Subject-specific geometrical detail rather than cost function formulation affects hip loading calculation

Wesseling, Mariska; Groote, Friedl De; Bosmans, Lode; Bartels, Ward; Meyer, Christophe; Desloovere, Kaat

doi:10.1080/10255842.2016.1154547

Cited by 41 publications

(47 citation statements)

References 52 publications

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“…This may have important implications for people with hip OA, who often exhibit hip muscle weakness (Loureiro et al, 2013) and altered hip muscle activation patterns (Rutherford et al, 2015;Sims et al, 2002). Wesseling et al (2016) demonstrated that HJCF minimisation had no effect on overall prediction of HJCF. However, their HJCF minimisation was performed within the static optimisation criteria, thus influencing the estimation of muscle activation rather than neuromuscular parameters of the NMS model.…”

Section: Discussionmentioning

confidence: 97%

“…This could introduce errors in the muscle lines of action and moment arms, which in turn affect the resulting HJCF. Though this is commonly done in NMS modelling (Graham et al, 2016;Kainz et al, 2016;Steele et al, 2012), future investigation should include imaging data to create models with subjectspecific geometries (Gerus et al, 2013;Wesseling et al, 2016) and joint kinematics (Brito da Luz et al, 2017). Subject-specific imaging data will also improve hip joint centre location calculations, an important parameter in estimating HJCF (Lenaerts et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

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Subject-specific calibration of neuromuscular parameters enables neuromusculoskeletal models to estimate physiologically plausible hip joint contact forces in healthy adults

Hoang

Pizzolato

Diamond

et al. 2018

Journal of Biomechanics

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In-vivo hip joint contact forces (HJCF) can be estimated using computational neuromusculoskeletal (NMS) modelling. However, different neural solutions can result in different HJCF estimations. NMS model predictions are also influenced by the selection of neuromuscular parameters, which are either based on cadaveric data or calibrated to the individual. To date, the best combination of neural solution and parameter calibration to obtain plausible estimations of HJCF have not been identified. The aim of this study was to determine the effect of three electromyography (EMG)-informed neural solution modes (EMG-driven, EMG-hybrid, and EMG-assisted) and static optimisation, each using three different parameter calibrations (uncalibrated, minimise joint moments error, and minimise joint moments error and peak HJCF), on the estimation of HJCF in a healthy population (n = 23) during walking. When compared to existing in-vivo data, the EMG-assisted mode and static optimisation produced the most physiologically plausible HJCF when using a NMS model calibrated to minimise joint moments error and peak HJCF. EMG-assisted mode produced first and second peaks of 3.55 times body weight (BW) and 3.97 BW during walking; static optimisation produced 3.75 BW and 4.19 BW, respectively. However, compared to static optimisation, EMG-assisted mode generated muscle excitations closer to recorded EMG signals (average across hip muscles R = 0.60 ± 0.37 versus R = 0.12 ± 0.14). Findings suggest that the EMG-assisted mode combined with minimise joint moments error and peak HJCF calibration is preferable for the estimation of HJCF and generation of realistic load distribution across muscles.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Subject-specific calibration of neuromuscular parameters enables neuromusculoskeletal models to estimate physiologically plausible hip joint contact forces in healthy adults

Hoang

Pizzolato

Diamond

et al. 2018

Journal of Biomechanics

View full text Add to dashboard Cite

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“…Some limitations of this study should be considered. Analyses were performed using a linearly-scaled generic musculoskeletal model, which does not account for subject-specific muscle pathways and moment arms, influential factors in estimating JCF's (Lenaerts et al, 2008;Wesseling et al, 2016a). Variation in calculated hip joint centre locations can influence HJCF estimations (Lenaerts et al, 2008;Stagni et al, 2000).…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, generic musculoskeletal models remain widely used due to their simplicity and availability (Graham et al, 2016;Kainz et al, 2016;Steele et al, 2012). Future investigation should consider creating musculoskeletal models with subject-specific geometries (Gerus et al, 2013;Wesseling et al, 2016a) and joint kinematics (Brito da Luz et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

A calibrated EMG-informed neuromusculoskeletal model can appropriately account for muscle co-contraction in the estimation of hip joint contact forces in people with hip osteoarthritis

Hoang

Diamond

Lloyd

et al. 2019

Journal of Biomechanics

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Abnormal hip joint contact forces (HJCF) are considered a primary mechanical contributor to the progression of hip osteoarthritis (OA). Compared to healthy controls, people with hip OA often present with altered muscle activation patterns and greater muscle cocontraction, both of which can influence HJCF. Neuromusculoskeletal (NMS) modelling is non-invasive approach to estimating HJCF, whereby different neural control solutions can be used to estimate muscle forces. Static optimisation, available within the popular NMS modelling software OpenSim, is a commonly used neural control solution, but may not account for an individual's unique muscle activation patterns and/or co-contraction that are often evident in pathological population. Alternatively, electromyography (EMG)-assisted neural control solutions, available within CEINMS software, have been shown to account for individual activation patterns in healthy people. Nonetheless, their application in people with hip OA, with conceivably greater levels of co-contraction, is yet to be explored. The aim of this study was to compare HJCF estimations using static optimisation (in OpenSim) and EMG-assisted (in CEINMS) neural control solutions during walking in people with hip OA. EMG-assisted neural control solution was more consistent with both EMG and joint moment data than static optimisation, and also predicted significantly higher HJCF peaks (p<0.001). The EMG-assisted neural control solution also accounted for more muscle co-contraction than static optimisation (p=0.03), which probably contributed to these higher HJCF peaks. Findings suggest that the EMG-assisted neural control solution may estimate more physiologically plausible HJCF than static optimisation in a population with high levels of cocontraction, such as hip OA.

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“…One of the major factors that could affect the prediction of joint and muscle forces is the accuracy of the geometrical representation of the lower-limb muscles [ 22 – 26 ]. In particular, musculoskeletal models were previously reported to be sensitive to errors in the insertion, intermediate, and origin points of the muscles [ 24 , 25 ], with the muscles spanning the hip joint causing the highest uncertainty in the prediction of muscle [ 25 ] and contact forces [ 26 ]. A recent cadaveric dataset based on medical imaging data, TLEM 2.0 [ 27 ], provides muscular geometrical information with the highest level of detail currently available; however, this data has not yet been adopted for musculoskeletal applications focusing on the hip joint.…”

Section: Introductionmentioning

confidence: 99%

Refining muscle geometry and wrapping in the TLEM 2 model for improved hip contact force prediction

et al. 2018

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Musculoskeletal models represent a powerful tool to gain knowledge on the internal forces acting at the joint level in a non-invasive way. However, these models can present some errors associated with the level of detail in their geometrical representation. For this reason, a thorough validation is necessary to prove the reliability of their predictions. This study documents the development of a generic musculoskeletal model and proposes a working logic and simulation techniques for identifying specific model features in need of refinement; as well as providing a quantitative validation for the prediction of hip contact forces (HCF). The model, implemented in the AnyBody Modeling System and based on the cadaveric dataset TLEM 2.0, was scaled to match the anthropometry of a patient fitted with an instrumented hip implant and to reproduce gait kinematics based on motion capture data. The relative contribution of individual muscle elements to the HCF and joint moments was analyzed to identify critical geometries, which were then compared to muscle magnetic resonance imaging (MRI) scans and, in case of inconsistencies, were modified to better match the volumetric scans. The predicted HCF showed good agreement with the overall trend and timing of the measured HCF from the instrumented prosthesis. The average root mean square error (RMSE), calculated for the total HCF was found to be 0.298*BW. Refining the geometries of the muscles thus identified reduced RMSE on HCF magnitudes by 17% (from 0.359*BW to 0.298*BW) over the whole gait cycle. The detailed study of individual muscle contributions to the HCF succeeded in identifying muscles with incorrect anatomy, which would have been difficult to intuitively identify otherwise. Despite a certain residual over-prediction of the final hip contact forces in the stance phase, a satisfactory level of geometrical accuracy of muscle paths has been achieved with the refinement of this model.

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Subject-specific geometrical detail rather than cost function formulation affects hip loading calculation

Cited by 41 publications

References 52 publications

Subject-specific calibration of neuromuscular parameters enables neuromusculoskeletal models to estimate physiologically plausible hip joint contact forces in healthy adults

Subject-specific calibration of neuromuscular parameters enables neuromusculoskeletal models to estimate physiologically plausible hip joint contact forces in healthy adults

A calibrated EMG-informed neuromusculoskeletal model can appropriately account for muscle co-contraction in the estimation of hip joint contact forces in people with hip osteoarthritis

Refining muscle geometry and wrapping in the TLEM 2 model for improved hip contact force prediction

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