scholarly journals Muscle activation assessment: Effects of method, stimulus number, and joint angle

2006 ◽  
Vol 34 (6) ◽  
pp. 740-746 ◽  
Author(s):  
Theodoros M. Bampouras ◽  
Neil D. Reeves ◽  
Vasilios Baltzopoulos ◽  
Constantinos N. Maganaris
Keyword(s):  
Muscle Activation ◽  
Joint Angle ◽  
Stimulus Number

scholarly journals The Effects of Elbow Joint Angle Change on the Elbow Flexor Muscle Activation in Pulley with Weight Exercise

2013 ◽  
Vol 25 (9) ◽  
pp. 1133-1136 ◽  
Author(s):  
Taewook Kang ◽  
Youngjoon Seo ◽  
Jaehoon Park ◽  
Eunseok Dong ◽  
Byungdo Seo ◽  
...  
Keyword(s):  
Elbow Joint ◽  
Muscle Activation ◽  
Joint Angle ◽  
Elbow Flexor ◽  
Flexor Muscle ◽  
Angle Change

Effect of Knee Joint Angle on Regional Hamstrings Activation During Isometric Knee-Flexion Exercise

2020 ◽  
pp. 1-6
Author(s):  
Raki Kawama ◽  
Masamichi Okudaira ◽  
David H. Fukuda ◽  
Hirohiko Maemura ◽  
Satoru Tanigawa
Keyword(s):  
Knee Joint ◽  
Muscle Activity ◽  
Repeated Measures ◽  
Knee Flexion ◽  
Muscle Activation ◽  
Joint Angle ◽  
Motor Nerve ◽  
Activity Level ◽  
Activity Levels ◽  
Knee Joint Angle

Context: Each hamstring muscle is subdivided into several regions by multiple motor nerve branches, which implies each region has different muscle activation properties. However, little is known about the muscle activation of each region with a change in the knee joint angle. Understanding of regional activation of the hamstrings could be helpful for designing rehabilitation and training programs targeted at strengthening a specific region. Objective: To investigate the effect of knee joint angle on the activity level of several regions within the individual hamstring muscles during isometric knee-flexion exercise with maximal effort (MVCKF). Design: Within-subjects repeated measures. Setting: University laboratory. Participants: Sixteen young males with previous participation in sports competition and resistance training experience. Intervention: The participants performed 2 MVCKF trials at each knee joint angle of 30°, 60°, and 90°. Outcome Measures: Surface electromyography was used to measure muscle activity in the proximal, middle, and distal regions of the biceps femoris long head (BFlh), semitendinosus, and semimembranosus of hamstrings at 30°, 60°, and 90° of knee flexion during MVCKF. Results: Muscle activity levels in the proximal and middle regions of the BFlh were higher at 30° and 60° of knee flexion than at 90° during MVCKF (all: P < .05). Meanwhile, the activity levels in the distal region of the BFlh were not different among all of the evaluated knee joint angles. In semitendinosus and semimembranosus, the activity levels were higher at 30° and 60° than at 90°, regardless of region (all: P < .05). Conclusion: These findings suggest that the effect of knee joint angle on muscle activity level differs between regions of the BFlh, whereas that is similar among regions of semitendinosus and semimembranosus during MVCKF.

scholarly journals Upper extremity biomechanics in native and non-native signers

Work ◽  
2021 ◽  
pp. 1-9
Author(s):  
Gretchen Roman ◽  
Daniel S. Peterson ◽  
Edward Ofori ◽  
Meghan E. Vidt
Keyword(s):  
Upper Extremity ◽  
Injury Risk ◽  
Muscle Activation ◽  
Joint Angle ◽  
Wrist Flexion ◽  
Upper Trapezius ◽  
Flexion Extension ◽  
Deaf Parents ◽  
Native Signers ◽  
Anterior Posterior

BACKGROUND: Individuals fluent in sign language (signers) born to non-signing, non-deaf parents (non-natives) may have a greater injury risk than signers born to signing, deaf parents (natives). A comprehensive analysis of movement while signing in natives and non-natives has not been completed and could provide insight into the greater injury prevalence of non-natives. OBJECTIVE: The objective of this study was to determine differences in upper extremity biomechanics between non-natives and natives. METHODS: Strength, ‘micro’ rests, muscle activation, ballistic signing, joint angle, and work envelope were captured across groups. RESULTS: Non-natives had fewer rests (p = 0.002) and greater activation (p = 0.008) in non-dominant upper trapezius. For ballistic signing, natives had greater anterior-posterior jerk (p = 0.033) and for joint angle, natives demonstrated greater wrist flexion-extension range of motion (p = 0.040). Natives also demonstrated greater maximum medial-lateral (p = 0.015), and greater minimum medial-lateral (p = 0.019) and superior-inferior (p = 0.027) positions. CONCLUSIONS: We observed that natives presented with more rests and less activation, but greater ballistic tendencies, joint angle, and envelope compared to non-natives. Additional work should explore potential links between these outcomes and injury risk in signers.

scholarly journals Range of motion, neuromechanical, and architectural adaptations to plantar flexor stretch training in humans

2014 ◽  
Vol 117 (5) ◽  
pp. 452-462 ◽  
Author(s):  
A. J. Blazevich ◽  
D. Cannavan ◽  
C. M. Waugh ◽  
S. C. Miller ◽  
J. B. Thorlund ◽  
...  
Keyword(s):  
Range Of Motion ◽  
Muscle Activation ◽  
Joint Angle ◽  
Maximum Voluntary Contraction ◽  
Muscle Length ◽  
Joint Moment ◽  
Muscle Stiffness ◽  
Control Group ◽  
Plantar Flexor ◽  
Passive Joint

The neuromuscular adaptations in response to muscle stretch training have not been clearly described. In the present study, changes in muscle (at fascicular and whole muscle levels) and tendon mechanics, muscle activity, and spinal motoneuron excitability were examined during standardized plantar flexor stretches after 3 wk of twice daily stretch training (4 × 30 s). No changes were observed in a nonexercising control group ( n = 9), however stretch training elicited a 19.9% increase in dorsiflexion range of motion (ROM) and a 28% increase in passive joint moment at end ROM ( n = 12). Only a trend toward a decrease in passive plantar flexor moment during stretch (−9.9%; P = 0.15) was observed, and no changes in electromyographic amplitudes during ROM or at end ROM were detected. Decreases in Hmax:Mmax(tibial nerve stimulation) were observed at plantar flexed (gastrocnemius medialis and soleus) and neutral (soleus only) joint angles, but not with the ankle dorsiflexed. Muscle and fascicle strain increased (12 vs. 23%) along with a decrease in muscle stiffness (−18%) during stretch to a constant target joint angle. Muscle length at end ROM increased (13%) without a change in fascicle length, fascicle rotation, tendon elongation, or tendon stiffness following training. A lack of change in maximum voluntary contraction moment and rate of force development at any joint angle was taken to indicate a lack of change in series compliance of the muscle-tendon unit. Thus, increases in end ROM were underpinned by increases in maximum tolerable passive joint moment (stretch tolerance) and both muscle and fascicle elongation rather than changes in volitional muscle activation or motoneuron pool excitability.

scholarly journals A muscle model for hybrid muscle activation

2015 ◽  
Vol 1 (1) ◽  
pp. 386-389 ◽  
Author(s):  
Christian Klauer ◽  
Maximilian Irmer ◽  
Thomas Schauer
Keyword(s):  
Muscle Activation ◽  
Joint Angle ◽  
Control Strategies ◽  
Muscle Model ◽  
Develop Model ◽  
Hammerstein Model ◽  
Shoulder Abduction ◽  
Individual Subject ◽  
Input Nonlinearity ◽  
Main Component

AbstractTo develop model-based control strategies for Functional Electrical Stimulation (FES) in order to support weak voluntary muscle contractions, a hybrid model for describing joint motions induced by concurrent voluntary-and FES induced muscle activation is proposed. It is based on a Hammerstein model – as commonly used in feedback controlled FES – and exemplarily applied to describe the shoulder abduction joint angle. Main component of a Hammerstein muscle model is usually a static input nonlinearity depending on the stimulation intensity. To additionally incorporate voluntary contributions, we extended the static non-linearity by a second input describing the intensity of the voluntary contribution that is estimated by electromyography (EMG) measurements – even during active FES. An Artificial Neural Network (ANN) is used to describe the static input non-linearity. The output of the ANN drives a second-order linear dynamical system that describes the combined muscle activation and joint angle dynamics. The tunable parameters are adapted to the individual subject by a system identification approach using previously recorded I/O-data. The model has been validated in two healthy subjects yielding RMS values for the joint angle error of 3.56° and 3.44°, respectively.

scholarly journals The Effects of Varying Glenohumeral Joint Angle on Acute Volume Load, Muscle Activation, Swelling, and Echo-Intensity on the Biceps Brachii in Resistance-Trained Individuals

Sports ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 204 ◽  
Author(s):  
Christopher Barakat ◽  
Renato Barroso ◽  
Michael Alvarez ◽  
Jacob Rauch ◽  
Nicholas Miller ◽  
...  
Keyword(s):  
Muscle Activation ◽  
Joint Angle ◽  
Biceps Brachii ◽  
Glenohumeral Joint ◽  
Training Session ◽  
Repeated Measure ◽  
Neutral Position ◽  
Echo Intensity ◽  
Joint Angles ◽  
Volume Load

There is a paucity of data on how manipulating joint angles during isolation exercises may impact overall session muscle activation and volume load in resistance-trained individuals. We investigated the acute effects of varying glenohumeral joint angle on the biceps brachii with a crossover repeated measure design with three different biceps curls. One session served as the positive control (CON), which subjects performed 9 sets of bicep curls with their shoulder in a neutral position. The experimental condition (VAR), varied the glenohumeral joint angle by performing 3 sets in shoulder extension (30°), 3 sets neutral (0°), and 3 sets in flexion (90°). Volume load and muscle activation (EMG) were recorded during the training sessions. Muscle swelling and strain were assessed via muscle thickness and echo-intensity responses at pre, post, 24 h, 48 h, and 72 h. There were no significant differences between conditions for most dependent variables. However, the overall session EMG amplitude was significantly higher (p = 0.0001) in VAR compared to CON condition (95%-CI: 8.4% to 23.3%). Our findings suggest that varying joint angles during resistance training (RT) may enhance total muscle activation without negatively affecting volume load within a training session in resistance-trained individuals.

scholarly journals InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling

2021 ◽  
Vol 15 ◽  
Author(s):  
Ali Nasr ◽  
Keaton A. Inkol ◽  
Sydney Bell ◽  
John McPhee
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Muscle Activation ◽  
Joint Angle ◽  
Learning Model ◽  
Joint Torque ◽  
Static Optimization ◽  
Machine Learning Model ◽  
Joint Velocity ◽  
Joint Acceleration

InverseMuscleNET, a machine learning model, is proposed as an alternative to static optimization for resolving the redundancy issue in inverse muscle models. A recurrent neural network (RNN) was optimally configured, trained, and tested to estimate the pattern of muscle activation signals. Five biomechanical variables (joint angle, joint velocity, joint acceleration, joint torque, and activation torque) were used as inputs to the RNN. A set of surface electromyography (EMG) signals, experimentally measured around the shoulder joint for flexion/extension, were used to train and validate the RNN model. The obtained machine learning model yields a normalized regression in the range of 88–91% between experimental data and estimated muscle activation. A sequential backward selection algorithm was used as a sensitivity analysis to discover the less dominant inputs. The order of most essential signals to least dominant ones was as follows: joint angle, activation torque, joint torque, joint velocity, and joint acceleration. The RNN model required 0.06 s of the previous biomechanical input signals and 0.01 s of the predicted feedback EMG signals, demonstrating the dynamic temporal relationships of the muscle activation profiles. The proposed approach permits a fast and direct estimation ability instead of iterative solutions for the inverse muscle model. It raises the possibility of integrating such a model in a real-time device for functional rehabilitation and sports evaluation devices with real-time estimation and tracking. This method provides clinicians with a means of estimating EMG activity without an invasive electrode setup.

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