Biomechanics and muscle coordination of human walking

Zajac, Felix E.; Neptune, Richard R.; Kautz, Steven A.

doi:10.1016/s0966-6362(02)00068-1

Cited by 485 publications

(130 citation statements)

References 109 publications

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“…Several reference charts for muscle activity during gait have been published [1,[25][26][27][28][29][30]. In these the starting and stopping times of SEMG for lower extremity were variable and did not always agree.…”

Section: Discussionmentioning

confidence: 99%

Identification and characterisation of heel strike transient

et al. 2006

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Section: Discussionmentioning

confidence: 99%

Identification and characterisation of heel strike transient

et al. 2006

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“…3,33,43,52 Because these simulations often combine human movement data measured in the laboratory with mathematical models of the musculoskeletal system, accurate estimations of biomechanical outputs such as intersegmental joint loads, muscle activation/coordination, and muscle force are possible. 18 The experimental methods used to create anatomic detail in musculoskeletal models have improved over the past decade through direct measurement of sarcomere length 26 and increased cadaveric sample sizes, 48 which has led to enhanced accuracy of simulations specific to individual patients.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Approach to Quantify the Impact of Uncertainty Propagation in Musculoskeletal Simulations

et al. 2014

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Uncertainty that arises from measurement error and parameter estimation can significantly affect the interpretation of musculoskeletal simulations; however, these effects are rarely addressed. The objective of this study was to develop an open-source probabilistic musculoskeletal modeling framework to assess how measurement error and parameter uncertainty propagate through a gait simulation. A baseline gait simulation was performed for a male subject using OpenSim for three stages: inverse kinematics, inverse dynamics, and muscle force prediction. A series of Monte Carlo simulations were performed that considered intrarater variability in marker placement, movement artifacts in each phase of gait, variability in body segment parameters, and variability in muscle parameters calculated from cadaveric investigations. Propagation of uncertainty was performed by also using the output distributions from one stage as input distributions to subsequent stages. Confidence bounds (5–95%) and sensitivity of outputs to model input parameters were calculated throughout the gait cycle. The combined impact of uncertainty resulted in mean bounds that ranged from 2.7° to 6.4° in joint kinematics, 2.7 to 8.1 N m in joint moments, and 35.8 to 130.8 N in muscle forces. The impact of movement artifact was 1.8 times larger than any other propagated source. Sensitivity to specific body segment parameters and muscle parameters were linked to where in the gait cycle they were calculated. We anticipate that through the increased use of probabilistic tools, researchers will better understand the strengths and limitations of their musculoskeletal simulations and more effectively use simulations to evaluate hypotheses and inform clinical decisions.

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“…The relationships between gait kinematics and EMG patterns have previously been estimated by muscle-based simulations of dynamic walking (Zajac et al. 2002, 2003). Moving further, a better comprehension of the relationships which exist between the neuro-musculo-skeletal system would significantly advance the understanding of locomotor control.…”

Section: Introductionmentioning

confidence: 99%

Bipedal robotic walking control derived from analysis of human locomotion

et al. 2018

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This paper proposes the design of a bipedal robotic controller where the function between the sensory input and motor output is treated as a black box derived from human data. In order to achieve this, we investigated the causal relationship between ground contact information from the feet and leg muscle activity n human walking and calculated filter functions which transform sensory signals to motor actions. A minimal, nonlinear, and robust control system was created and subsequently analysed by applying it to our bipedal robot RunBot III without any central pattern generators or precise trajectory control. The results demonstrate that our controller can generate stable robotic walking. This indicates that complex locomotion patterns can result from a simple model based on reflexes and supports the premise that human-derived control strategies have potential applications in robotics or assistive devices.Electronic supplementary materialThe online version of this article (10.1007/s00422-018-0750-5) contains supplementary material, which is available to authorized users.

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Biomechanics and muscle coordination of human walking

Cited by 485 publications

References 109 publications

Identification and characterisation of heel strike transient

Identification and characterisation of heel strike transient

A Probabilistic Approach to Quantify the Impact of Uncertainty Propagation in Musculoskeletal Simulations

Bipedal robotic walking control derived from analysis of human locomotion

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