Relationship between cardiopulmonary responses and isokinetic moments: the optimal angular velocity for muscular endurance

Lee, Chan-Bok; Eun, Denny; Kim, Kangho; Park, Jae-Wan; Jee, Yong-Seok

doi:10.12965/jer.1734942.471

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…In this context, the results of the total work corrected for body mass were significantly lower in deaf players, suggesting that lower values of total work may result in reduced cardiorespiratory fitness, measured by an incremental exercise test. Recently, the association between aerobic performance and total work was observed by other researchers (Lee et al, 2017). Furthermore, a significant relationship between functional tests and isokinetic muscle strength was found in soccer players and futsal players (de Lira et al, 2017).…”

Section: Discussionmentioning

confidence: 67%

“…Our study found that higher absolute PT values of knee flexors and extensors are associated with higher VO 2max values. Moreover, we found a correlation with the total work of the knee extensors, which is the energy used for muscular effort during movement, i.e., the product of torque by angular displacement (Lee et al, 2017). In this context, the results of the total work corrected for body mass were significantly lower in deaf players, suggesting that lower values of total work may result in reduced cardiorespiratory fitness, measured by an incremental exercise test.…”

Section: Discussionmentioning

confidence: 92%

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Cardiorespiratory fitness and muscular strength of deaf futsal players

et al. 2019

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The present study compared the cardiopulmonary exercise capacity and peak torque of the knee extensor and flexor muscles in team players associated with the Gaucho Deaf Futsal Federation players to those of their hearing peers. In this cross-sectional study, 16 male athletes, eight futsal players with hearing impairment (deaf group, DG; 22.6± 7.7 years), and eight futsal players with normal hearing (control group, CG; 22.5± 2.9 years) underwent a cardiopulmonary test on a treadmill and isokinetic dynamometry at 60°/sec. All athletes were subjected to a cardiopulmonary test on a treadmill and isokinetic dynamometry at 60°/ sec. The main results showed a reduction in the cardiorespiratory fitness of deaf athletes when compared to the control group maximal oxygen consumption (VO2max) (40.3± 9.8 mL/kg/min vs. 50.7± 4.7 mL/kg/min, P= 0.01), oxygen pulse (15.3± 4.8 mL/bpm vs. 20.7± 2.6 mL/bpm, P = 0.01) and ventilation (70.1± 22 L/min vs. 96.2± 15 L/min, P= 0.01), respectively. The relative torque peak of the dominant knee flexors was significantly lower in the deaf group when compared to the control (1.5± 0.2 N.m/kg vs. 1.9± 0.2 N.m/kg, P= 0.004), respectively. There was a significant correlation between VO2max and peak torque of the dominant knee flexors (rs = 0.83, P< 0.001) and extensors (rs = 0.65, P= 0.006). When compared to players with normal hearing, deaf players showed lower cardiorespiratory fitness and decreased knee flexor strength. The performance of the thigh muscles was associated with aerobic capacity.

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Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 92%

Cardiorespiratory fitness and muscular strength of deaf futsal players

et al. 2019

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“…The axes reported in Table 2 and Table 3 represent the following: (1) the X-axis represents vertical displacement, which is the distance between the highest and lowest positions of the volunteer’s hand during bicep extension and flexion; (2) the Y-axis represents horizontal displacement, which is the sideways vibration of the volunteer’s hand during bicep extension and flexion; (3) the Z-axis represents depth displacement, which represent the farthest and nearest positions of the volunteer’s hand from their body during bicep extension and flexion. Figure 3 shows the data pattern changes along the horizontal axis, which indicates the changes in completion time, whereas the vertical axis indicates the changes in angular velocity according to muscular endurance [ 45 ]. As time passed by, we observed the loss in muscle endurance and angular velocity during fatigue presence using the gyroscope and the accelerometer.…”

Section: Methods: Data Processing and Experiments Setupmentioning

confidence: 99%

Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables

Elshafei

Shihab

2021

Sensors

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Fatigue is a naturally occurring phenomenon during human activities, but it poses a bigger risk for injuries during physically demanding activities, such as gym activities and athletics. Several studies show that bicep muscle fatigue can lead to various injuries that may require up to 22 weeks of treatment. In this work, we adopt a wearable approach to detect biceps muscle fatigue during a bicep concentration curl exercise as an example of a gym activity. Our dataset consists of 3000 bicep curls from twenty middle-aged volunteers at ages between 27 to 30 and Body Mass Index (BMI) ranging between 18 to 28. All volunteers have been gym-goers for at least 1 year with no records of chronic diseases, muscle, or bone surgeries. We encountered two main challenges while collecting our dataset. The first challenge was the dumbbell’s suitability, where we found that a dumbbell weight (4.5 kg) provides the best tradeoff between longer recording sessions and the occurrence of fatigue on exercises. The second challenge is the subjectivity of RPE, where we average the reported RPE with the measured heart rate converted to RPE. We observed from our data that fatigue reduces the biceps’ angular velocity; therefore, it increases the completion time for later sets. We extracted a total of 33 features from our dataset, which have been reduced to 16 features. These features are the most overall representative and correlated with bicep curl movement, yet they are fatigue-specific features. We utilized these features in five machine learning models, which are Generalized Linear Models (GLM), Logistic Regression (LR), Random Forests (RF), Decision Trees (DT), and Feedforward Neural Networks (FNN). We found that using a two-layer FNN achieves an accuracy of 98% and 88% for subject-specific and cross-subject models, respectively. The results presented in this work are useful and represent a solid start for moving into a real-world application for detecting the fatigue level in bicep muscles using wearable sensors as we advise athletes to take fatigue into consideration to avoid fatigue-induced injuries.

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“…We look for data pattern changes along the horizontal axis which indicates the changes in completion time whereas the vertical axis indicates the changes in angular velocity according to muscular endurance [ 36 ]. To analyze the data pattern changes in the horizontal axis, we measured the time required to complete the first set of biceps repetitions for each volunteer, which is the time interval from the 1st repetition until the end of the 15th repetition.…”

Section: Experiments Evaluationmentioning

confidence: 99%

“…Then, we calculate the difference in completion time between each set compared to the first set. To analyze the data pattern changes in the vertical axis, we measured the absolute magnitude of each repetition in the first set to calculate the muscular endurance [ 36 ] during the first set of biceps repetitions. We repeated the same approach to measure the muscular endurance for the remaining sets separately.…”

Section: Experiments Evaluationmentioning

confidence: 99%

On the Impact of Biceps Muscle Fatigue in Human Activity Recognition

Elshafei

Costa

Shihab

2021

Sensors

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Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems’ performance. In this work, we use the biceps concentration curls exercise as an example of a HAR activity to observe the impact of fatigue impact on such systems. Our dataset consists of 3000 biceps concentration curls performed and collected from 20 volunteers aged between 20–35. Our findings indicate that fatigue often occurs in later sets of an exercise and extends the completion time of later sets by up to 31% and decreases muscular endurance by 4.1%. Another finding shows that changes in data patterns are often occurring during fatigue presence, causing seven features to become statistically insignificant. Further findings indicate that fatigue can cause a substantial decrease in performance in both subject-specific and cross-subject models. Finally, we observed that a Feedforward Neural Network (FNN) showed the best performance in both cross-subject and subject-specific models in all our evaluations.

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Relationship between cardiopulmonary responses and isokinetic moments: the optimal angular velocity for muscular endurance

Cited by 5 publications

References 17 publications

Cardiorespiratory fitness and muscular strength of deaf futsal players

Cardiorespiratory fitness and muscular strength of deaf futsal players

Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables

On the Impact of Biceps Muscle Fatigue in Human Activity Recognition

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