Literature DB >> 10412400

Evaluation of performance criteria for simulation of submaximal steady-state cycling using a forward dynamic model.

R R Neptune1, M L Hull.   

Abstract

The objectives of this study were twofold. The first was to develop a forward dynamic model of cycling and an optimization framework to simulate pedaling during submaximal steady-state cycling conditions. The second was to use the model and framework to identify the kinetic, kinematic, and muscle timing quantities that should be included in a performance criterion to reproduce natural pedaling mechanics best during these pedaling conditions. To make this identification, kinetic and kinematic data were collected from 6 subjects who pedaled at 90 rpm and 225 W. Intersegmental joint moments were computed using an inverse dynamics technique and the muscle excitation onset and offset were taken from electromyographic (EMG) data collected previously (Neptune et al., 1997). Average cycles and their standard deviations for the various quantities were used to describe normal pedaling mechanics. The model of the bicycle-rider system was driven by 15 muscle actuators per leg. The optimization framework determined both the timing and magnitude of the muscle excitations to simulate pedaling at 90 rpm and 225 W. Using the model and optimization framework, seven performance criteria were evaluated. The criterion that included all of the kinematic and kinetic quantities combined with the EMG timing was the most successful in replicating the experimental data. The close agreement between the simulation results and the experimentally collected kinetic, kinematic, and EMG data gives confidence in the model to investigate individual muscle coordination during submaximal steady-state pedaling conditions from a theoretical perspective, which to date has only been performed experimentally.

Mesh:

Year:  1998        PMID: 10412400     DOI: 10.1115/1.2797999

Source DB:  PubMed          Journal:  J Biomech Eng        ISSN: 0148-0731            Impact factor:   2.097


  17 in total

1.  Electromyographic analysis of hip adductor muscles during incremental fatiguing pedaling exercise.

Authors:  Kohei Watanabe; Keisho Katayama; Koji Ishida; Hiroshi Akima
Journal:  Eur J Appl Physiol       Date:  2009-05-24       Impact factor: 3.078

2.  The relationships between muscle, external, internal and joint mechanical work during normal walking.

Authors:  Kotaro Sasaki; Richard R Neptune; Steven A Kautz
Journal:  J Exp Biol       Date:  2009-03       Impact factor: 3.312

3.  Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement.

Authors:  Jennifer L Hicks; Thomas K Uchida; Ajay Seth; Apoorva Rajagopal; Scott L Delp
Journal:  J Biomech Eng       Date:  2015-01-26       Impact factor: 2.097

Review 4.  Review and perspective: neuromechanical considerations for predicting muscle activation patterns for movement.

Authors:  Lena H Ting; Stacie A Chvatal; Seyed A Safavynia; J Lucas McKay
Journal:  Int J Numer Method Biomed Eng       Date:  2012-05-16       Impact factor: 2.747

5.  Minimal formulation of joint motion for biomechanisms.

Authors:  Ajay Seth; Michael Sherman; Peter Eastman; Scott Delp
Journal:  Nonlinear Dyn       Date:  2010-10-01       Impact factor: 5.022

6.  Individual muscle contributions to the axial knee joint contact force during normal walking.

Authors:  Kotaro Sasaki; Richard R Neptune
Journal:  J Biomech       Date:  2010-07-23       Impact factor: 2.712

7.  Motor adaptation to prosthetic cycling in people with trans-tibial amputation.

Authors:  W Lee Childers; Boris I Prilutsky; Robert J Gregor
Journal:  J Biomech       Date:  2014-04-26       Impact factor: 2.712

8.  Hip and ankle responses for reactive balance emerge from varying priorities to reduce effort and kinematic excursion: A simulation study.

Authors:  Chris S Versteeg; Lena H Ting; Jessica L Allen
Journal:  J Biomech       Date:  2016-08-08       Impact factor: 2.712

9.  Novel Insights Into Biarticular Muscle Actions Gained From High-Density Electromyogram.

Authors:  Kohei Watanabe; Taian Martins Vieira; Alessio Gallina; Motoki Kouzaki; Toshio Moritani
Journal:  Exerc Sport Sci Rev       Date:  2021-07-01       Impact factor: 6.230

10.  How to assess performance in cycling: the multivariate nature of influencing factors and related indicators.

Authors:  A Margherita Castronovo; Silvia Conforto; Maurizio Schmid; Daniele Bibbo; Tommaso D'Alessio
Journal:  Front Physiol       Date:  2013-05-21       Impact factor: 4.566

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.