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Why does the metabolic cost of walking increase on compliant substrates?

ABSTRACT: Walking on compliant substrates requires more energy than walking on hard substrates but the biomechanical factors that contribute to this increase are debated. Previous studies suggest various causative mechanical factors, including disruption to pendular energy recovery, increased muscle work, decreased muscle efficiency and increased gait variability. We test each of these hypotheses simultaneously by collecting a large kinematic and kinetic dataset of human walking on foams of differing thickness. This allowed us to systematically characterize changes in gait with substrate compliance, and, by combining data with mechanical substrate testing, drive the very first subject-specific computer simulations of human locomotion on compliant substrates to estimate the internal kinetic demands on the musculoskeletal system. Negative changes to pendular energy exchange or ankle mechanics are not supported by our analyses. Instead we find that the mechanistic causes of increased energetic costs on compliant substrates are more complex than captured by any single previous hypothesis. We present a model in which elevated activity and mechanical work by muscles crossing the hip and knee are required to support the changes in joint (greater excursion and maximum flexion) and spatio-temporal kinematics (longer stride lengths, stride times and stance times, and duty factors) on compliant substrates.

SUBMITTER: Grant B

PROVIDER: S-EPMC9709563 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Why does the metabolic cost of walking increase on compliant substrates?

Grant Barbara B Charles James J Geraghty Brendan B Gardiner James J D'Août Kristiaan K Falkingham Peter L PL Bates Karl T KT

Journal of the Royal Society, Interface 20221130 196

Walking on compliant substrates requires more energy than walking on hard substrates but the biomechanical factors that contribute to this increase are debated. Previous studies suggest various causative mechanical factors, including disruption to pendular energy recovery, increased muscle work, decreased muscle efficiency and increased gait variability. We test each of these hypotheses simultaneously by collecting a large kinematic and kinetic dataset of human walking on foams of differing thic ...[more]

PMID: 36448287

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Project description:Assessment of metabolic 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 walking 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 electromyographic (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 CoT at different gait speeds using three metabolic cost models (Umberger et al., 2003; Bhargava et al., 2004; Umberger, 2010). The calculated CoT values were compared with published CoT data as a function of walking speed, step length asymmetry, stance time asymmetry, double support time asymmetry, and severity of motor impairment (i.e., Fugl-Meyer score). Overall, only SOCal and EMGCal with the Bhargava metabolic cost model were able to reproduce accurately published experimental trends between CoT and various clinical measures of walking asymmetry post-stroke. Tuning of the parameters in the different metabolic cost models could potentially resolve the observed CoT magnitude differences between model predictions and experimental measurements. Realistic CoT predictions may allow researchers to predict human performance, surgical outcomes, and rehabilitation outcomes reliably using computational simulations.

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Why does the metabolic cost of walking increase on compliant substrates?

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Why does the metabolic cost of walking increase on compliant substrates?

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