scholarly journals Physiological complexity: influence of ageing, disease and neuromuscular fatigue on muscle force and torque fluctuations

2021 ◽  
Vol 106 (10) ◽  
pp. 2046-2059
Author(s):  
Jamie Pethick ◽  
Samantha L. Winter ◽  
Mark Burnley
Keyword(s):  
Muscle Force ◽  
Neuromuscular Fatigue ◽  
Physiological Complexity

Predictive Neuromuscular Fatigue of the Lower Extremity Utilizing Computer Modeling

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Michael A. Samaan ◽  
Joshua T. Weinhandl ◽  
Steven A. Hans ◽  
Sebastian Y. Bawab ◽  
Stacie I. Ringleb
Keyword(s):  
Lower Extremity ◽  
Muscle Force ◽  
Cruciate Ligament ◽  
Force Production ◽  
Neuromuscular Fatigue ◽  
Lower Extremity Injury ◽  
Muscle Forces ◽  
Flexor Muscle ◽  
Lower Extremity Muscle ◽  
Muscle Force Production

This paper studies the modeling of lower extremity muscle forces and their correlation to neuromuscular fatigue. Two analytical fatigue models were combined with a musculoskeletal model to estimate the effects of hamstrings fatigue on lower extremity muscle forces during a side step cut. One of the fatigue models (Tang) used subject-specific knee flexor muscle fatigue and recovery data while the second model (Xia) used previously established fatigue and recovery parameters. Both fatigue models were able to predict hamstrings fatigue within 20% of the experimental data, with the semimembranosus and semitendinosus muscles demonstrating the largest (11%) and smallest (1%) differences, respectively. In addition, various hamstrings fatigue levels (10–90%) on lower extremity muscle force production were assessed using one of the analytical fatigue models. As hamstrings fatigue levels increased, the quadriceps muscle forces decreased by 21% (p < 0.01), while gastrocnemius muscle forces increased by 36% (p < 0.01). The results of this study validate the use of two analytical fatigue models in determining the effects of neuromuscular fatigue during a side step cut, and therefore, this model can be used to assess fatigue effects on risk of lower extremity injury during athletic maneuvers. Understanding the effects of fatigue on muscle force production may provide insight on muscle group compensations that may lead to altered lower extremity motion patterns as seen in noncontact anterior cruciate ligament (ACL) injuries.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

scholarly journals Differences in strength and landing biomechanics between female jumpers and swimmers

2021 ◽  
pp. 1-11
Author(s):  
Mianfang Ruan ◽  
Qiang Zhang ◽  
Xin Zhang ◽  
Jing Hu ◽  
Xie Wu
Keyword(s):  
Muscle Force ◽  
Female Athletes ◽  
Young Female ◽  
Single Component ◽  
Plyometric Training ◽  
Isokinetic Testing ◽  
Female College ◽  
Landing Biomechanics ◽  
Drop Landing ◽  
Landing Mechanics

BACKGROUND: It remains unclear if plyometric training as a single component could improve landing mechanics that are potentially associated with lower risk of ACL injury in the long term OBJECTIVE: The purpose of this study was to investigate the influence of experience undertaking plyometrics on landing biomechanics in female athletes. METHODS: Non-jumpers with little experience in plyometric training (12 female college swimmers) and jumpers with five years of experience in plyometric training (12 female college long jumpers and high jumpers) were recruited to participate in two testing sessions: an isokinetic muscle force test for the dominant leg at 120∘/s and a 40-cm drop landing test. An independent t test was applied to detect any significant effects between cohorts for selected muscle force, kinematic, kinetic, and electromyography variables. RESULTS: While female jumpers exhibited greater quadriceps eccentric strength (P= 0.013) and hamstring concentric strength (P= 0.023) during isokinetic testing than female swimmers, no significant differences were observed in kinematics, kinetics, and muscle activities during both drop landing and drop jumping. CONCLUSIONS: The results suggest that the female jumpers did not present any training-induced modification in landing mechanics regarding reducing injury risks compared with the swimmers. The current study revealed that plyometric training as a single component may not guarantee the development of low-risk landing mechanics for young female athletes.

scholarly journals What Happened with Muscle Force, Dynamic Stability And Falls?- a 10-Year Longitudinal Follow-Up in Adults with Myotonic Dystrophy Type 1

2021 ◽  
pp. 1-10
Author(s):  
Elisabet Hammarén ◽  
Lena Kollén
Keyword(s):  
Myotonic Dystrophy ◽  
Dynamic Stability ◽  
Muscle Force ◽  
Step Test ◽  
Maximum Speed ◽  
Myotonic Dystrophy Type 1 ◽  
Myotonic Dystrophy Type ◽  
Patient Reported ◽  
Leg Muscle

Background: Individuals with myotonic dystrophy type 1 (DM1) are known to stumble and fall, but knowledge is scarce regarding dynamic stability in this disorder. Objective: To describe disease progress regarding muscle force, dynamic stability and patient reported unintentional falls during a ten-year period, in individuals with DM1. Methods: Quantification of isometric muscle force in four leg muscle groups and assessment of Timed 10-meter-walk in maximum speed (T10max), Timed Up&Go (TUG) and Step test (STEP) were performed at three occasions in a DM1 cohort, together with self-reported falls. Results: Thirty-four people (m/f:11/23, age:50.2 + /–9.4) participated. The muscle force loss after ten years was large in the distal ankle muscles. A steeper force decrease was seen in most muscles between year five and ten compared to the former five-year period. Males reported more falls than females, 91%vs 35%had fallen last year. A positive correlation, ρ= 0.633, p <  0.001, was shown between walking time (T10max) and number of falls. Frequent fallers were only seen among those with slower walk (T10max >  10seconds), and fewer steps in the STEP test (STEP≤5 steps). Conclusions: A diminishing leg muscle strength and worse dynamic stability were seen in the group, with a steeper decrease in the latter five years. Weak ankle dorsiflexors, a slower walk and difficulties to lift the forefoot were related to frequent falls.

Perceived physical exertion is a good indicator of neuromuscular fatigue for the core muscles

2019 ◽  
Vol 49 ◽  
pp. 102360 ◽  
Author(s):  
Carlos Cruz-Montecinos ◽  
Alejandro Bustamante ◽  
Macarena Candia-González ◽  
Carolina González-Bravo ◽  
Paula Gallardo-Molina ◽  
...  
Keyword(s):  
Physical Exertion ◽  
Neuromuscular Fatigue ◽  
The Core
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