidated.…”

Section: Introductionmentioning

confidence: 99%

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Musculoskeletal simulation of professional ski jumpers during take-off considering aerodynamic forces

Huang,

Jiang,

Chen

et al. 2023

Front. Bioeng. Biotechnol.

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Introduction: Musculoskeletal simulation has been widely used to analyze athletes’ movements in various competitive sports, but never in ski jumping. Aerodynamic forces during ski jumping take-off have been difficult to account for in dynamic simulation. The purpose of this study was to establish an efficient approach of musculoskeletal simulation of ski jumping take-off considering aerodynamic forces and to analyze the muscle function and activity.Methods: Camera-based marker-less motion capture was implemented to measure the take-off kinematics of eight professional jumpers. A suitable full-body musculoskeletal model was constructed for the simulation. A method based on inverse dynamics iteration was developed and validated to estimate the take-off ground reaction force. The aerodynamic forces, which were calculated based on body kinematics and computational fluid dynamics simulations, were exerted on the musculoskeletal model as external forces. The activation and joint torque contributions of lower extremity muscles were calculated through static optimization.Results: The estimated take-off ground reaction forces show similar trend with the results from past studies. Although overall inconsistencies between simulated muscle activation and EMG from previous studies were observed, it is worth noting that the activation of the tibialis anterior, gluteus maximus, and long head of the biceps femoris was similar to specific EMG results. Among lower extremity extensors, soleus, vastus lateralis, biceps femoris long head, gluteus maximus, and semimembranosus showed high levels of activation and joint extension torque contribution.Discussion: Results of this study advanced the understanding of muscle action during ski jumping take-off. The simulation approach we developed may help guide the physical training of jumpers for improved take-off performance and can also be extended to other phases of ski jumping.

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Section: Discussionsupporting

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Musculoskeletal simulation of professional ski jumpers during take-off considering aerodynamic forces

Huang,

Jiang,

Chen

et al. 2023

Front. Bioeng. Biotechnol.

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“…Some methods investigate if a multimodal motion analysis approach (combination of IMU-sensors with either RGB-video- or depth-camera) enhances tracking performance thus minimizing the kinematic error ( Atrsaei et al, 2016 ; Halilaj et al, 2021 ; Pearl et al, 2023 ). Other methods explicitly aim to generate simulation results without undesired residuals ( Vaughan et al, 1982 ; Koopman et al, 1995 ; Kuo, 1998 ; Cahouët et al, 2002 ; Mazzà and Cappozzo, 2004 ; Riemer et al, 2008 ; Remy and Thelen, 2009 ; Riemer and Hsiao-Wecksler, 2009 ; Jackson et al, 2015 ; Samaan et al, 2016 ; Faber et al, 2018 ; Muller et al, 2018 ; Noamani et al, 2018 ; Fritz et al, 2019 ; Pallarès-López et al, 2019 ; Sturdy et al, 2022 ; Werling et al, 2023 ). Certain methods primarily target a different goal than generating simulation results without residuals.…”

Section: Resultsmentioning

confidence: 99%

Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results—a systematic review

Wechsler,

Wolf,

Shanbhag

et al. 2024

Front. Bioeng. Biotechnol.

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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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“…The calculations of moment arm and angular momentum relied on accurate estimation of CoM. Because CoM calculations depended partially on literature data (de Leva 1996), systematic errors may have been introduced (Fritz et al 2019). The impact of this for moment arm and related variables was accounted for by CoM-CoP alignment corrections (see Methods).…”

Section: Methodological Considerationsmentioning

confidence: 99%

Imitation jumps in ski jumping: Technical execution and relationship to performance level

Ettema¹,

Braaten

Danielsen³

et al. 2020

Journal of Sports Sciences

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Imitation jumps are frequently used in training for ski jumping. Yet, the dynamics of these jumps differ considerably. Thus, the relevance of imitation jumps for ski jumping performance is not elucidated. The aim of this study was to investigate the relationship between the technical execution of imitation jumps and ski jumping performance levels. We compared the imitation jumps of 11 ski jumpers of different performance level using a Spearman correlation transform of time traces of the kinetics (measured using force cells and motion capture) of imitation jumps. The kinetic aspects that were related to performance centred on the moment arm of ground reaction force to the centre of mass before the onset of the push-off, angular momentum early in push-off, thigh angle during the main period of push-off and vertical velocity towards the end of push-off. We propose that the thigh angle may be a key element allowing high development of linear momentum, while preparing for appropriate aerodynamic position. Furthermore, the findings suggest that the kinetic development prior to (and during) push-off is more important than the kinematic end state at take-off.

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Influence of body segment parameter estimation on calculated ground reaction forces in highly dynamic movements

Cited by 8 publications

References 28 publications

Musculoskeletal simulation of professional ski jumpers during take-off considering aerodynamic forces

Musculoskeletal simulation of professional ski jumpers during take-off considering aerodynamic forces

Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results—a systematic review

Imitation jumps in ski jumping: Technical execution and relationship to performance level

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