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      Timing and Modulation of Activity in the Lower Limb Muscles During Indoor Rowing: What Are the Key Muscles to Target in FES-Rowing Protocols?

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          Abstract

          The transcutaneous stimulation of lower limb muscles during indoor rowing (FES Rowing) has led to a new sport and recreation and significantly increased health benefits in paraplegia. Stimulation is often delivered to quadriceps and hamstrings; this muscle selection seems based on intuition and not biomechanics and is likely suboptimal. Here, we sample surface EMGs from 20 elite rowers to assess which, when, and how muscles are activated during indoor rowing. From EMG amplitude we specifically quantified the onset of activation and silencing, the duration of activity and how similarly soleus, gastrocnemius medialis, tibialis anterior, rectus femoris, vastus lateralis and medialis, semitendinosus, and biceps femoris muscles were activated between limbs. Current results revealed that the eight muscles tested were recruited during rowing, at different instants and for different durations. Rectus and biceps femoris were respectively active for the longest and briefest periods. Tibialis anterior was the only muscle recruited within the recovery phase. No side differences in the timing of muscle activity were observed. Regression analysis further revealed similar, bilateral modulation of activity. The relevance of these results in determining which muscles to target during FES Rowing is discussed. Here, we suggest a new strategy based on the stimulation of vasti and soleus during drive and of tibialis anterior during recovery.

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          Most cited references35

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          Interpreting Signal Amplitudes in Surface Electromyography Studies in Sport and Rehabilitation Sciences

          Surface electromyography (sEMG) is a popular research tool in sport and rehabilitation sciences. Common study designs include the comparison of sEMG amplitudes collected from different muscles as participants perform various exercises and techniques under different loads. Based on such comparisons, researchers attempt to draw conclusions concerning the neuro- and electrophysiological underpinning of force production and hypothesize about possible longitudinal adaptations, such as strength and hypertrophy. However, such conclusions are frequently unsubstantiated and unwarranted. Hence, the goal of this review is to discuss what can and cannot be inferred from comparative research designs as it pertains to both the acute and longitudinal outcomes. General methodological recommendations are made, gaps in the literature are identified, and lines for future research to help improve the applicability of sEMG are suggested.
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            Trunk muscle onset detection technique for EMG signals with ECG artefact.

            The timing of trunk muscle activation has become an important element in the understanding of human movement in normal and chronic low back pain populations. The detection of anticipatory postural adjustment via trunk muscle onsets from electromyographic (EMG) signals can be problematic due to baseline noise or electro-cardiac (ECG) artefact. Shewhart protocols or whole signal analyses may show different degrees of sensitivity under different conditions. Muscle activity onsets were determined from surface EMG of seven muscles for five trials before and after fatigue were examined in four subjects (n=280). The objective of this study was to examine two detection methods (Shewhart and integrated protocol (IP)) in determining the onsets of trunk muscles. The variability of the baseline amplitude and the impact of added Gaussian noise on the detected onsets were used to test for robustness. The results of this study demonstrate that before and after fatigue there is a large degree of baseline variance in the trunk muscles (coefficients of variation between 40-65%) between trials. This could be normal response to body sway. The IP method was less susceptible to false onsets (detecting onsets in the baseline window) 3 vs. 51%. The findings suggest the IP method is robust with large variance in the baseline if the signal to noise ratio is greater than six. In spite of the robustness of the algorithm, the findings would suggest that statistical assessments should be used to target trials for selective visual inspection for subtle trunk muscle onsets.
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              No evidence of expertise-related changes in muscle synergies during rowing.

              The purpose of the present study was to determine whether expertise in rowing is driven by a specific structure in muscular coordination. We compared seven experienced rowers and eight untrained (i.e., inexperienced) subjects during rowing on an ergometer. Both surface electromyography activity and mechanical patterns (forces exerted at the handle and the foot-stretcher) were recorded during a high intensity rowing exercise. A non-negative matrix factorization was applied to 23 electromyographic patterns to differentiate muscle synergies. Results showed that expertise was not associated with different dimensionality in the electromyographic data and that three muscle synergies were sufficient to explain the majority of the variance accounted for (i.e., >90% of the total variance) in the two populations. The synergies extracted were similar in the two populations, with identical functional roles. While the temporal organization of the propulsive synergies was very similar, slight differences were found in the composition of the muscle synergies (muscle synergy vectors) between the two populations. The results suggests that rowing expertise would not require the development of novel muscle synergies but would imply intrinsic synergies already used in different behaviors. Performance in rowing is more probably linked to adjustments in the mechanical output of the muscle synergies rather than to differences in the shape and timing of their activations. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                17 March 2020
                March 2020
                : 20
                : 6
                : 1666
                Affiliations
                [1 ]Laboratorio di Ingegneria del Sistema Neuromuscolare (LISiN), Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy; giacintoluigi.cerone@ 123456polito.it (G.L.C.); marco.gazzoni@ 123456polito.it (M.G.)
                [2 ]PolitoBIOMed Lab, Politecnico di Torino, 10129 Torino, Italy
                [3 ]School of Engineering, University of Warwick, Coventry CV4 7AL, UK; brian.andrews@ 123456nds.ox.ac.uk
                [4 ]Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX1 2JD, UK
                Author notes
                Author information
                https://orcid.org/0000-0002-6239-7301
                https://orcid.org/0000-0002-5295-5314
                Article
                sensors-20-01666
                10.3390/s20061666
                7147320
                32192073
                6804d59c-5362-47c2-8de0-645bf0590e4d
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 02 January 2020
                : 13 March 2020
                Categories
                Article

                Biomedical engineering
                rowing,electromyography,muscle,functional electrical stimulation
                Biomedical engineering
                rowing, electromyography, muscle, functional electrical stimulation

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