Neuro-Mechanics of Recumbent Leg Cycling in Post-Acute Stroke Patients

Emilia Ambrosini, Cristiano De Marchis, Alessandra Pedrocchi, Giancarlo Ferrigno, Marco Monticone, Maurizio Schmid, Tommaso D'Alessio, Silvia Conforto, Simona Ferrante, Emilia Ambrosini, Cristiano De Marchis, Alessandra Pedrocchi, Giancarlo Ferrigno, Marco Monticone, Maurizio Schmid, Tommaso D'Alessio, Silvia Conforto, Simona Ferrante

Abstract

Cycling training is strongly applied in post-stroke rehabilitation, but how its modular control is altered soon after stroke has been not analyzed yet. EMG signals from 9 leg muscles and pedal forces were measured bilaterally during recumbent pedaling in 16 post-acute stroke patients and 12 age-matched healthy controls. Patients were asked to walk over a GaitRite mat and standard gait parameters were computed. Four muscle synergies were extracted through nonnegative matrix factorization in healthy subjects and patients unaffected legs. Two to four synergies were identified in the affected sides and the number of synergies significantly correlated with the Motricity Index (Spearman's coefficient = 0.521). The reduced coordination complexity resulted in a reduced biomechanical performance, with the two-module sub-group showing the lowest work production and mechanical effectiveness in the affected side. These patients also exhibited locomotor impairments (reduced gait speed, asymmetrical stance time, prolonged double support time). Significant correlations were found between cycling-based metrics and gait parameters, suggesting that neuro-mechanical quantities of pedaling can inform on walking dysfunctions. Our findings support the use of pedaling as a rehabilitation method and an assessment tool after stroke, mainly in the early phase, when patients can be unable to perform a safe and active gait training.

Keywords: Biomechanics; Electromyography; Hemiparesis; Motor control; Muscle synergies; Pedaling.

Figures

Figure 1
Figure 1
Experimental setup.
Figure 2
Figure 2
Average set of synergies extracted from the healthy subjects (WHEALTHY, left column) and reconstructed synergy activation coefficients at different cadences, obtained from the application of the Nonnegative Reconstruction by fixing the matrix WHEALTHY (right column, each line is obtained by averaging both sides of all subjects).
Figure 3
Figure 3
Spatio-temporal structure of the modules extracted from the patients. Group A, B, and C represent the average among subjects with two (n = 3), three (n = 6), and four (n = 7) modules in the affected side; Group D shows the average among all subjects with 4 modules (n = 16) in the unaffected side. Synergy vectors W and activation coefficients H obtained at different cadences are also averaged.
Figure 4
Figure 4
Reconstructed module recruitment (synergy activation coefficients H, lower panels) by applying NNR with fixed WHEALTHY (upper panels) for the two-, three-, and four-module affected sub-groups, for the unaffaced sub-group, and for the healthy subjects group at 30 RPM.
Figure 5
Figure 5
Tangential force profiles without passive contributions for the different sub-groups of patients (the solid lines represent the mean value of the corresponding sub-group) and for the healthy subjects group (the area indicate the mean value ± the standard deviation).
Figure 6
Figure 6
Cycling-based metrics computed for the trial at 30 RPM. Mean values and standard deviation are reported for the two-, three-, and four-module sub-group of patients and for the healthy subjects group.
Figure 7
Figure 7
Gait parameters computed for the two-, three-, and four-module sub-group of patients. Mean values and standard deviation are reported.

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