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Michelle A. Sandrey, Yu-Jen Chang, and Jean L. McCrory

time (prefatigue and postfatigue). The dependent variables were the linear envelope measurements of the medial gastrocnemius (MG), SOL, and tibialis anterior and tibial peak accelerations (resultant acceleration takeoff and landing). Participants A total of 30 active college-aged students with and

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Matthew S. Tenan, Andrew J. Tweedell, and Courtney A. Haynes

movement. When considering surface EMG, this onset is commonly quantified via a linear envelope methodology proposed by David Winter. 1 Additional algorithmic approaches have also been validated for surface EMG onset detection, such as the Teager-Kaiser energy operator 2 and Sample Entropy. 3

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Denys Batista Campos, Isabella Christina Ferreira, Matheus Almeida Souza, Macquiden Amorim Jr, Leonardo Intelangelo, Gabriela Silveira-Nunes, and Alexandre Carvalho Barbosa

Valkyria ™ Software ( Ivolution ™ Isoinertial Equipment). The sEMG and the optical encoder were video synchronized. The whole sEMG signal was smoothed. The linear envelope (integrated electromyographic signal or iEMG) was extracted from the whole sEMG signal to describe the intensity of the neuromuscular

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J.-M. John Wilson, D. Gordon E. Robertson, and J. Peter Stothart

In an effort to seek further understanding of lower limb muscle function in the rowing movement, an electromyographic analysis was undertaken of rowers rowing on a Gjessing ergometer. A strain gauged transducer was inserted in the ergometer linkage between handle and flywheel to detect pulling force. Electrodes were placed on the following lower limb muscles: gluteus maximus, biceps femoris, rectus femoris, vastus lateralis, gastrocnemius, and tibialis anterior. Linear envelope electromyograms from each muscle and the force signals were sampled synchronously at 50 Hz. The results indicated that all six muscles were active from catch to finish of the drive phase. Biceps femoris, gluteus maximus, gastrocnemius, and vastus lateralis all began their activity at or just prior to catch and reached maximal excitation near peak force of the stroke. Rectus femoris and tibialis anterior activity began prior to the catch and reached maximal excitation subsequent to peak force. The coactivation of the five leg muscles, of which four were biarticular, included potentially antagonistic actions that would cancel each other’s effects. Clearly, however, other explanations must be considered. Both gastrocnemius and biceps femoris have been shown to act as knee extensors and may do so in the case of the rowing action. Furthermore, rectus femoris may act with unchanging length as a knee extensor by functioning as a rigid link between the pelvis and tibia. In this manner, energy created by the hip extensors is transferred across the knee joint via the isometrically contracting rectus femoris muscle.

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Dean C. Hay, Mark P. Wachowiak, and Ryan B. Graham

Advances in time-frequency analysis can provide new insights into the important, yet complex relationship between muscle activation (ie, electromyography [EMG]) and motion during dynamic tasks. We use wavelet coherence to compare a fundamental cyclical movement (lumbar spine flexion and extension) to the surface EMG linear envelope of 2 trunk muscles (lumbar erector spinae and internal oblique). Both muscles cohere to the spine kinematics at the main cyclic frequency, but lumbar erector spinae exhibits significantly greater coherence than internal oblique to kinematics at 0.25, 0.5, and 1.0 Hz. Coherence phase plots of the 2 muscles exhibit different characteristics. The lumbar erector spinae precedes trunk extension at 0.25 Hz, whereas internal oblique is in phase with spine kinematics. These differences may be due to their proposed contrasting functions as a primary spine mover (lumbar erector spinae) versus a spine stabilizer (internal oblique). We believe that this method will be useful in evaluating how a variety of factors (eg, pain, dysfunction, pathology, fatigue) affect the relationship between muscles’ motor inputs (ie, activation measured using EMG) and outputs (ie, the resulting joint motion patterns).

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Gareth Ryan, Heather Johnston, and Janice Moreside

constant (fourth-order 3-Hz Butterworth) to create a linear envelope. SEMG data from the ER trials were normalized to MVIC and displayed as percentages. Peak and average muscle activation at each abduction angle were determined from the processed data, as well as peak and average INFRA/PD, which was used

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Stephen M. Suydam, Kurt Manal, and Thomas S. Buchanan

details can be found in Appendix . EMG signals were processed by high-pass filtering using a fourth-order Butterworth filter with a 30-Hz cutoff frequency, which removed DC offsets, and then the signal was full-wave rectified. A linear envelope was created by low-pass filtering the signals at 4 Hz. The

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Afshin Samani and Mathias Kristiansen

recorded during the set performed at 75% of 3RM, the linear envelopes of the EMG measurements were computed (fourth-order, low-pass Butterworth filter with a cutoff frequency at 5 Hz). The maximum of the linear envelope was used as a normalization factor for matching the EMG measurement. The linear

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Jinger S. Gottschall, Bryce Hastings, and Zachary Becker

-pass, sixth-order 10- to 500-Hz band-pass Butterworth filter. The surface EMG signals were full-wave rectified, and relative muscle activity was determined by creating a linear envelope using a low-pass, fourth-order Butterworth filter with a cutoff frequency of 6 Hz. We placed 50-mm bipolar, silver

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Harsh H. Buddhadev and Philip E. Martin

a second-order Butterworth filter to create a linear envelope. For the most challenging experimental condition (i.e., 125 W-90 rpm), peak EMG value was identified for each of 15 crank cycles for each muscle group. These peak EMG values were subsequently averaged to get an overall mean peak EMG value