Prediction of biceps muscle fatigue and force using electromyography signal analysis for repeated isokinetic dumbbell curl exercise

Hwang, Hyun Jun; Chung, Wan Ho; Song, Joo Ho; Lim, Jong Kwang; Kim, Kwang Ho

doi:10.1007/s12206-016-1053-1

Cited by 22 publications

(14 citation statements)

References 24 publications

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“…The participant performed biceps brachii curls without any load for 5 min. After a 3-min rest, the participant performed a series of biceps curls with different weights of dumbbells for the determination of the weight of 10RM while sitting on a bench ( Hwang et al, 2016 ). Participants were asked “How many more repetitions do you think you can do with this weight?” If the participant could complete more than 10 repetitions, the weight would be increased until the determination of the amount of 10RM for each individual.…”

Section: Methodsmentioning

confidence: 99%

Immediate and Delayed Effects of Cupping Therapy on Reducing Neuromuscular Fatigue

Hou

Wang

Griffin

et al. 2021

Front. Bioeng. Biotechnol.

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Cupping therapy has been popular in elite athletes in recent years. However, the effect of cupping therapy on reducing muscle fatigue has not been investigated. The purpose of this study was to investigate the immediate and delayed effects of cupping therapy on reducing biceps brachii fatigue during biceps curls. Twelve healthy untrained participants were recruited for this repeated-measures study. Cupping therapy (–300 mmHg pressure for 5 min) and sham control (no negative pressure for 5 min) were applied after biceps fatigue induced by performing repeated biceps curls at 75% of the 10 repetitions of maximum of the non-dominant hand. Surface electromyography (EMG) with spectral analyses [mean frequency (MNF), median frequency (MDF), and spectral moments ratio (SMR)] were used to assess muscle fatigue during the fatigue task. EMG signals during the first 10 repetitions and the last 10 repetitions of biceps curls were used to assess neuromuscular fatigue. There were significant decreases in MNF and MDF and a significant increase in SMR immediately and 24 h after the sham control (no intervention). When comparing the MNF, MDF, and SMR after cupping therapy to the sham control, there was no significant immediate effect on reducing muscle fatigue. However, there was a significant delayed effect on improving recovery following fatigue for the cupping therapy compared to the sham control (MNF changes: sham 0.87 ± 0.02 vs. cupping 0.91 ± 0.02, p < 0.05; MDF changes sham: 0.85 ± 0.03 vs. cupping: 0.91 ± 0.02, p < 0.05; SMR changes: sham 1.89 ± 0.15 vs. cupping 1.58 ± 0.13, p < 0.05). The findings of this study demonstrate that there is a time effect of cupping therapy for reducing muscle fatigue. Cupping therapy is effective on reducing biceps brachii muscle fatigue after 24 h.

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Section: Methodsmentioning

confidence: 99%

Immediate and Delayed Effects of Cupping Therapy on Reducing Neuromuscular Fatigue

Hou

Wang

Griffin

et al. 2021

Front. Bioeng. Biotechnol.

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“…The first visit was aimed to determine the ten-repetition maximum (10 RM). After a 5-min warm-up (biceps curls without load) followed by a 3-min rest, the subject performed biceps curls using dumbbells with different weights during several trials [24]. In the first trial, the initial weight was adjusted according to the subject's estimation.…”

Section: Methodsmentioning

confidence: 99%

Effects of Muscle Fatigue and Recovery on Complexity of Surface Electromyography of Biceps Brachii

Liao

Zhang

Cao

et al. 2021

Entropy

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This study aimed to investigate the degree of regularity of surface electromyography (sEMG) signals during muscle fatigue during dynamic contractions and muscle recovery after cupping therapy. To the best of our knowledge, this is the first study assessing both muscle fatigue and muscle recovery using a nonlinear method. Twelve healthy participants were recruited to perform biceps curls at 75% of the 10 repetitions maximum under four conditions: immediately and 24 h after cupping therapy (−300 mmHg pressure), as well as after sham control (no negative pressure). Cupping therapy or sham control was assigned to each participant according to a pre-determined counter-balanced order and applied to the participant’s biceps brachii for 5 min. The degree of regularity of the sEMG signal during the first, second, and last 10 repetitions (Reps) of biceps curls was quantified using a modified sample entropy (Ems) algorithm. When exercise was performed immediately or 24 h after sham control, Ems of the sEMG signal showed a significant decrease from the first to second 10 Reps; when exercise was performed immediately after cupping therapy, Ems also showed a significant decrease from the first to second 10 Reps but its relative change was significantly smaller compared to the condition of exercise immediately after sham control. When exercise was performed 24 h after cupping therapy, Ems did not show a significant decrease, while its relative change was significantly smaller compared to the condition of exercise 24 h after sham control. These results indicated that the degree of regularity of sEMG signals quantified by Ems is capable of assessing muscle fatigue and the effect of cupping therapy. Moreover, this measure seems to be more sensitive to muscle fatigue and could yield more consistent results compared to the traditional linear measures.

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“…We use the 4.5 kg weight dumbbell in our work because of three reasons. The first reason is that several previous works have used medium-weight dumbbells ranging between 3.5 kg and 5.5 kg to study muscular strength and fatigue [51][52][53]. The second reason is that medium-weight dumbbells are often reported as the most commonly used dumbbells across gym-goers [54].…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…In contrast, heavyweight dumbbells lead to shorter recording sessions with fewer data entries, which do not capture kinetic changes clearly throughout the exercise because participants reach fatigue quickly. Although we use a 4.5 kg weight dumbbell as recommended by previous studies, we believe having dumbbell weights will provide us with more information and different patterns of biceps muscle fatigue [51][52][53].…”

Section: Work Limitationsmentioning

confidence: 99%

Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd

Elshafei

Costa

Shihab

2022

Sensors

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Nowadays, wearables-based Human Activity Recognition (HAR) systems represent a modern, robust, and lightweight solution to monitor athlete performance. However, user data variability is a problem that may hinder the performance of HAR systems, especially the cross-subject HAR models. Such a problem may have a lesser effect on the subject-specific model because it is a tailored model that serves a specific user; hence, data variability is usually low, and performance is often high. However, such a performance comes with a high cost in data collection and processing per user. Therefore, in this work, we present a personalized model that achieves higher performance than the cross-subject model while maintaining a lower data cost than the subject-specific model. Our personalization approach sources data from the crowd based on similarity scores computed between the test subject and the individuals in the crowd. Our dataset consists of 3750 concentration curl repetitions from 25 volunteers with ages and BMI ranging between 20–46 and 24–46, respectively. We compute 11 hand-crafted features and train 2 personalized AdaBoost models, Decision Tree (AdaBoost-DT) and Artificial Neural Networks (AdaBoost-ANN), using data from whom the test subject shares similar physical and single traits. Our findings show that the AdaBoost-DT model outperforms the cross-subject-DT model by 5.89%, while the AdaBoost-ANN model outperforms the cross-subject-ANN model by 3.38%. On the other hand, at 50.0% less of the test subject’s data consumption, our AdaBoost-DT model outperforms the subject-specific-DT model by 16%, while the AdaBoost-ANN model outperforms the subject-specific-ANN model by 10.33%. Yet, the subject-specific models achieve the best performances at 100% of the test subjects’ data consumption.

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Prediction of biceps muscle fatigue and force using electromyography signal analysis for repeated isokinetic dumbbell curl exercise

Cited by 22 publications

References 24 publications

Immediate and Delayed Effects of Cupping Therapy on Reducing Neuromuscular Fatigue

Immediate and Delayed Effects of Cupping Therapy on Reducing Neuromuscular Fatigue

Effects of Muscle Fatigue and Recovery on Complexity of Surface Electromyography of Biceps Brachii

Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd

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