Muscle fatigue compensation of the electromyography signal for elbow joint angle estimation using adaptive feature

Triwiyanto, Triwiyanto; Wahyunggoro, Oyas; Nugroho, Hanung Adi; Herianto, Herianto

doi:10.1016/j.compeleceng.2018.07.026

Cited by 42 publications

(24 citation statements)

References 16 publications

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“…The median frequency is defined as the frequency that divided the spectrum into two equal areas. It has been widely used in the studies related to muscle fatigue ( 27 , 28 ). The median frequency of sEMG during different sessions were calculated and compared to indicate the fatigue states in this study.…”

Section: Methodsmentioning

confidence: 99%

Increased Corticomuscular Coherence and Brain Activation Immediately After Short-Term Neuromuscular Electrical Stimulation

Wang

et al. 2018

Front. Neurol.

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Neuromuscular Electrical Stimulation (NMES) is commonly used in motor rehabilitation for stroke patients. It has been verified that NMES can improve muscle strength and activate the brain, but the studies on how NMES affects the corticomuscular connection are limited. Some studies found an increased corticomuscular coherence (CMC) after a long-term NMES. However, it is still unknown about CMC during NMES, as relatively pure EMG is very difficult to obtain with the contamination of NMES current pulses. In order to approach the condition during NMES, we designed an experiment with short-term NMES and immediately captured data within 100 s. The repetition of wrist flexion was used to realize static muscle contractions for CMC calculation and dynamic contractions for event-related desynchronization (ERD). The result of 13 healthy participants showed that maximal values (p = 0.0020) and areas (p = 0.0098) of CMC and beta ERD were significantly increased immediately after NMES. It was concluded that a short-term NMES can still reinforce corticomuscular functional connection and brain activation related to motor task. This study verified the immediate strengthen of corticomuscular changes after NMES, which was expected to be the basis of long-term neural plasticity induced by NMES.

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Section: Methodsmentioning

confidence: 99%

Increased Corticomuscular Coherence and Brain Activation Immediately After Short-Term Neuromuscular Electrical Stimulation

Wang

et al. 2018

Front. Neurol.

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“…The reason for such a dilemma is fatigue affects the collected data; its impacts will extend to the extracted features from the same data. A previous work [ 63 ] shows that muscle fatigue affects the electromyography (EMG) data signals collected from biceps by increasing the Root Mean Square Error (RMSEs), leading to the misclassification of some activities.…”

Section: Discussionmentioning

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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“…In RQ2, we investigated the effects of the data pattern changes, associated with fatigue, in the feature extraction outcome. Previous work [ 40 ] shows that muscle fatigue affects the Electromyography (EMG) data signals collected from biceps by increasing the Root Mean Square Error (RMSEs) of extracted features. Similarly, our findings show that fatigue can hinder the correlation values of some of the extracted features to the extent of turning them into insignificant features.…”

Section: Discussionmentioning

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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Muscle fatigue compensation of the electromyography signal for elbow joint angle estimation using adaptive feature

Cited by 42 publications

References 16 publications

Increased Corticomuscular Coherence and Brain Activation Immediately After Short-Term Neuromuscular Electrical Stimulation

Increased Corticomuscular Coherence and Brain Activation Immediately After Short-Term Neuromuscular Electrical Stimulation

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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