Analysis of different image-based biofeedback models for improving cycling performances

Bibbo, Daniele; Conforto, Silvia; Bernabucci, Ivan; Carli, Marco; Schmid, Maurizio; D’Alessio, Tommaso

doi:10.1117/12.910605

Cited by 18 publications

(16 citation statements)

References 7 publications

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“…In this way, the subjects were helped to effectively orient the forces along the pedaling cycle, receiving real time information about which sector of the cycle they had to improve to reach an optimal pedaling technique (Bibbo et al, 2012). An entirely filled circle thus corresponds to IE = 1.…”

Section: Methodsmentioning

confidence: 99%

Feedback of mechanical effectiveness induces adaptations in motor modules during cycling

Marchis¹,

Schmid²,

Bibbo³

et al. 2013

Front. Comput. Neurosci.

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Recent studies have reported evidence that the motor system may rely on a modular organization, even if this behavior has yet to be confirmed during motor adaptation. The aim of the present study is to investigate the modular motor control mechanisms underlying the execution of pedaling by untrained subjects in different biomechanical conditions. We use the muscle synergies framework to characterize the muscle coordination of 11 subjects pedaling under two different conditions. The first one consists of a pedaling exercise with a strategy freely chosen by the subjects (Preferred Pedaling Technique, PPT), while the second condition constrains the gesture by means of a real time visual feedback of mechanical effectiveness (Effective Pedaling Technique, EPT). Pedal forces, recorded using a pair of instrumented pedals, were used to calculate the Index of Effectiveness (IE). EMG signals were recorded from eight muscles of the dominant leg and Non-negative Matrix Factorization (NMF) was applied for the extraction of muscle synergies. All the synergy vectors, extracted cycle by cycle for each subject, were pooled across subjects and conditions and underwent a 2-dimensional Sammon's non-linear mapping. Seven representative clusters were identified on the Sammon's projection, and the corresponding eight-dimensional synergy vectors were used to reconstruct the repertoire of muscle activation for all subjects and all pedaling conditions (VAF > 0.8 for each individual muscle pattern). Only 5 out of the 7 identified modules were used by the subjects during the PPT pedaling condition, while 2 additional modules were found specific for the pedaling condition EPT. The temporal recruitment of three identified modules was highly correlated with IE. The structure of the identified modules was found similar to that extracted in other studies of human walking, partly confirming the existence of shared and task specific muscle synergies, and providing further evidence on the modularity of the motor system.

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Section: Methodsmentioning

confidence: 99%

Feedback of mechanical effectiveness induces adaptations in motor modules during cycling

Marchis¹,

Schmid²,

Bibbo³

et al. 2013

Front. Comput. Neurosci.

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“…On this assumption, the position of each element in the sagittal plane, being their lengths known, can be reconstructed using 2 of the relative angles values θC, θP, θ1, θ2, θ3. The angles θC and θP, together with the pedal forces Fn and Ft, along the perpendicular and parallel axis to the pedal load plane, were directly measured on a cycling simulator [14,30], already validated in recent works for monitoring cycling activity in real time [31]. An inverse dynamics model provided muscular moments for each joint.…”

Section: Biomechanical Modelmentioning

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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“…Different representations are used to provide information to the athlete, and some studies in the literature focus on the use of visualization techniques (Aris et al, 2005). The aim of letting cyclists learn to pull up on the pedal and thus increase Eff Mech , has driven some authors to present visual feedback to the riders in rather different ways (Sanderson, 1987; Mornieux et al, 2010; Bibbo et al, 2012). The above-mentioned studies reported a significant variation of the pedaling gesture with the use of visual feedback confirming the hypothesis that, independently of the particular rendering scheme, the use of biofeedback allows riders to improve Eff Mech .…”

Section: Factors Related To Cycling Performancementioning

confidence: 99%

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

et al. 2013

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Finding an optimum for the cycling performance is not a trivial matter, since the literature shows the presence of many controversial aspects. In order to quantify different levels of performance, several indexes have been defined and used in many studies, reflecting variations in physiological and biomechanical factors. In particular, indexes such as Gross Efficiency (GE), Net Efficiency (NE) and Delta Efficiency (DE) have been referred to changes in metabolic efficiency (EffMet), while the Indexes of Effectiveness (IE), defined over the complete crank revolution or over part of it, have been referred to variations in mechanical effectiveness (EffMech). All these indicators quantify the variations of different factors [i.e., muscle fibers type distribution, pedaling cadence, setup of the bicycle frame, muscular fatigue (MFat), environmental variables, ergogenic aids, psychological traits (PsychTr)], which, moreover, show high mutual correlation. In the attempt of assessing cycling performance, most studies in the literature keep all these factors separated. This may bring to misleading results, leaving unanswered the question of how to improve cycling performance. This work provides an overview on the studies involving indexes and factors usually related to performance monitoring and assessment in cycling. In particular, in order to clarify all those aspects, the mutual interactions among these factors are highlighted, in view of a global performance assessment. Moreover, a proposal is presented advocating for a model-based approach that considers all factors mentioned in the survey, including the mutual interaction effects, for the definition of an objective function E representing the overall effectiveness of a training program in terms of both EffMet and EffMech.

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Analysis of different image-based biofeedback models for improving cycling performances

Cited by 18 publications

References 7 publications

Feedback of mechanical effectiveness induces adaptations in motor modules during cycling

Feedback of mechanical effectiveness induces adaptations in motor modules during cycling

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

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

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