Effects of muscle fatigue on the usability of a myoelectric human-computer interface

Barszap, Alexander G.; Skavhaug, Ida-Maria; Joshi, Sanjay S.

doi:10.1016/j.humov.2016.06.009

Cited by 15 publications

(4 citation statements)

References 32 publications

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“…Constant exercise load results in the rise of EMG activity during fatigue ( Chlif et al, 2018 ). Electrical currents generated by muscle contractions can be monitored in the form of EMG signals and displayed on a computer ( Barszap et al, 2016 ). Bioelectricity is detectable when muscle contracts ( Tang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Application of Surface Electromyography in Exercise Fatigue: A Review

Sun

Liu

Lin

et al. 2022

Front. Syst. Neurosci.

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Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

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Section: Introductionmentioning

confidence: 99%

Application of Surface Electromyography in Exercise Fatigue: A Review

Sun

Liu

Lin

et al. 2022

Front. Syst. Neurosci.

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“…In the development of an advanced human–robot interface, user satisfaction should be investigated to determine the optimal control configuration. Several studies have evaluated interface usability [ 1 , 2 , 3 ] and user satisfaction [ 4 ]. For an individualized interface, the system must recognize the reactions of the user to its responses.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalography Reflects User Satisfaction in Controlling Robot Hand through Electromyographic Signals

Kim

Miyakoshi

Kim

et al. 2022

Sensors

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This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were exposed to different experiences as the cutoff frequencies of a low-pass filter were changed. The participants attempted to grab a bottle by controlling a robot. They were asked to evaluate four indicators (stability, imitation, response time, and movement speed) and indicate their satisfaction at the end of each trial by completing a questionnaire. The electroencephalography signals of the participants were recorded while they controlled the robot and responded to the questionnaire. Two independent component clusters in the precuneus and postcentral gyrus were the most sensitive to subjective evaluations. For the moment that dominated satisfaction, we observed that brain activity exhibited significant differences in satisfaction not immediately after feeding an input but during the later stage. The other indicators exhibited independently significant patterns in event-related spectral perturbations. Comparing these indicators in a low-frequency band related to the satisfaction with imitation and movement speed, which had significant differences, revealed that imitation covered significant intervals in satisfaction. This implies that imitation was the most important contributing factor among the four indicators. Our results reveal that regardless of subjective satisfaction, objective performance evaluation might more fully reflect user satisfaction.

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“…Hence, there are some previous researchers were associated fatigue or exercise to the heart rate variability [3,4], blood pressure [5], respiratory rate [6], electromyogram [7,8], and mechanomyogram [9]. On the other hand, there has been in recent years, an increasing amount of literature on detecting fatigue by using the EMG monitoring with signal processing analysis [10][11][12][13], computer control [14] and statistical [15]. However, in terms of oxygen circulation to supply to working muscle, the other physiological systems such as respiratory and cardiovascular or called as cardio respiratory systems are…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network

Ahmad

2018

JPFMTS

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Exhaustion or extreme of fatigue is the highest condition of body performance during exercise. This state presents an optimum energy to execute by an athlete before their level of fitness reduced and required the recovery process. The purpose of this study is to monitor and predict an exhaustion threshold from three physiological systems; respiratory, cardiovascular and muscular by using artificial neural network. A developed wearable device to measure those parameters is needed for the data collection in fatigue experiment protocol. Then, it was separated into its category and filtering that signal to remove all unwanted noise in the database. Statistical feature extraction was executed for divided into five levels of exhaustion to implement supervised machine learning method. A mathematical model for prediction was developed in artificial neural network based on the data obtained from the exhaustion threshold. This model can facilitate the coach and athlete to monitor their level of exhaustion as well as prevent from the severe injury due to over exercise.

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Effects of muscle fatigue on the usability of a myoelectric human-computer interface

Cited by 15 publications

References 32 publications

Application of Surface Electromyography in Exercise Fatigue: A Review

Application of Surface Electromyography in Exercise Fatigue: A Review

Electroencephalography Reflects User Satisfaction in Controlling Robot Hand through Electromyographic Signals

Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network

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