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.

/pdf/application-of-surface-electromyography-in-exercise-fatigue-9mq1m5rv.pdf

Application of Surface Electromyography in Exercise Fatigue: A Review

Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso. Biomechanics of sprint running: a methodological contribution Autor(es): Moscatelli, Francesco; Mancini, Nicola Publicado por: Imprensa da Universidade de Coimbra URL persistente: URI:http://hdl.handle.net/10316.2/28382 DOI: DOI:http://dx.doi.org/10.14195/2182-7087_4_12

/pdf/biomechanics-of-sprint-running-a-methodological-contribution-2o7erjg097.pdf

Biomechanics of sprint running: a methodological contribution

An important research area in physiological and sport sciences is the analysis of the variations of the muscle reaction due to changes in walking speed. In this paper, we investigated the effect of walking speed variations on leg muscle reaction by the analysis of Electromyogram (EMG) signals at different walking inclines. For this purpose, we benefited from fractal theory and sample entropy to analyze how the complexity of EMG signals changes at different walking speeds. According to the results, although fractal theory could not show a clear trend between the variations of the complexity of EMG signals and the variations of the walking speed, however, based on the results, increasing the speed of walking in the case of different inclines is mapped on to the decrement of the sample entropy of EMG signals. Therefore, sample entropy could decode the effect of walking speed on the reaction of leg muscle. This analysis method could be applied to analyze the variations of other physiological signals of humans durin walking.

Investigation of the effect of walking speed on leg muscle reaction by complexity-based analysis of electromyogram (emg) signals

Bioelectric signals are significant indicators of the state of health of the human body, especially in sports monitoring, where athletes’ fatigue state and performance need to be monitored in real-time to develop a proper training plan. Due to the characteristics of sports, it is difficult to obtain the dynamic bioelectrical signals of the human body during exercise. This paper provides a comprehensive overview of the current knowledge on Intelligent Garment Systems (IGS) for long-lasting bioelectric monitoring in sports. This review includes a detailed examination of human bioelectric signals, focusing on ECG, EMG, and GSR signals and their applications in intelligent wearable technologies. The definition and development history of IGS is also discussed, along with a review of the primary research components of IGS, including dry textile electrodes, methods for connecting sensors to IGS, and processing methods for bioelectric signals. The paper concludes by highlighting the current challenges faced by IGS in terms of real-time dynamic monitoring and connection problems and outlining the future directions for this field, including the need for further advancements in bioelectric signal processing and analysis, the development of new materials and connection technologies, and the integration of artificial intelligence and machine learning into IGS.

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

In order to effectively analyze the multi-dimensional feature of surface electromyography (sEMG) signals under fatigue state, a fatigue identification method based on temporal-spatial-spectral joint domain is proposed in this paper. Firstly, the sEMG signals of 10 healthy male subjects during grip strength are collected. Secondly, the complex Morlet wavelet transform is used to construct the multi-order tensor data with time, space, frequency and task information, and then the tensor linear Laplace discriminant analysis method is employed to obtain the best projection matrix. The training set and test set are projected by the projection matrix respectively to obtain the features with large discrimination. Finally, linear discriminant analysis (LDA) is adopted to classify the grasping action under normal and fatigue conditions. Experimental results show that the proposed method can achieve better average classification accuracy, compared with the root mean square, autoregressive coefficient and tensor high-order discriminant analysis methods. It is thereby shown that our method can effectively detect different states of muscle contraction. 

Muscle Fatigue Identification in Temporal -Spatial-Spectral Joint Domain

The aim of this research work is to assess the muscles fatigue of the male runner during 400 meters (m) running with three types of running strategies. The Electromyography (EMG) signals from the Rectus Femoris (RF), Biceps Femoris (BF), Gluteus Maximus (GM), Gastrocnemius Lateralis (GL), and Gastrocnemius Medialis (GMS) were collected by using bipolar electrodes from the right lower extremity’s muscles. EMG signals were collected during the run on the tartan athletic track. Five subjects (non-athletes) had run 400m with three various types of running strategies. The first type: the first 200m running 85-93% of full speed and the last 200m sprinting (full speed), second type: the first 300m running 85-93% of sprinting and the last 100m sprinting, and third type: running 85-93% of sprinting for 400m. The EMG signals were transformed to the time-frequency domain using Short Time Fourier Transform to calculate the instantaneous mean frequency (IMNF) and instantaneous median frequency (IMDF). The less index fatigues were during 1st strategy, while the RF, BF, GM, and GL muscles got recovered with IMNF and IMDF with the three strategies, and the GMS muscle has less negative regression slope value with IMNF with 1st strategy during the 4th 100m of the 400m running event. From the results, it can be concluded the running with the 1st strategy get less fatigues compared with the 2nd and 3rd strategy based on the results of time-frequency domain features (IMNF and IMDF).

Assessment of Muscles Fatigue during 400-Meters Running Strategies Based on the Surface EMG Signals

The main goal of this research work is to study and evaluate the muscles force and fatigue of Gastrocnemius Medialis (GMS), Gluteus Maximus (GM), and Gastrocnemius Lateralis (GL) during running for 400-meters based on surface Electromyography (sEMG) signals. The sEMG signals of the selected muscles from the right leg have been collected by using bipolar electrodes from 15 subjects during the run on the tartan athletic track with two pacing strategies. The first strategy: 1st 200-meters running 87% - 94% of full speed and last 200-meters sprinting (full speed). The second strategy: 1st 300-meters running 87% - 94% of sprinting and last 100-meters sprinting. The rate of fatigue has been calculated by using Root Mean Square (RMS) and Median Frequency (MDF) features. Then, the slopes of linear regression were calculated from both RMS and MDF at each 100-meters. The linear slope values represented the rate of fatigue and force. From the results of 1st and 2nd running strategies, the force of GM and GL muscles increased during the 4th 100-meters of the 1st strategy and decreased with GM and GMS muscles during the 4th 100-meters of the 2nd strategy. The less index fatigues were during the 1st strategy for most selected muscles. Finally, it can be concluded the running with the 1st strategy get less fatigues and the force of most selected muscles increased compared with the 2nd strategy based on the results of time and frequency domain features (RMS and MDF).

Evaluation of Lower Limb Muscles Fatigue and Force during Running 400-Meters Using Learning Machine

The purpose of the present preliminary investigation is to characterize the vibration signal between normal and symptomatic knee joint. Accelerometers were attached to the medial and lateral retinaculum of dominant knee to acquire the vibration signal induced during flexion and extension of leg. Signals were compared in time and frequency domain and statistical analysis were performed using mean, variance, standard deviation, skewness and kurtosis. The results show that the frequency range for the symptomatic VAG signal is higher than the normal. The results confirm that the proposed approach is indeed feasible for the non-invasive diagnosis of knee pathology disorders.

https://www.akademiabaru.com/wvcarmea/docu/067.pdf

Feature Extraction of Knee Joint Vibroarthrographic (VAG) Signal Using Statistical Means

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Papers

Assessment of Muscles Fatigue during 400-Meters Running Strategies Based on the Surface EMG Signals

Evaluation of Lower Limb Muscles Fatigue and Force during Running 400-Meters Using Learning Machine

Feature Extraction of Knee Joint Vibroarthrographic (VAG) Signal Using Statistical Means