Comparative Analysis of Shoulder Muscle Activity During Cast Movements for Clubbell and Dumbbell Exercises

2021 ◽  
Vol 23 (3) ◽  
pp. 160-169
Author(s):  
Jeong-No Lee ◽  
Gyeong-Heon You ◽  
Yun-Ah Shin
2010 ◽  
Vol 25 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Bryan R. Picco ◽  
Steven L. Fischer ◽  
Clark R. Dickerson

2019 ◽  
Author(s):  
Rodrigo S. Maeda ◽  
Paul L. Gribble ◽  
J. Andrew Pruszynski

AbstractPrevious work has demonstrated that when learning a new motor task, the nervous system modifies feedforward (ie. voluntary) motor commands and that such learning transfers to fast feedback (ie. reflex) responses evoked by mechanical perturbations. Here we show the inverse, that learning new feedback responses transfers to feedforward motor commands. Sixty human participants (34 females) used a robotic exoskeleton and either 1) received short duration mechanical perturbations (20 ms) that created pure elbow rotation or 2) generated self-initiated pure elbow rotations. They did so with the shoulder joint free to rotate (normal arm dynamics) or locked (altered arm dynamics) by the robotic manipulandum. With the shoulder unlocked, the perturbation evoked clear shoulder muscle activity in the long-latency stretch reflex epoch (50-100ms post-perturbation), as required for countering the imposed joint torques, but little muscle activity thereafter in the so-called voluntary response. After locking the shoulder joint, which alters the required joint torques to counter pure elbow rotation, we found a reliable reduction in the long-latency stretch reflex over many trials. This reduction transferred to feedforward control as we observed 1) a reduction in shoulder muscle activity during self-initiated pure elbow rotation trials and 2) kinematic errors (ie. aftereffects) in the direction predicted when failing to compensate for normal arm dynamics, even though participants never practiced self-initiated movements with the shoulder locked. Taken together, our work shows that transfer between feedforward and feedback control is bidirectional, furthering the notion that these processes share common neural circuits that underlie motor learning and transfer.


Author(s):  
Ciro Agnelli ◽  
John A. Mercer

Background: Triathletes typically wear a wetsuit during the swim portion of an event, but it is not clear if muscle activity is influenced by wearing a wetsuit. Purpose: To investigate if shoulder muscle activity was influenced by wearing a full-sleeve wetsuit vs. no wetsuit during dryland swimming. Methods: Participants (n=10 males; 179.1±13.2 cm; 91.2±7.25 kg; 45.6±10.5 years) completed two dry land swimming conditions on a swim ergometer: No Wetsuit (NW) and with Wetsuit (W). Electromyography (EMG) of four upper extremity muscles was recorded (Noraxon telemetry EMG, 500 Hz) during each condition: Trapezius (TRAP), Triceps (TRI), Anterior Deltoid (AD) and Posterior Deltoid (PD). Each condition lasted 90 seconds with data collected during the last 60 seconds. Resistance setting was self-selected and remained constant for both conditions. Stroke rate was controlled at 60 strokes per minute by having participants match a metronome. Average (AVG) and Root Mean Square (RMS) EMG were calculated over 45 seconds and each were compared between conditions using a paired t-test (α=0.05) for each muscle. Results: PD and AD AVG and RMS EMG were each greater (on average 40.0% and 66.8% greater, respectively) during W vs. NW (p<0.05) while neither TRAP nor TRI AVG or RMS EMG were different between conditions (p>0.05). Conclusion: The greater PD and AD muscle activity while wearing a wetsuit might affect swimming performance and /or stroke technique on long distance event.


2009 ◽  
Vol 39 (8) ◽  
pp. 663-685 ◽  
Author(s):  
Rafael F. Escamilla ◽  
Kyle Yamashiro ◽  
Lonnie Paulos ◽  
James R. Andrews

2018 ◽  
Vol 476 (6) ◽  
pp. 1276-1283 ◽  
Author(s):  
Kwanwoo Kim ◽  
Hyun-Jung Hwang ◽  
Seul-Gi Kim ◽  
Jin-Hyuck Lee ◽  
Woong Kyo Jeong

2020 ◽  
Vol 123 (3) ◽  
pp. 1193-1205 ◽  
Author(s):  
Rodrigo S. Maeda ◽  
Julia M. Zdybal ◽  
Paul L. Gribble ◽  
J. Andrew Pruszynski

Generalizing newly learned movement patterns beyond the training context is challenging for most motor learning situations. Here we tested whether learning of a new physical property of the arm during self-initiated reaching generalizes to new arm configurations. Human participants performed a single-joint elbow reaching task and/or countered mechanical perturbations that created pure elbow motion with the shoulder joint free to rotate or locked by the manipulandum. With the shoulder free, we found activation of shoulder extensor muscles for pure elbow extension trials, appropriate for countering torques that arise at the shoulder due to forearm rotation. After locking the shoulder joint, we found a partial reduction in shoulder muscle activity, appropriate because locking the shoulder joint cancels the torques that arise at the shoulder due to forearm rotation. In our first three experiments, we tested whether and to what extent this partial reduction in shoulder muscle activity generalizes when reaching in different situations: 1) different initial shoulder orientation, 2) different initial elbow orientation, and 3) different reach distance/speed. We found generalization for the different shoulder orientation and reach distance/speed as measured by a reliable reduction in shoulder activity in these situations but no generalization for the different elbow orientation. In our fourth experiment, we found that generalization is also transferred to feedback control by applying mechanical perturbations and observing reflex responses in a distinct shoulder orientation. These results indicate that partial learning of new intersegmental dynamics is not sufficient for modifying a general internal model of arm dynamics. NEW & NOTEWORTHY Here we show that partially learning to reduce shoulder muscle activity following shoulder fixation generalizes to other movement conditions, but it does not generalize globally. These findings suggest that the partial learning of new intersegmental dynamics is not sufficient for modifying a general internal model of the arm’s dynamics.


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