Effects of Treadmill Walking Speed on Lateral Gastrocnemius Muscle Firing

Clancy, Edward A.; Cairns, Kevin D.; Riley, Patrick O.; Meister, Melvin; Kerrigan, D. Casey

doi:10.1097/01.phm.0000130033.91894.5a

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…Interestingly, our finding of the prolonged firing duration of MG poststroke as compared to baseline value (Fig. 4a) was in line with a simulation model study from Clancy and coworkers who set 0.5 m/s as a typical speed for persons after stroke (Clancy et al, 2004). They found that the profile of the main peak of MG was broadened, with a delayed onset and completion, at the lower speed.…”

Section: Symmetery Indexsupporting

confidence: 93%

Muscle activation changes during body weight support treadmill training after focal cortical ischemia: A rat hindlimb model

Wei

Zheng

et al. 2011

Journal of Electromyography and Kinesiology

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Section: Symmetery Indexsupporting

confidence: 93%

Muscle activation changes during body weight support treadmill training after focal cortical ischemia: A rat hindlimb model

Wei

Zheng

et al. 2011

Journal of Electromyography and Kinesiology

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“…However, computerized algorithms can fail to accurately detect burst onset in experimental data, indicating that some of these techniques may not be sensitive enough to improved SNR. In several studies, EMG burst properties were analyzed with visual inspection instead of a computerized algorithm (Clancy et al 2004; Latash et al 1995; Tedroff et al 2008; Vasseljen et al 2006), suggesting that algorithm validation using artificially generated EMG signals may not be sufficient to test robustness and accuracy of onset detection techniques.…”

Section: Introductionmentioning

confidence: 99%

Teager–Kaiser energy operator signal conditioning improves EMG onset detection

et al. 2010

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Accurate identification of the onset of muscle activity is an important element in the biomechanical analysis of human movement. The purpose of this study was to determine if inclusion of the Teager–Kaiser energy operator (TKEO) in signal conditioning would increase the accuracy of popular electromyography (EMG) onset detection methods. Three methods, visual determination, threshold-based method, and approximated generalized likelihood ratio were used to estimate the onset of EMG burst with and without TKEO conditioning. Reference signals, with known onset times, were constructed from EMG signals collected during isometric contraction of the vastus lateralis (n = 17). Additionally, vastus lateralis EMG signals (n = 255) recorded during gait were used to evaluate a clinical application of the TKEO conditioning. Inclusion of TKEO in signal conditioning significantly reduced mean detection error of all three methods compared with signal conditioning without TKEO, using artificially generated reference data (13 vs. 98 ms, p < 0.001) and also compared with experimental data collected during gait (55 vs. 124 ms, p < 0.001). In conclusion, addition of TKEO as a step in conditioning surface EMG signals increases the detection accuracy of EMG burst boundaries.

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“…These data are consistent with the idea that muscle activation increases with increasing moments and that a greater muscle mass is activated more effectively at all contraction speeds. Gait data also supports this contention [56,57]. High moments and muscle mass require greater activation compared with the activation needed during isokinetic contractions of increasing speed.…”

Section: Discussionmentioning

confidence: 63%

Training History-Dependent Functional Role of EMG Model-Predicted Antagonist Moments in Knee Extensor Moment Generation in Healthy Young Adults

et al. 2022

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Resistance training (RT) improves the skeletal muscle’s ability to generate maximal voluntary force and is accompanied by changes in the activation of the antagonist muscle which is not targeted primarily by RT. However, the nature and role of neural adaptation to RT in the antagonist muscle is paradoxical and not well understood. We compared moments, agonist muscle activation, antagonist activation, agonist-antagonist coactivation, and electromyographic (EMG) model-predicted moments generated by antagonist hamstring muscle coactivation during isokinetic knee extension in leg strength-trained (n = 10) and untrained (n = 11) healthy, younger adults. Trained vs. untrained adults were up to 58% stronger. During knee extension, hamstring activation was 1.6-fold greater in trained vs. untrained adults (p = 0.022). This hamstring activation produced 2.6-fold greater model-predicted antagonist moments during knee extension in the trained (42.7 ± 19.55 Nm) vs. untrained group (16.4 ± 12.18 Nm; p = 0.004), which counteracted (reduced) quadriceps knee extensor moments ~43 Nm (0.54 Nm·kg−1) and by ~16 Nm (0.25 Nm·kg−1) in trained vs. untrained. Antagonist hamstring coactivation correlated with decreases and increases, respectively, in quadriceps moments in trained and untrained. The EMG model-predicted antagonist moments revealed training history-dependent functional roles in knee extensor moment generation.

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Effects of Treadmill Walking Speed on Lateral Gastrocnemius Muscle Firing

Cited by 13 publications

References 21 publications

Muscle activation changes during body weight support treadmill training after focal cortical ischemia: A rat hindlimb model

Muscle activation changes during body weight support treadmill training after focal cortical ischemia: A rat hindlimb model

Teager–Kaiser energy operator signal conditioning improves EMG onset detection

Training History-Dependent Functional Role of EMG Model-Predicted Antagonist Moments in Knee Extensor Moment Generation in Healthy Young Adults

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