Application of Surface Electromyography in the Estimate of Neural-muscle Function (review)
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The surface electromyography (sEMG) is the noninvasive method which can record and measure the changes of local muscle activities. The sEMG's amplitude and frequence signal will change with muscular movement. sEMG application in present study on muscle fatigue is a reliable predictor of muscle functional level. The researches on the sEMG signals changes of limb muscles of hemiplegic patients will take a important role in providing scientific evidence for the neural rehabilitation training after stroke.Keywords:
Muscle Fatigue
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This chapter focuses on the basics of surface electromyography (sEMG) and related methods for the study of human motor control and its adaptations. Recordings of voluntary sEMG provide partial information on the mechanisms involved in muscle activity. However, the combination of sEMG and methods based on electrically and magnetically evoked potentials allows stepping further in a comprehensive approach of movement strategies during tasks, and the influence of various factors such as etiology of cramps, fatigue, training, aging on such strategies. The recorded sEMG signal represents the electrical activity of numerous motor units. The chapter describes the basic methodology to record H-reflex and the relevant factors to consider when assessing its modulations. sEMG and evoked potentials provide relevant information on age-related changes within the muscle and the nervous system, as well as on the neural adjustments required to perform various motor tasks.
Neurophysiology
Muscle Fatigue
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Wearable technology had set to play a major role in solving various problems in medical or sports applications in sports training, ergonomics, and mostly physical therapy. Surface electromyography (sEMG) has been used for localised muscle fatigue research area, although the success of sEMG based techniques is currently limited to isometric contraction and is not acceptable to the human movement community. This work propose the use of sEMG based sensor to measure the muscle activity during dynamic contractions. In this study, the use of a low cost commercial available sensor called MYO armband was implemented to reveal the viability to utilise this type of sensor to quantify muscle fatigue. The acquired sEMG signals from the muscle were analysed with the use of a set of selected features, RMS and MDF have been used as indicators of fatigue, and then classifying the signal into two classes non-fatigue and fatigue using backpropagation neural networks, allowing the prediction of muscle fatigue. Results show that automating the process of localised muscle fatigue detection is showed with higher accuracy compared with other methods.
Muscle Fatigue
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EMG is the only one that can be connected with the nervous system,and can reflect the signal carrier directly during muscle contraction.In this paper,by using the method of literature we make a brief review on the application of surface electromyography in the research field of science of sports,in order to understand the working mechanism of the nerve-muscle system.Results showed that:Study of surface electromyography technology involved in science of sports was still in primary stage,but the application was extremely extensive,mainly as follows:used for fatigue evaluation of muscle movement,the relationship between EMG and muscle strength,muscle coordination of activities and contribution degree and the selection of athletes etc.
Muscle Fatigue
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It is of great significance to detect and predict muscle fatigue for avoiding muscle injury and the negative effects on human-machine interface. This paper presents a novel approach to obtain reliable information about muscle fatigue by analyzing surface electromyography (sEMG) and near-infrared spectroscopy (NIRS) simultaneously. Muscle fatigue was induced via sustained isometric contraction at 50% of maximal voluntary contraction (MVC) force with six subjects. Four fatigue metrics, root mean square (RMS) and median frequency (MDF) of sEMG, blood volume (BV) and muscle oxygenation (ΔHbO 2 ) extracted from NIRS, were proposed to measure muscle fatigue from the perspective of electrophysiology and hemodynamics respectively. The experimental results suggested that sEMG and NIRS applied together could provide more detailed and reliable information about muscle fatigue, gaining a better understanding of fatigue process.
Muscle Fatigue
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Surface electromyography(sEMG) measurement has been an essential approach to analyze human behaviors because we can generally consider that sEMG signals represent the muscle activities as the final output of our nerve system. One of the most serious problems for considering sEMG signal as the muscle activity is the shift of the relative position between muscles and skin depending on a posture. The motion of forearm rotation is the prominent example of muscle-skin shifting depending on postural changes. The sEMG signal from a sensor may represent the different muscle activity when the muscle-skin shifting is happened. In this study, we discuss a method to quantify the muscle-skin shift from the sEMG signals in response to the postural changes. We use the high density sEMG sensor that is possible to measure sEMG signal as the potential map. We proposed the computation algorithm to quantify the amount of muscle-skin shifting based on the change of the sEMG signals in response to the postural changes. We conducted the experiments of wrist extension motions under three different forearm postures: forearm pronation, natural posture and forearm supination. Experimental results from three healthy subjects show that we can quantify the extent of muscle-skin shifting as an angle by using proposed algorithm.
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In the last 4 decades, surface electromyography (sEMG) signal processing has been applied to detect local muscle fatigue, this non-invasive approach is suitable for detecting EMG signals generated by athletes in motion. Also, EMG could directly reveal the muscle’s performance like endurance and recruitment of motor units, which is hard to be obtained by other methods. With the sEMG system, we can research whether EMG signals can be used to measure muscle fatigue and how this relates to injury risk. This thesis aims to build a sensor node for sEMG to detect local muscle fatigue. An sEMG system is built for this purpose, and a physiological experiment is designed to collect sEMG signals from human muscle (Vastus Medialis) using the sEMG system. Both isometric and isotonic exercises are studied. The data analyzing method is calculating mean power spectrum frequency (MNF), median power spectrum frequency (MDF), and muscle fiber propagation velocity (MFPV) of the collected sEMG signals, because local muscle fatigue is related to MNF/MDF decrease and MFPV decrease. 5 groups of isometric exercise, wall-sit and 2 groups of isotonic exercise, cycling, are recorded. All the athletes are healthy males, around 25. The data analyzing result shows that MNF/MDF decrease is related to muscle fatigue, and MFPV changes similarly with MNF/MDF.
Muscle Fatigue
Vastus medialis
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The muscle will produce tiny change of bioelectricity. Surface electromyogram(sEMG) is the noninvasive method that describe and measure local muscle activities changes. The sEMG's amplitude and frequence signals will change with muscular movement. The changes relate to kinetic manner,exercise state and exercise induce fatigue.
Muscle Fatigue
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sEMG, as a kind of bioelectrical signal reflecting muscle motion state, generally applies to motion recognition and human interface. Healthy subjects are selected in most studies, while for hemiplegic patients, especially patients with severe hemiplegia, high accuracy motion recognition is difficult to acquire due to the non-ideal sEMG signal from dysfunction muscles. Therefore, this paper presents an upper limb exercise therapy, based on 5 defined motions and 6 Muscle-Units, for patients with severe hemiplegia. Through the sampling and analysis of sEMG signals from 8 subjects, including 4 healthy and 4 hemiplegic patients, we draw a conclusion of the relevance between specific motions and Muscle-Units, which can be used as a reference for paralyzed arm training. According to this relevance, six Muscle-Units can be classified into two categories: major Muscle-Units and minor Muscle-Units. In order to improve the interest and positivity of patients, a PC based virtual interactive platform is established. The sEMG signal from major Muscle-Units is processed with a moving average algorithm, and the result is used as the control signal for training interaction.
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This chapter contains sections titled: Introduction A Few "Tips and Tricks" Time and Frequency Domain Analysis of sEMG: What Are We Looking For? Application of sEMG to the Study of Exercise Strength and Power Training Muscle Damage Studied by Means of sEMG References
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Surface Electromyogram (SEMG) is a one--dimensional time series signal of neuromuscular system recorded from skin surface. The muscle will produce tiny change of bioelectricity. The amplitude and power spectrum of SEMG will change with muscular movement. However the change of SEMG is correlative with the methods and of measurements and types of exercise. As an non—traumatic testing technique, SEMG will play an important role in studying exercise--induced fatigue.
Muscle Fatigue
Muscular fatigue
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