Relationships of 35 lower limb muscles to height and body mass quantified using MRI

Handsfield, Geoffrey G.; Meyer, Craig H.; Hart, Joseph M.; Abel, Mark F.; Blemker, Silvia S.

doi:10.1016/j.jbiomech.2013.12.002

Cited by 292 publications

(347 citation statements)

References 51 publications

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“…F max was calculated based on the muscle's scaled volume V m estimated using the regression equations in Handsfield et al (2014), and optimal fibre length l f,opt estimated from a subject-specific scaled musculoskeletal model (OpenSim v3.3;Delp et al, 2007;Arnold et al, 2010;Dick et al, 2016). σ 0 is estimated specific muscle stress (225 kPa; Spector et al, 1980;Roy et al, 1982) ( Table S1).…”

Section: Muscle Models For Estimating Lg and Mg Forcesmentioning

confidence: 99%

“…Gerus et al, 2012;Gerus et al, 2015), rather than being experimentally guided; however, this was not the purpose of this study. Additionally, the overall accuracy of the muscle model is scaled by F max (Eqn 3), that was determined from both an optimal fibre length and an estimated muscle volume that were derived from regression equations from other studies (Delp et al, 2007;Handsfield et al, 2014).…”

Section: Differences Between Muscles and Pedalling Conditionsmentioning

confidence: 99%

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Comparison of human gastrocnemius forces predicted by Hill-type muscle models and estimated from ultrasound images

Dick

Biewener

Wakeling

2017

Journal of Experimental Biology

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Hill-type models are ubiquitous in the field of biomechanics, providing estimates of a muscle's force as a function of its activation state and its assumed force-length and force-velocity properties. However, despite their routine use, the accuracy with which Hill-type models predict the forces generated by muscles during submaximal, dynamic tasks remains largely unknown. This study compared human gastrocnemius forces predicted by Hill-type models with the forces estimated from ultrasound-based measures of tendon length changes and stiffness during cycling, over a range of loads and cadences. We tested both a traditional model, with one contractile element, and a differential model, with two contractile elements that accounted for independent contributions of slow and fast muscle fibres. Both models were driven by subject-specific, ultrasoundbased measures of fascicle lengths, velocities and pennation angles and by activation patterns of slow and fast muscle fibres derived from surface electromyographic recordings. The models predicted, on average, 54% of the time-varying gastrocnemius forces estimated from the ultrasound-based methods. However, differences between predicted and estimated forces were smaller under low speed-high activation conditions, with models able to predict nearly 80% of the gastrocnemius force over a complete pedal cycle. Additionally, the predictions from the Hill-type muscle models tested here showed that a similar pattern of force production could be achieved for most conditions with and without accounting for the independent contributions of different muscle fibre types.

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Section: Muscle Models For Estimating Lg and Mg Forcesmentioning

confidence: 99%

Section: Differences Between Muscles and Pedalling Conditionsmentioning

confidence: 99%

Comparison of human gastrocnemius forces predicted by Hill-type muscle models and estimated from ultrasound images

Dick

Biewener

Wakeling

2017

Journal of Experimental Biology

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“…Furthermore, the musculoskeletal model includes a limited number of bundles to represent complex threedimensional muscle geometries, which results in different total lengths l ୫୲ between the measured and modeled musculotendon fibers; an average difference of 3.3 (± 3.1) cm was found in the specimen pose, due in part to the measurements being taken on the surface of the muscle bellies (Moiseev et al, 2008) and in part to errors introduced by straight line muscle representation (Jensen and Davy, 1975) One of the aims of the presented methodology was to offer an appropriate estimation of l ୭ ୫ and l ୱ ୲ in subject specific models built from medical images to enable muscle-actuated simulations, as the other muscle parameters can already be identified. Pennation angle can be taken from the literature as it has small influence on muscle force (Zajac, 1989), v ୫ୟ୶ is generally assumed equal to 10 l ୭ ୫ /s (Zajac, 1989) and F ୧ୱ୭ ୫ is proportional to the physiological cross sectional area (PCSA), calculated as PCSA = (Vol * cos α ୭ )/l ୭ ୫ (Ward et al, 2009), where Vol is the muscle volume obtained through segmentation for clinical populations (Hainisch et al, 2012) or using regression equations for young healthy subjects (Handsfield et al, 2014). Further work is necessary to assess the proposed algorithm when applied on muscles crossing other joints, to investigate its potential use in combination with experimental measurements of muscle architecture parameters, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…One of the reasons is that while personalized bone geometries and joint parameters can be obtained from segmented medical images, musculotendon parameters currently cannot be easily measured or estimated, although improvements in imaging and segmentation technology might change the situation in the future (Blemker et al, 2007). For example, if a Hill-type muscle model as proposed by Zajac (1989) is adopted, the maximum isometric force ‫ܨ‬ ௦ can be calculated from muscle volumes (Hainisch et al, 2012;Handsfield et al, 2014), but optimal fiber length ݈ and tendon slack length ݈ ௦ ௧ are parameters that are difficult to estimate and highly influence the model force outputs (Scovil and Ronsky, 2006;Redl et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Estimation of musculotendon parameters for scaled and subject specific musculoskeletal models using an optimization technique

Modenese

Ceseracciu

Reggiani

et al. 2016

Journal of Biomechanics

148

112

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“…The hip-joint contact force was calculated as follows: (9) (10) parameters (the objective function, physiological cross-sectional area, force-length relation, and muscle moment arm length). Therefore, we also conducted a sensitivity analysis by changing four parameters, including the objective function (two options) and the physiological cross-sectional area (three options [24,27,32].…”

Section: Calculation Of Hip-joint Contact Forcementioning

confidence: 99%

Relationship between Hip Flexion Contracture and Hip-Joint Contact Force in Standing Posture: A Computer Simulation Study

Inai¹,

Edama²,

Takabayashi³

et al. 2017

J Ergonomics

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Mechanical stress on articular cartilage and long-duration standing postures are risk factors for hip osteoarthritis progression. This study aims to examine the relationship between hip flexion contracture and the hip-joint contact force in standing postures using computer simulation. A musculoskeletal model composed of seven segments (Head, Arms, and Trunk (HAT) and thighs, shanks, and two feet) was created. Various standing postures (708 variations) were generated, and five hip flexion contracture conditions were set: zero contracture and flexions of 0°, 10°, 20°, and 30°. A standing posture satisfying the hip flexion contracture condition with the minimum sum of the muscle activations was obtained as the optimal standing posture, and the hip-joint contact force in the optimal standing posture was calculated. A sensitivity analysis was conducted by varying four parameters (the objective function, physiological cross-sectional area, force-length relation, and muscle moment arm length). The hip-joint contact force and hip extensor muscle forces (i.e., those of the gluteus maximus, semitendinosus, semimembranosus, and biceps femoris long head) during standing increased with the development of hip flexion contracture. The hip-joint contact force for the standing posture with a 30° hip flexion contracture was almost twice that for the no-contracture condition (8.7 and 3.7 N/kg, respectively). The sensitivity analysis showed that variation of the four parameters did not affect our main finding. The main finding of this study is that hip-joint contact force during standing increases with the development of hip flexion contracture. The findings of this study may help to prevent hip osteoarthritis progression.

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Relationships of 35 lower limb muscles to height and body mass quantified using MRI

Cited by 292 publications

References 51 publications

Comparison of human gastrocnemius forces predicted by Hill-type muscle models and estimated from ultrasound images

Comparison of human gastrocnemius forces predicted by Hill-type muscle models and estimated from ultrasound images

Estimation of musculotendon parameters for scaled and subject specific musculoskeletal models using an optimization technique

Relationship between Hip Flexion Contracture and Hip-Joint Contact Force in Standing Posture: A Computer Simulation Study

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