Human ankle plantar flexor muscle–tendon mechanics and energetics during maximum acceleration sprinting

Existing “off-the-shelf” musculoskeletal models are problematic when simulating movements that involve substantial hip and knee flexion, such as the upstroke of pedalling, because they tend to generate excessive passive fibre force. The goal of this study was to develop a refined musculoskeletal model capable of simulating pedalling and fast running, in addition to walking, which predicts the activation patterns of muscles better than existing models. Specifically, we tested whether the anomalous co-activation of antagonist muscles, commonly observed in simulations, could be resolved if the passive forces generated by the underlying model were diminished. We refined the OpenSim™ model published by Rajagopal et al. (2016) by increasing the model's range of knee flexion, updating the paths of the knee muscles, and modifying the force-generating properties of eleven muscles. Simulations of pedalling, running and walking based on this model reproduced measured EMG activity better than simulations based on the existing model – even when both models tracked the same subject-specific kinematics. Improvements in the predicted activations were associated with decreases in the net passive moments; for example, the net passive knee moment during the upstroke of pedalling decreased from 36.9 N m (existing model) to 6.3 N m (refined model), resulting in a dramatic decrease in the co-activation of knee flexors. The refined model is available from SimTK.org and is suitable for analysing movements with up to 120° of hip flexion and 140° of knee flexion.

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“…We set the maximum shortening velocity of each MTU to 15 l o m s -1 consistent with previous simulations of walking and running (e.g. 1,19 ).…”

Section: Methodsmentioning

confidence: 99%

Why are Antagonist Muscles Co-activated in My Simulation? A Musculoskeletal Model for Analysing Human Locomotor Tasks

2017

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“…Muscle lengths of the soleus and medial gastrocnemius were calculated with the Muscle Analysis tool in OpenSim. We normalized muscle and ligament lengths by each structure's neutral length, and then calculated strains of ligaments and muscles with the following Equation

ε (%) = \frac{l - l_{n}}{l_{n}} \times 100

…”

Section: Methodsmentioning

confidence: 99%

“…where l n is the neutral length of a ligament and muscle in the neutral standing position, l is the length, and ε is the strain at each instantaneous point along the respective time‐series data. Ankle kinematics and ligament and muscle strains of both left and right legs were time‐normalized and interpolated to 0–100% of stance phase …”

Section: Methodsmentioning

confidence: 99%

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Number of Segments Within Musculoskeletal Foot Models Influences Ankle Kinematics and Strains of Ligaments and Muscles

Kim

Kipp

2019

Journal Orthopaedic Research

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Multi‐segment foot models (MFMs) are becoming a common tool in musculoskeletal research on the ankle‐foot complex. The purpose of this study was to compare ankle joint kinematics as well as ligament and muscle strains that result from MFM with a different number of segments during vertical hopping. Ten participants were recruited and performed double‐limb vertical hops. Marker positions and ground reaction forces were collected. Two‐segment (2MFM), three‐segment (3MFM), and five‐segment MFM (5MFM) were used to calculate ankle kinematics and the strains of the anterior talofibular and calcaneofibular ligaments and of the soleus and gastrocnemius muscles. Ranges of motion and peak strains were analyzed with Kruskal–Wallis and post hoc tests, whereas the time‐series of the ankle kinematics and ligament and muscle strains were analyzed with statistical parametric mapping. There were significant main effects for MFM in the talocrural joint range of motion and peak strains of ligaments and muscles. In addition, there were significant main effects for MFM in time‐series data of the talocrural joint angle as well as for ligament and muscle strains. In all cases, the post hoc analyses showed that the 2MFM consistently overestimated the range of motion and tissue strains compared to the 3MFM and 5MFM, while 3MFM and 5MFM did not differ from each other in the most variables. This study showed that the number of segments in MFM significantly affects the biomechanical estimates of joint kinematics and tissue strains during hopping. Clinical significance: MFM that combine all foot structures beyond the talus into one segment likely overestimate ankle joint biomechanics. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2231–2240, 2019

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“…Thus, the 197 underlying control strategies could be adjusted from prioritising positional control to a strategy for high 198 force output. 199While the predicted muscle fibre lengths in more distal limb muscles can be compared with in-vivo 200 measurements during locomotion(Lai et al, 2016), validation of our muscle-index approach is limited 201 by the lack of equivalent in-vivo measurements in more proximal limb muscles such as the GMAX, 202 which limits evaluation of the predicted function of muscle fibres in these muscles and the decoupling 203 effects of tendon compliance. We performed a post-hoc sensitivity analysis by varying the compliance 204 of all muscles to 8% at Fo m for walking and running simulations of one participant and observed minimal 205 change in muscle fibre length, mechanical work, and functional behaviours of the MTU and muscle 206 fibres.…”

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confidence: 99%

Muscle-specific indices to characterise the functional behaviour of human lower-limb muscles during locomotion

Lai

Biewener

Wakeling

2019

Journal of Biomechanics

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27The mechanical output of a muscle may be characterised by having distinct functional behaviours, 28 which can shift to satisfy the varying demands of movement, and may vary relative to a proximo-distal 29 gradient in the muscle-tendon architecture (MTU) among lower-limb muscles in humans and other 30 terrestrial vertebrates. We adapted a previous joint-level approach to develop a muscle-specific index-31 based approach to characterise the functional behaviours of human lower-limb muscles during 32 movement tasks. Using muscle mechanical power and work outputs derived from experimental data 33 and computational simulations of human walking and running, our index-based approach differentiated 34 known distinct functional behaviours with varying mechanical demands, such as greater spring-like 35 function during running compared with walking; with anatomical location, such as greater motor-like 36 function in proximal compared with the distal lower-limb muscles; and with MTU architecture, such as 37 greater strut-like muscles fibre function compared with the MTU in the ankle plantarflexors. The 38 functional indices developed in this study provide distinct quantitative measures of muscle function in 39 the human lower-limb muscles during dynamic movement tasks, which may be beneficial towards 40 tuning the design and control strategies of physiologically-inspired robotic and assistive devices. 41 42 Lai et al. 3

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Human ankle plantar flexor muscle–tendon mechanics and energetics during maximum acceleration sprinting

Cited by 39 publications

References 65 publications

Why are Antagonist Muscles Co-activated in My Simulation? A Musculoskeletal Model for Analysing Human Locomotor Tasks

Why are Antagonist Muscles Co-activated in My Simulation? A Musculoskeletal Model for Analysing Human Locomotor Tasks

Number of Segments Within Musculoskeletal Foot Models Influences Ankle Kinematics and Strains of Ligaments and Muscles

Muscle-specific indices to characterise the functional behaviour of human lower-limb muscles during locomotion

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