Musculoskeletal modelling based estimates of load dependent relative muscular effort during resistance training exercises

2021 ◽  
pp. 1-11
Author(s):  
William I. Wolf ◽  
Hoon Kim ◽  
Kristof Kipp
2013 ◽  
Vol 25 (3) ◽  
pp. 67 ◽  
Author(s):  
K Nolte ◽  
PE Krüger ◽  
PS Els

Objective. To evaluate whether three-dimensional (3D) musculoskeletal modelling could be effective in assessing the safety and efficacy of exercising on a seated row resistance-training machine. The focus of the evaluation was on biomechanical and anthropometric considerations of the end user.Methods. Three anthropometric cases were created; these represented a 5th percentile female as well as a 50th and a 95th percentile male based on body mass index. Two repetitions, with a resistance equal to 50% of the functional strength of one repetition maximum (1RM) for each anthropometric case, were performed.Results. Results indicate that the default model of the LifeModeler software has important limitations that should be taken into consideration when used to evaluate exercise equipment. Adjustments had to be made to the model to solve the forward dynamics simulations; as a result, no muscle forces or contraction values were obtained. This negatively influenced the value of the results as these parameters are important when analysing an exercise. The seated row resistance-training machine’s engineered or manufactured adjustability was sufficient, as it appeared to accommodate the three anthropometric cases adequately during execution of this exercise.Conclusion. It appears that 3D musculoskeletal modelling can be used to evaluate resistance-training exercises such as the seated row; however, the limitations indicated by this study must be taken into consideration, especially when using the default LifeModeler model


2013 ◽  
Vol 25 (3) ◽  
pp. 67
Author(s):  
K Nolte ◽  
PE Krüger ◽  
PS Els

Objective. To evaluate whether three-dimensional (3D) musculoskeletal modelling could be effective in assessing the safety and efficacy of exercising on a seated row resistance-training machine. The focus of the evaluation was on biomechanical and anthropometric considerations of the end user.Methods. Three anthropometric cases were created; these represented a 5th percentile female as well as a 50th and a 95th percentile male based on body mass index. Two repetitions, with a resistance equal to 50% of the functional strength of one repetition maximum (1RM) for each anthropometric case, were performed.Results. Results indicate that the default model of the LifeModeler software has important limitations that should be taken into consideration when used to evaluate exercise equipment. Adjustments had to be made to the model to solve the forward dynamics simulations; as a result, no muscle forces or contraction values were obtained. This negatively influenced the value of the results as these parameters are important when analysing an exercise. The seated row resistance-training machine’s engineered or manufactured adjustability was sufficient, as it appeared to accommodate the three anthropometric cases adequately during execution of this exercise.Conclusion. It appears that 3D musculoskeletal modelling can be used to evaluate resistance-training exercises such as the seated row; however, the limitations indicated by this study must be taken into consideration, especially when using the default LifeModeler model


2017 ◽  
Vol 8 ◽  
Author(s):  
Carlos Balsalobre-Fernández ◽  
David Marchante ◽  
Eneko Baz-Valle ◽  
Iván Alonso-Molero ◽  
Sergio L. Jiménez ◽  
...  

2020 ◽  
Vol 258 (8) ◽  
pp. 1795-1801
Author(s):  
Jesús Vera ◽  
Beatríz Redondo ◽  
Alejandro Perez-Castilla ◽  
Raimundo Jiménez ◽  
Amador García-Ramos

2019 ◽  
Vol 2 (4) ◽  
pp. 247-255
Author(s):  
Scott A. Conger ◽  
Alexander H.K. Montoye ◽  
Olivia Anderson ◽  
Danielle E. Boss ◽  
Jeremy A. Steeves

Speed of movement has been shown to affect the validity of physical activity (PA) monitors during locomotion. Speed of movement may also affect the validity of accelerometer-based PA monitors during other types of exercise. Purpose: To assess the ability of the Atlas Wearables Wristband2 (a PA monitor developed specifically for resistance training [RT] exercise) to identify the individual RT exercise type and count repetitions during RT exercises at various movement speeds. Methods: 50 male and female participants completed seven sets of 10 repetitions for five different upper/lower body RT exercises while wearing a Wristband2 on the left wrist. The speed of each set was completed at different metronome-paced speeds ranging from a slow speed of 4 sec·rep−1 to a fast speed of 1 sec·rep−1. Repeated Measures ANOVAs were used to compare the actual exercise type/number of repetitions among the seven different speeds. Mean absolute percent error (MAPE) and bias were calculated for repetition counting. Results: For each exercise, there tended to be significant differences between the slower speeds and the fastest speed for activity type identification and repetition counting (p < .05). Across all exercises, the highest accuracy for activity type identification (91 ± 1.8% correct overall), repetition counting (8.77 ± 0.17 of 10 reps overall) and the lowest MAPE (14 ± 1.7% overall) and bias (−1.23 ± 0.17 reps overall) occurred during the 1.5 sec·rep−1 speed (the second fastest speed tested). Conclusions: The validity of the Atlas Wearables Wristband2 to identify exercise type and count repetitions varied based on the speed of movement during RT exercises.


2019 ◽  
Vol 2 (4) ◽  
pp. 218-227
Author(s):  
Jeremy A. Steeves ◽  
Scott A. Conger ◽  
Joe R. Mitrzyk ◽  
Trevor A. Perry ◽  
Elise Flanagan ◽  
...  

Background: Devices for monitoring physical activity have focused mainly on measuring aerobic activity; however, the 2018 Physical Activity Guidelines for Americans also recommend muscle-resistance training two or more days per week. Recently, a wrist-worn activity monitor, the Atlas Wristband2, was developed to recognize resistance training exercises. Purpose: To assess the ability of the Wristband2 to identify the type and number of repetitions of resistance training exercises, when worn on the left wrist as directed by the manufacturer, and when worn on the right wrist. Methods: While wearing monitors on both wrists, 159 participants completed a circuit-style workout consisting of two sets of 12 repetitions of 14 different resistance training exercises. Data from the monitors were used to determine classification accuracies for identifying exercise type verses direct observation. The average repetitions and mean absolute error (MAE) for repetitions were calculated for each exercise. Results: The Wristband2 classification accuracy for exercise type was 78.4 ± 2.5%, ranging from 54.7 ± 3.4% (dumbbell [DB] bench press) to 97.5 ± 1.0% (DB biceps curls), when worn on the left wrist. An average of 11.0 ± 0.2 repetitions, ranging from 9.0 ± 0.3 repetitions (DB lunges) to 11.9 ± 0.1 repetitions (push-ups), were identified. For all exercises, MAE ranged from 0.0–4.6 repetitions. When worn on the right wrist, exercise type classification accuracy dropped to 24.2 ± 5.1%, and repetitions decreased to 8.1 ± 0.8 out of 12. Conclusions: The Wristband2, worn on the left wrist, had acceptable exercise classification and repetition counting capabilities for many of the 14 exercises used in this study, and may be a useful tool to objectively track resistance training.


2020 ◽  
Vol 34 (6) ◽  
pp. 1519-1524
Author(s):  
Jonathon Weakley ◽  
Daniel Chalkley ◽  
Rich Johnston ◽  
Amador García-Ramos ◽  
Andrew Townshend ◽  
...  

2014 ◽  
Vol 119 (1) ◽  
pp. 133-145 ◽  
Author(s):  
Alex S. Ribeiro ◽  
Marcelo Romanzini ◽  
Brad J. Schoenfeld ◽  
Mariana F. Souza ◽  
Ademar Avelar ◽  
...  

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