Metabolic cost calculations of gait using musculoskeletal energy models, a comparison study

Koelewijn, Anne D.; Heinrich, D.; Bogert, Antonie J. van den

doi:10.1371/journal.pone.0222037

Cited by 60 publications

(68 citation statements)

References 45 publications

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“…Although all modeling combinations produced the positive or negative trends relative to published data, not all of them resulted in slopes that were statistically similar to those found in the literature. This finding is similar to a recent study by Koelwijn et al (Koelewijn et al, 2019), where several metabolic cost models (including U03 and B04) were shown to predict the correct trends for CoT as a function of walking slope or gait speed in accordance to trends found in the literature (Margaria, 1968). Additionally, Koelwijn et al (Koelewijn et al, 2019) found that B04 tended to underestimate CoT values in comparison to experimental values, which is in agreement with our findings.…”

Section: Discussionsupporting

confidence: 93%

“…Bhargava's model needed muscle specific tension to be around 37 N/cm 2 rather than 61 N/cm 2 to match experimental averages the best. This new value is closer to values used within OpenSim (Delp et al, 2007) and studies such as (Miller, 2014;Koelewijn et al, 2019). Future work to optimize muscle specific tension could improve the magnitude of CoT predictions (Hamner and Delp, 2013;Uchida et al, 2016a;Falisse et al, 2019).…”

Section: Discussionmentioning

confidence: 60%

“…With this change, the resulting CoT estimates fit within one standard deviation of the average reported by Finley and Bastian, 2017. Inversely, to compensate for CoT underestimation by the Bhargava model, muscle specific tension had to be decreased to 37 N/cm 2 for the resulting CoT estimates to lie within the range of CoT values published by (Miller, 2014;Koelewijn et al, 2019).…”

Section: Resultsmentioning

confidence: 73%

“…By predicting the factors, designs, or movements that minimize metabolic energy expenditure, these studies highlight the potential benefits of combining musculoskeletal and metabolic cost models. However, for these predictions to become realistic for practical applications, the accuracy of these combined models needs to improve (Miller, 2014), (Koelewijn et al, 2019).…”

Section: Model Personalization Affects Metabolic Cost?mentioning

confidence: 99%

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Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking

Arones

Shourijeh

Patten

et al. 2020

Preprint

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Assessment of metabolic energy cost as a metric for human performance has expanded across various fields within the scientific, clinical, and engineering communities. As an alternative to measuring metabolic cost experimentally, musculoskeletal models incorporating metabolic cost models have been developed. However, to utilize these models for practical applications, the accuracy of their metabolic cost predictions requires improvement. Previous studies have reported the benefits of using personalized musculoskeletal models for various applications, yet no study has evaluated how model personalization affects metabolic cost estimation. This study investigated the effect of musculoskeletal model personalization on estimates of metabolic cost of transport (CoT) during post-stroke walking using three commonly used metabolic cost models. We analyzed data previously collected from two male stroke survivors with right-sided hemiparesis. The three metabolic cost models were implemented within three musculoskeletal modeling approaches involving different levels of personalization. The first approach used a scaled generic OpenSim model and found muscle activations via static optimization (SOGen). The second approach used a personalized EMG-driven musculoskeletal model with personalized functional axes but found muscle activations via static optimization (SOCal). The third approach used the same personalized EMG-driven model but calculated muscle activations directly from EMG data (EMGCal). For each approach, the muscle activation estimates were used to calculate each subject’s cost of transport (CoT) at different gait speeds using three metabolic cost models (Umberger 2003, Umberger 2010, and Bhargava 2004). The calculated CoT values were compared with published CoT trends as a function of stance time, double support time, step positions, walking speed, and severity of motor impairment (i.e., Fugl-Meyer score). Overall, U10-SOCal, U10-EMGCal, U03-SOCal, and U03-EMGCal were able to produce slopes between CoT and the different measures of walking asymmetry that were statistically similar to those found in the literature. Although model personalization seemed to improve CoT estimates, further tuning of parameters associated with the different metabolic cost models in future studies may allow for realistic CoT predictions. An improvement in CoT predictions may allow researchers to predict human performance, surgical, and rehabilitation outcomes reliably using computational simulations.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 60%

Section: Resultsmentioning

confidence: 73%

Section: Model Personalization Affects Metabolic Cost?mentioning

confidence: 99%

See 2 more Smart Citations

Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking

Arones

Shourijeh

Patten

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…2-b). Hill-type muscle models can be used with models of metabolic energy consumption [21,22,23] and muscle fatigue [24,25,26] to estimate these quantities in simulations. Musculoskeletal parameter values are determined for average humans based on measurements from a large number of people and cadavers [27,28,29,30] and can be customized to match an individual's height, weight, or CT and MRI scan data [31,32].…”

Section: Musculoskeletal Simulationsmentioning

confidence: 99%

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Song

Kidziński

Peng

et al. 2020

Preprint

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Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Despite advances in neuroscience techniques, it is still difficult to measure and interpret the activity of the millions of neurons involved in motor control. Thus, researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This “Learn to Move” competition, which we have run annually since 2017 at the NeurIPS conference, has attracted over 1300 teams from around the world. Top teams adapted state-of-art deep reinforcement learning techniques to produce complex motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research.

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A Review in Biomechanics Modeling

Let

Filip

Let

et al. 2020

Proceedings of the International Conference of Mechatronics and Cyber- MixMechatronics - 2020

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Metabolic cost calculations of gait using musculoskeletal energy models, a comparison study

Cited by 60 publications

References 45 publications

Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking

Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

A Review in Biomechanics Modeling

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