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Mark Holten Mora-Jensen, Pascal Madeleine and Ernst Albin Hansen

bout rate enhancement was reported in healthy individuals ( Hansen, Ebbesen, Dalsgaard, Mora-Jensen, & Rasmussen, 2015 ). Briefly, the phenomenon constitutes a cumulating increase in freely chosen tapping frequency following submaximal muscle activation and movement consisting of externally unloaded

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Ui-Jae Hwang, Sung-Hoon Jung, Hyun-A Kim, Jun-Hee Kim and Oh-Yun Kwon

with EMS, which may produce a load insufficient to yield group differences. The analysis did not reveal a significant difference in the CSA of the RA or LAW between the groups, so we could not confirm that ST was superior to EMS for increasing the CSA of the RA and LAW; moreover, muscle activation was

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Kevin McCurdy and John Walker

. 5 – 10 These segments have been shown to differ in moment arm length, mechanical line of action to the intended movement, fiber type, and anatomical structure, 7 , 11 , 12 which is suggested to determine muscle activation patterns. 7 For instance, McAndrew et al 11 determined that 3 regions

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Aaron Derouin and Jim R. Potvin

/(EMG AG  + EMG ANT ) × 100, where EMG AG and EMG ANT refer to agonist and antagonist muscle activations, respectively. Co-contraction was also calculated for the gastrocnemius medialis and vastus lateralis. The statistical analysis for each of the 18 conditions included a calculation of the means and

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James W. Youdas, Hannah E. Baartman, Brian J. Gahlon, Tyler J. Kohnen, Robert J. Sparling and John H. Hollman

from low to high. Of the 16 estimates of torso muscle activation (4 muscles × 4 exercise conditions) in the present study, 11 were classified as moderate recruitment according to DiGiovine classification of muscle recruitment. 15 This finding is consistent with McGill et al’s work, 8 who found

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Christopher Kevin Wong, Lizbeth Conway, Grant Fleming, Caitlin Gopie, Dara Liebeskind and Stephen Xue

attributed to neural adaptations that facilitate muscle activation, 1 while muscle hypertrophy and strength gains occur later, after 6 to 13 weeks. 2 Recent research has shown that, despite muscle hypertrophy, weakness can persist, particularly in the early stages of joint pathology. 3 , 4 Joint and

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Deepika Singla and M. Ejaz Hussain

neuromuscular adaptations in the upper body to MBPT in cricket players of different age groups. Overall, the results of the present study showed that 8 weeks of plyometric training can elicit significant neuromuscular adaptations in cricketers from different age groups by improving their muscle activation

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Sean A. Jones, Derek N. Pamukoff, Timothy C. Mauntel, J. Troy Blackburn and Joseph B. Myers

dyskinesis is an alteration of static scapular position and dynamic scapular motion that contribute to a lengthening of the posterior musculature and shortening of the anterior musculature, which may contribute to abnormal muscle activation. 3 Fortunately, scapular dyskinesis and SIS can be effectively

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William P. Berg and Michael R. Hughes

Muscle activation was measured using EMG in 28 males (n = 28) while participants caught visually identical balls of known and unknown weights (50, 1.32, 2.18, and 2.99 kg) under variable (1–10s) and constant (3s) foreperiods. EMG integrals were computed for three time intervals before the catch (anticipatory), and one after (compensatory). Load uncertainty caused the CNS to use an anticipatory strategy characterized by preparation to catch balls of an unknown weight by utilizing about 92% of the muscle activation used to catch the heaviest possible ball under the known weight condition. The CNS appeared to scale anticipatory muscle activation to afford an opportunity to catch a ball of an unknown weight between .50 and 2.99 kg. The constant 3s foreperiod, which permitted temporal anticipation, did not influence the anticipatory neuromotor strategy adopted by the CNS to cope with load uncertainty. Load uncertainty also altered compensatory neuromotor control in catching.

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Thomas S. Buchanan, David G. Lloyd, Kurt Manal and Thor F. Besier

This paper provides an overview of forward dynamic neuromusculoskeletal modeling. The aim of such models is to estimate or predict muscle forces, joint moments, and/or joint kinematics from neural signals. This is a four-step process. In the first step, muscle activation dynamics govern the transformation from the neural signal to a measure of muscle activation—a time varying parameter between 0 and 1. In the second step, muscle contraction dynamics characterize how muscle activations are transformed into muscle forces. The third step requires a model of the musculoskeletal geometry to transform muscle forces to joint moments. Finally, the equations of motion allow joint moments to be transformed into joint movements. Each step involves complex nonlinear relationships. The focus of this paper is on the details involved in the first two steps, since these are the most challenging to the biomechanician. The global process is then explained through applications to the study of predicting isometric elbow moments and dynamic knee kinetics.