Inter-individual variability of forces and modular muscle coordination in cycling: A study on untrained subjects

Marchis, Cristiano De; Schmid, Maurizio; Bibbo, Daniele; Bernabucci, Ivan; Conforto, Silvia

doi:10.1016/j.humov.2013.07.018

Cited by 50 publications

(34 citation statements)

References 47 publications

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“…It is a valuable tool for the reduction of large multivariate data sets, making it useful for muscle coordination during repetitive actions such as cycling to identify common underlying features. In addition to PC analysis, other data reduction tools, such as nonnegative matrix factorization, have been utilized to investigate muscle coordination during cycling (Barroso et al 2014;De Marchis et al 2013;Hug et al 2010), although these have been focused on data reduction with respect to muscle synergies. In this study we were not attempting to describe muscle synergies and used PC analysis as it provided the opportunity to extract sources of variability related to specific outcomes while allowing for negative values.…”

Section: Methodsmentioning

confidence: 99%

Muscle coordination limits efficiency and power output of human limb movement under a wide range of mechanical demands

Blake

Wakeling

2015

Journal of Neurophysiology

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Blake OM, Wakeling JM. Muscle coordination limits efficiency and power output of human limb movement under a wide range of mechanical demands. J Neurophysiol 114: 3283-3295, 2015. First published October 7, 2015 doi:10.1152/jn.00765.2015.-This study investigated the influence of cycle frequency and workload on muscle coordination and the ensuing relationship with mechanical efficiency and power output of human limb movement. Eleven trained cyclists completed an array of cycle frequency (cadence)-power output conditions while excitation from 10 leg muscles and power output were recorded. Mechanical efficiency was maximized at increasing cadences for increasing power outputs and corresponded to muscle coordination and muscle fiber type recruitment that minimized both the total muscle excitation across all muscles and the ineffective pedal forces. Also, maximum efficiency was characterized by muscle coordination at the top and bottom of the pedal cycle and progressive excitation through the uniarticulate knee, hip, and ankle muscles. Inefficiencies were characterized by excessive excitation of biarticulate muscles and larger duty cycles. Power output and efficiency were limited by the duration of muscle excitation beyond a critical cadence (120 -140 rpm), with larger duty cycles and disproportionate increases in muscle excitation suggesting deteriorating muscle coordination and limitations of the activation-deactivation capabilities. Most muscles displayed systematic phase shifts of the muscle excitation relative to the pedal cycle that were dependent on cadence and, to a lesser extent, power output. Phase shifts were different for each muscle, thereby altering their mechanical contribution to the pedaling action. This study shows that muscle coordination is a key determinant of mechanical efficiency and power output of limb movement across a wide range of mechanical demands and that the excitation and coordination of the muscles is limited at very high cycle frequencies. electromyography; excitation; power output; cadence; cycling; motor control HUMAN LIMB MOVEMENT IS ACHIEVED through interactions between the neuromuscular and skeletal systems. Movement performance is commonly measured by the resultant mechanical efficiency (ratio of mechanical power output to metabolic power) and power output, both of which are significantly influenced by the coordinated excitation of individual muscles and the coordination of that excitation between muscles Dorel et al. 2012;Samozino et al. 2007;Wakeling et al. 2010). Therefore, investigating movement performance requires a comprehensive understanding of how the individual muscle excitations and the coordination between muscles (muscle coordination) change in response to the demands.Mechanical demands such as workload and movement velocity can be uncoupled during cycling (Wakeling et al. 2006;Wakeling and Horn 2009), making it an appropriate model to investigate the effects of mechanical demands on individual muscle excitation and muscle coordination and the resultant performan...

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Section: Methodsmentioning

confidence: 99%

Muscle coordination limits efficiency and power output of human limb movement under a wide range of mechanical demands

Blake

Wakeling

2015

Journal of Neurophysiology

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“…The experimental protocol carried out for this study has been previously reported (De Marchis et al 2013a). Summarizing, participants accomplished the motor task on an aerodynamically braked cycle-simulator equipped with instrumented pedals and standard 0.17-m cranks.…”

Section: Experimental Protocolmentioning

confidence: 99%

“…In this study, we aim at assessing the presence of IMC among muscles that have been previously shown to be synergistic during a free pedaling task (Hug et al 2010;De Marchis et al 2013a). Under the hypothesis that the presence of intermuscular synchronization during this task could be mainly related to cortical command and activity of the CPGs, IMC could provide a frequency domain correlate of temporal modular control by the CNS and has the potential to provide more direct information on the underlying neural mechanisms of rhythmic lower limb control both at the cortical and spinal level.…”

Section: Introductionmentioning

confidence: 99%

Intermuscular coherence contributions in synergistic muscles during pedaling

et al. 2015

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The execution of rhythmical motor tasks requires the control of multiple skeletal muscles by the Central Nervous System (CNS), and the neural mechanisms according to which the CNS manages their coordination are not completely clear yet. In this study, we analyze the distribution of the neural drive shared across muscles that work synergistically during the execution of a free pedaling task. Electromyographic (EMG) activity was recorded from eight lower limb muscles of eleven healthy untrained participants during an unconstrained pedaling exercise. The coordinated activity of the lower limb muscles was described within the framework of muscle synergies, extracted through the application of nonnegative matrix factorization. Intermuscular synchronization was assessed by calculating intermuscular coherence between pairs of EMG signals from co-active, both synergistic and non-synergistic muscles within their periods of co-activation. The spatiotemporal structure of muscle coordination during pedaling was well represented by four muscle synergies for all the subjects. Significant coherence values within the gamma band (30-60 Hz) were identified only for one out of the four extracted muscle synergies. This synergy is mainly composed of the activity of knee extensor muscles, and its function is related to the power production and crank propelling during the pedaling cycle. In addition, a significant coherence peak was found in the lower frequencies for the GAM/SOL muscle pair, possibly related to the ankle stabilizing function of these two muscles during the pedaling task. No synchronization was found either for the other extracted muscle synergies or for pairs of co-active but non-synergistic muscles. The obtained results seem to suggest the presence of intermuscular synchronization only when a functional force production is required, with the observed gamma band contribution possibly reflecting a cortical drive to synergistic muscles during pedaling.

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“…This aspect can be studied evaluating the role of muscle activity [2,3,4] or the power exerted, using different techniques [5]. To this aim a biomechanical model based system, able to predict forces acting on each involved joint, can be adopted with the design of an optimization criterion suited to determine the contribution of each muscle to the overall force [6].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Embedded System for Real-time Estimation of Muscle Forces

Lozito

Schmid

Conforto

et al. 2015

Procedia Computer Science

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This work documents the progress towards the implementation of an embedded solution for muscular forces assessment during cycling activity. The core of the study is the adaptation to a real-time paradigm an inverse biomechanical model. The model is well suited for real-time applications since all the optimization problems are solved through a direct neural estimator. The real-time version of the model was implemented on an embedded microcontroller platform to profile code performance and precision degradation, using different numerical techniques to balance speed and accuracy in a low computational resources environment

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Inter-individual variability of forces and modular muscle coordination in cycling: A study on untrained subjects

Cited by 50 publications

References 47 publications

Muscle coordination limits efficiency and power output of human limb movement under a wide range of mechanical demands

Muscle coordination limits efficiency and power output of human limb movement under a wide range of mechanical demands

Intermuscular coherence contributions in synergistic muscles during pedaling

A Neural Network Embedded System for Real-time Estimation of Muscle Forces

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