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Journal articles on the topic 'Signal EMG du muscle'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Shakya, Sahaj, and Bipul Ranjitkar. "Forearm Bio-Medical Signal Processing." International Journal on Engineering Technology 2, no. 1 (2024): 49–59. https://doi.org/10.3126/injet.v2i1.72518.

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This research utilizes low-dimensional surface EMG and EEG data, obtained from the human arm using ECG electrodes, to analyze forearm muscle signals through a novel approach. Both EMG and EEG signals are employed side by side: EEG captures brain activity, particularly in the beta (13-30 Hz) and alpha (8-12 Hz) frequency ranges, while EMG focuses on muscle activity in the 20 Hz to 200 Hz range. Beta waves are associated with motor planning and voluntary movements, while alpha waves decrease during movement execution, indicating disengagement from a resting state. Event-related desynchronization

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Strzecha, Krzysztof, Marek Krakós, Bogusław Więcek, et al. "Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences." Applied Sciences 11, no. 10 (2021): 4625. http://dx.doi.org/10.3390/app11104625.

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This work deals with electromyography (EMG) signal processing for the diagnosis and therapy of different muscles. Because the correct muscle activity measurement of strongly noised EMG signals is the major hurdle in medical applications, a raw measured EMG signal should be cleaned of different factors like power network interference and ECG heartbeat. Unfortunately, there are no completed studies showing full multistage signal processing of EMG recordings. In this article, the authors propose an original algorithm to perform muscle activity measurements based on raw measurements. The effective

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Azad Feyzullayev, Aynur Jabiyeva, Azad Feyzullayev, Aynur Jabiyeva. "STRATEGY FOR REDUCING MUSCLE FATIGUE IN THE USE OF BIOELECTRIC PROSTHESIS BASED ON ELECTROMYOGRAPHIC ANALYSIS." Caucasus-Economic and Social Analysis Journal of Southern Caucasus 63, no. 01 (2025): 25–36. https://doi.org/10.36962/cesajsc63012025-25.

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Muscle fatigue is characterised by a decrease in the ability of muscles to perform required functions, which is accompanied by accumulation of metabolites, changes in ionic balance and electrical properties of muscle tissue. These physiological changes lead to marked transformations of electromyographic (EMG) signals, including a shift of their spectrum to lower frequencies. Such changes affect the quality of signal recognition and control accuracy of bioelectric prostheses, since most myoelectric control algorithms are based on the stability of spectral and amplitude characteristics of EMG si

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Arifin, Fatchul, Tri Arief Sardjono, and Mauridhi Hery Purnomo. "THE RELATIONSHIP BETWEEN ELECTROMYOGRAPHY SIGNAL OF NECK MUSCLE AND HUMAN VOICE SIGNAL FOR CONTROLLING LOUDNESS OF ELECTROLARYNX." Biomedical Engineering: Applications, Basis and Communications 26, no. 05 (2014): 1450054. http://dx.doi.org/10.4015/s1016237214500549.

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Human voice intonation is affected by pitch and loudness. Pitch is related to the frequency of human voice, while loudness is related to the magnitude of human voice. Someone who does not have vocal cords, has no ability to produce voice. This problem is suffered by laryngectomy patients. Over half of all laryngectomy patients worldwide use electrolarynx for the rehabilitation of their speech ability. Unfortunately, the electrolarynx voice produces monotonic and flat intonation. Small changes in pitch and loudness of electrolarynx will give a better expression in laryngectomy patients. However

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Shiao, Yaojung, and Thang Hoang. "Exercise Condition Sensing in Smart Leg Extension Machine." Sensors 22, no. 17 (2022): 6336. http://dx.doi.org/10.3390/s22176336.

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Skeletal muscles require fitness and rehsabilitation exercises to develop. This paper presents a method to observe and evaluate the conditions of muscle extension. Based on theories about the muscles and factors that affect them during leg contraction, an electromyography (EMG) sensor was used to capture EMG signals. The signals were applied by signal processing with the wavelet packet entropy method. Not only did the experiment follow fitness rules to obtain correct EMG signal of leg extension, but the combination of inertial measurement unit (IMU) sensor also verified the muscle state to dis

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Merletti, R., B. Indino, T. Graven-Nielsen, and D. Farina. "Surface EMG Crosstalk Evaluated from Experimental Recordings and Simulated Signals." Methods of Information in Medicine 43, no. 01 (2004): 30–35. http://dx.doi.org/10.1055/s-0038-1633419.

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Summary Objectives: Surface EMG crosstalk is the EMG signal detected over a non-active muscle and generated by a nearby muscle. The aim of this study was to analyze the sources of crosstalk signals in surface EMG recordings and to discuss methods proposed in the literature for crosstalk quantification and reduction. Methods: The study is based on both simulated and experimental signals. The simulated signals are generated by a structure based surface EMG signal model. Signals were recorded with both intramuscular and surface electrodes and single motor unit surface potentials were extracted wi

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Neto, Osmar Pinto, and Evangelos A. Christou. "Rectification of the EMG Signal Impairs the Identification of Oscillatory Input to the Muscle." Journal of Neurophysiology 103, no. 2 (2010): 1093–103. http://dx.doi.org/10.1152/jn.00792.2009.

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Rectification of EMG signals is a common processing step used when performing electroencephalographic–electromyographic (EEG–EMG) coherence and EMG–EMG coherence. It is well known, however, that EMG rectification alters the power spectrum of the recorded EMG signal (interference EMG). The purpose of this study was to determine whether rectification of the EMG signal influences the capability of capturing the oscillatory input to a single EMG signal and the common oscillations between two EMG signals. Several EMG signals were reconstructed from experimentally recorded EMG signals from the surfa

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L N, Dayananda. "Portable Non – Invasive Device for ECG and EMG Monitoring." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48910.

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Abstract— This project proposes the design and implementation of a portable, non-invasive ECG and EMG monitoring system using the ESP32-S3 microcontroller. The system is capable of capturing and displaying real-time bioelectrical signals from the human body using two dedicated sensors—an AD8232 module for electrocardiogram (ECG) signal acquisition and a modular EMG sensor for muscle activity monitoring. The ESP32-S3’s built-in ADC is used to digitize the signals, which are then processed and displayed as scrolling waveforms on a 320×240 SPI TFT display. A push-button interface is incorporated

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Ojha, Anuj. "An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview." Extensive Reviews 3, no. 1 (2023): 24–37. http://dx.doi.org/10.21467/exr.3.1.8382.

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Electromyography (EMG) is about studying electrical signals from muscles and can provide a wealth of information on the function, contraction, and activity of your muscles. In the field of EMG pattern recognition, these signals are used to identify and categorize patterns linked to muscle activity. Various machine learning (ML) methods are used for this purpose. Successful detection of these patterns depends on using effective signal-processing techniques. It is crucial to reduce noise in EMG for accurate and meaningful information about muscle activity, improving signal quality for precise as

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HAMZI, Maroua, Mohamed BOUMEHRAZ, and Rafia HASSANI. "Flexion Angle Estimation from Single Channel Forearm EMG Signals using Effective Features." Electrotehnica, Electronica, Automatica 71, no. 3 (2023): 61–68. http://dx.doi.org/10.46904/eea.23.71.3.1108007.

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Electromyography (EMG) records the electrical activity generated by skeletal muscles, offering valuable insights into muscle function and movement. To address the complexity of EMG signals, various signal analysis methods have been developed in the time and frequency domains for engineering applications like myoelectric control of prosthetics and movement analysis. In this study, EMG signals were acquired from ten healthy volunteers in different forearm positions using a Myoware Muscle Sensor and MPU6050 board. From each EMG signal, root mean square (RMS), standard deviation (STD), and mean ab

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Rusli, Rusli Ully, Ruslan Ruslan, Sarifin G., Arimbi Arimbi, and Mariyal Qibtiyah. "Measurement of Medial Head Gastrocnemius Muscle Contraction Strength in Basic Sepak Takraw Techniques Using Electromyogram Signals." COMPETITOR: Jurnal Pendidikan Kepelatihan Olahraga 15, no. 3 (2023): 683. http://dx.doi.org/10.26858/cjpko.v15i3.53403.

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This study aims to measure the strength of contraction of the gastrocnemius medial head muscle in basic techniques sepak sila using electromyogram signals. The subjects in this research were 3 South Sulawesi sepak takraw athletes. EMG signal measurement using the Trigno™ Wireless EMG System. The output data is the results of the EMG signal, the Root Mean Square value of each muscle component measured. The data analysis technique uses quantitative descriptive. The results of EMG signal measurements produce RMS values for each muscle measured as follows: (1). The subject produced the largest fir

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Pratama, Destra Andika, Yeni Irdayanti, and Satrio Aditiyas Sukardi. "EMG Signal Analysis on Flexion Extension Movements of The Hand and Leg Using Matlab." Radiasi : Jurnal Berkala Pendidikan Fisika 16, no. 2 (2023): 61–70. http://dx.doi.org/10.37729/radiasi.v16i2.3373.

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Muscle Spiker Shield is a tool used to record electrical signals generated by the muscles of the human body. These signals can provide important information about the health and activities of organisms, especially humans. As technology advances, more and more devices can be used to record the activity of these signals, including the Muscle Spiker Shield. One of the uses of the Muscle Spiker Shield is to monitor muscle wave activity. Human muscle waves are electrical signals generated by muscles and can provide information about the state of a person's movement activity. Monitoring human muscle

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Liang, Hongbo, Yingxin Yu, Mika Mochida, et al. "EEG-Based EMG Estimation of Shoulder Joint for the Power Augmentation System of Upper Limbs." Symmetry 12, no. 11 (2020): 1851. http://dx.doi.org/10.3390/sym12111851.

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Brain–Machine Interfaces (BMIs) have attracted much attention in recent decades, mainly for their applications involving severely disabled people. Recently, research has been directed at enhancing the ability of healthy people by connecting their brains to external devices. However, there are currently no successful research reports focused on robotic power augmentation using electroencephalography (EEG) signals for the shoulder joint. In this study, a method is proposed to estimate the shoulder’s electromyography (EMG) signals from EEG signals based on the concept of a virtual flexor–extensor

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Tanuja Subba, Et al. "A Study on Electromyography Signal as a Controller." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2024): 4662–67. http://dx.doi.org/10.17762/ijritcc.v11i9.10014.

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Human computer interaction (HCI) is the study of interfaces between human and computer. When an input keyboard is pressed the output is displayed in the monitor is a simple example of human and computer interaction. World Wide Web is yet another example of HCI. HCI is everywhere and has become an important aspect in human life. HCI have many subfields and one among them is the study of biosignals. Signals that are generated from living body during muscle contraction, eye movement, brain signal are biosignals and these signals have potential for developing an interface for human computer intera

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Caesaria, Arifah Putri, Endro Yulianto, Sari Luthfiyah, Triwiyanto Triwiyanto, and Achmad Rizal. "Effect of Muscle Fatigue on EMG Signal and Maximum Heart Rate for Pre and Post Physical Activity." Journal of Electronics, Electromedical Engineering, and Medical Informatics 5, no. 1 (2023): 39–45. http://dx.doi.org/10.35882/jeeemi.v5i1.278.

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Sport is a physical activity that can optimize body development through muscle movement. Physical activity without rest with strong and prolonged muscle contractions results in muscle fatigue. Muscle fatigue that occurs causes a decrease in the work efficiency of muscles. Electrocardiography (ECG) is a recording of the heart's electrical activity on the body's surface. EMG is a technique for measuring electrical activity in muscles. This study aims to detect the effect of muscle fatigue on cardiac signals by monitoring ECG and EMG signals. This research method uses the Maximum Heart Rate with

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Song, Kwangsub, Sangui Choi, and Hooman Lee. "Voluntary Muscle Contraction Detection Algorithm Based on LSTM for Muscle Quality Measurement Algorithm." Applied Sciences 11, no. 18 (2021): 8676. http://dx.doi.org/10.3390/app11188676.

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In this paper, we propose the long–short-term memory (LSTM)-based voluntary and non-voluntary (VNV) muscle contraction classification algorithm in an electrical stimulation (ES) environment. In order to measure the muscle quality (MQ), we employ the non-voluntary muscle contraction signal, which occurs by the ES. However, if patient movement, such as voluntary muscle contractionm, occurs during the ES, the electromyography (EMG) sensor captures the VNV muscle contraction signals. In addition, the voluntary muscle contraction signal is a noise component in the MQ measurement technique, which us

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Anas Fouad Ahmed. "A quick survey of filtering techniques for surface electromyography signals." Global Journal of Engineering and Technology Advances 11, no. 3 (2022): 105–10. http://dx.doi.org/10.30574/gjeta.2022.11.3.0101.

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Electromyography (EMG) represents the electrical activity of muscles, and it has a wide range of usage in biomedical and clinical tasks. During myoelectrical stimulation, the EMG signal has two sources: the meaningful electrical response of the muscles and signal noise. Technical noise (such as power line noise) and biological noise (ECG). The noises in the system must be efficiently rejected, as this will disturb the analysis of the activity of the muscle. This paper presents different types of noise that corrupt the EMG signal and the main denoising approaches for minimizing the noise effect

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Anas, Fouad Ahmed. "A quick survey of filtering techniques for surface electromyography signals." Global Journal of Engineering and Technology Advances 11, no. 3 (2022): 105–10. https://doi.org/10.5281/zenodo.6962096.

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Electromyography (EMG) represents the electrical activity of muscles, and it has a wide range of usage in biomedical and clinical tasks. During myoelectrical stimulation, the EMG signal has two sources: the meaningful electrical response of the muscles and signal noise. Technical noise (such as power line noise) and biological noise (ECG). The noises in the system must be efficiently rejected, as this will disturb the analysis of the activity of the muscle. This paper presents different types of noise that corrupt the EMG signal and the main denoising approaches for minimizing the noise effect

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JUNG, CHAN YONG, JUN-SIK PARK, YONGHYUN LIM, et al. "ESTIMATING FATIGUE LEVEL OF FEMORAL AND GASTROCEMIUS MUSCLES BASED ON SURFACE ELECTROMYOGRAPHY IN TIME AND FREQUENCY DOMAIN." Journal of Mechanics in Medicine and Biology 18, no. 05 (2018): 1850042. http://dx.doi.org/10.1142/s0219519418500422.

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This paper presents a new method for estimating muscle fatigue level based on surface electromyography (EMG) of femoral and gastrocnemius muscles during repetitive motions with various load. The relationship between fatigue level and EMG signals was examined through repetitive movements of the femoral and gastrocnemius muscles with the use of leg extension and squat machines. The fatigue level was based on the maximum voluntary contraction (MVC) levels with various loads. The integrated EMG (IEMG) value and the mean frequency value for each load cycle were obtained through the surface EMG sign

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Malik Mohd Ali, Abdul, Syed Faiz Ahmed, Athar Ali, M. Kamran Joyo, Kushairy A. Kadir, and Radzi Ambar. "EMG-Based Spasticity Robotic Arm Forupper Arm Fatigue Identification." International Journal of Engineering & Technology 7, no. 2.34 (2018): 79. http://dx.doi.org/10.14419/ijet.v7i2.34.13917.

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Electromyogram (EMG) signal reflect the electrical activity of human muscle and contains information about the structure of muscle. Furthermore, motor unit action potential (MUAP) is the results from spatial and temporal summation of difference muscle fibers of a single motor. The EMG signal results, in turn is from the summation of different MUAPs which are sufficiently near the recording electrode. EMG signal can identify the differences between signals from bicep, triceps and forearms during exercise. Raw data from the experiment is vital to assist physiotherapy to understand when the subje

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Fauzi, Muhammad, Endro Yulianto, Bambang Guruh Irianto, et al. "Effect of Muscle Fatigue on Heart Signal on Physical Activity with Electromyogram and Electrocardiogram (EMG Parameter ) Monitoring Signals." Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics 4, no. 3 (2022): 114–22. http://dx.doi.org/10.35882/ijeeemi.v4i3.240.

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Physical activity is an activity of body movement by utilizing skeletal muscles that is carried out daily. One form of physical activity is an exercise that aims to improve health and fitness. Parameters related to health and fitness are heart and muscle activity. Strong and prolonged muscle contractions result in muscle fatigue. To measure muscle fatigue, the authors used electromyographic (EMG) signals through monitoring changes in muscle electrical activity. This study aims to make a tool to detect the effect of muscle fatigue on cardiac signals on physical activity. This research method us

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Lima Alberton, Cristine, Stephanie Santana Pinto, Natália Amélia da Silva Azenha, et al. "Kinesiological Analysis of Stationary Running Performed in Aquatic and Dry Land Environments." Journal of Human Kinetics 49, no. 1 (2015): 5–14. http://dx.doi.org/10.1515/hukin-2015-0103.

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Abstract The purpose of the present study was to analyze the electromyographic (EMG) signals of the rectus femoris (RF), vastus lateralis (VL), semitendinosus (ST) and short head of the biceps femoris (BF) during the performance of stationary running at different intensities in aquatic and dry land environments. The sample consisted of 12 female volunteers who performed the stationary running exercise in aquatic and dry land environments at a submaximal cadence (80 beats·min-1 controlled by a metronome) and at maximal velocity, with EMG signal measurements from the RF, VL, ST and BF muscles. T

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Granados-Ruiz, Jackeline, David Asael Gutiérrez-Hernández, Carlos Lino-Ramírez, et al. "METHODOLOGICAL APPROACH FOR EXTRACTION OF CHARACTERISTICS OF BIOLOGICAL SIGNALS." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 02 (2019): 3011–20. https://doi.org/10.5281/zenodo.14811307.

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Generally, signal processing is applied to a set of data that is derived from the sampling of an acquired signal. This treatment is carried out with the help of a computer that in turn executes a series of logical and mathematical operations. The treatment of signals is linked to other techniques and scientific disciplines. Some of the applications of the signal treatments may be in the form of processing of audio signals, treatment of digital images, digital communications and biological signals. In this case, the treatment was applied to biological signals such as ECG (Electrocardiogram sign

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Prasad, V. V. K. D. V., B. Nagasirisha, Joycy Y. Janitha, Naik R. Venkatesh, Naga Sai B. Lalithadithya, and T. Ramya. "Feature extraction and classification of different hand movements from the emg signal using linear discriminant analysis classifier." i-manager’s Journal on Electronics Engineering 14, no. 2 (2024): 19. http://dx.doi.org/10.26634/jele.14.2.20585.

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In biomedical research, Electromyography (EMG) data play a crucial role as a bridge between human motions and machine interpretation, offering valuable insights into muscle activation. EMG signals give vital information on hand movements in the context of applications like gesture recognition, prosthetic control, and rehabilitation. This paper describes the classification of EMG signals based on muscle motions, which makes it simpler to identify distinct gestures or movements. A Linear Discriminant Analysis (LDA) classifier is used to differentiate between various classes of muscle activity. I

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Huang, Q. H., Y. P. Zheng, X. Chena, J. F. He, and J. Shi. "A System for the Synchronized Recording of Sonomyography, Electromyography and Joint Angle." Open Biomedical Engineering Journal 1, no. 1 (2007): 77–84. http://dx.doi.org/10.2174/1874120700701010077.

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Ultrasound and electromyography (EMG) are two of the most commonly used diagnostic tools for the assessment of muscles. Recently, many studies reported the simultaneous collection of EMG signals and ultrasound images, which were normally amplified and digitized by different devices. However, there is lack of a systematic method to synchronize them and no study has reported the effects of ultrasound gel to the EMG signal collection during the simultaneous data collection. In this paper, we introduced a new method to synchronize ultrasound B-scan images, EMG signals, joint angles and other relat

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TSUJI, TOSHIO, NAN BU, JUN ARITA, and MAKOTO OHGA. "A SPEECH SYNTHESIZER USING FACIAL EMG SIGNALS." International Journal of Computational Intelligence and Applications 07, no. 01 (2008): 1–15. http://dx.doi.org/10.1142/s1469026808002119.

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This paper proposes a novel phoneme classification method using facial electromyography (EMG) signals. This method makes use of differential EMG signals between muscles for phoneme classification, which enables a speech synthesizer to be constructed using fewer electrodes. The EMG signal is derived as a differential between monopolar electrodes attached to two different muscles, unlike conventional methods in which the EMG signal is derived as a differential between bipolar electrodes attached to the same muscle. Frequency-based feature patterns are then extracted using a filter bank, and the

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Qassim, Hassan M., Wan Zuha Wan Hasan, Hafiz R. Ramli, Hazreen Haizi Harith, Liyana Najwa Inche Mat, and Luthffi Idzhar Ismail. "Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier." Sensors 22, no. 5 (2022): 1900. http://dx.doi.org/10.3390/s22051900.

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The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves cal

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Triwiyanto, Triwiyanto, Triana Rahmawati, I. Putu Alit Pawana, and Evrinka Hikaristiana Maulidia. "Investigation of Electrode Location to Improve the Accuracy of Wearable Hand Exoskeleton Trainer Based on Electromyography." Journal of Biomimetics, Biomaterials and Biomedical Engineering 55 (March 28, 2022): 71–80. http://dx.doi.org/10.4028/p-y7g473.

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EMG signal has a random and stochastic characteristics, so it is difficult to predict the amplitude. Furthermore, the EMG signal depends on the electrodes location. Therefore, a proper muscle selection determines the system's accuracy value. The purpose of this study was to investigate the exact location of the electrodes to improve the accuracy of the wearable hand exoskeleton trainer based on electromyography (EMG) signal control. The main advantage of the results of this study is that the most dominant muscle was found in the development of a wearable hand exoskeleton based on an EMG signal

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Rifky, Ismail. "Muscle Power Signal Acquisition Monitoring Using Surface EMG." J Biomed Res Environ Sci 3, no. 5 (2022): 664–68. https://doi.org/10.37871/jbres1493.

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Recent demand and interest in patient health monitoring has driven significant interest toward developing varies alternative rehabilitation monitoring instrument. Muscle power can be seen from how many power it’s generate from contraction effort that can be sense by its voltage potential in microvolt order. Surface Electromyogram (EMG) is used to take the record acquisition of muscle power data as patient take the rehabilitation program for some period. In this paper the surface EMG designed to get some level amplification to maintain the data readable and filter to minimize noise that a

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Wang, Tianyu, Shanshan Yao, Li-Hua Shao, and Yong Zhu. "Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing." Sensors 24, no. 20 (2024): 6670. http://dx.doi.org/10.3390/s24206670.

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Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and e

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GAO, YONGSHENG, SHENGXIN WANG, FEIYUN XIAO, and JIE ZHAO. "AN ANGLE-EMG BIOMECHANICAL MODEL OF THE HUMAN ELBOW JOINT." Journal of Mechanics in Medicine and Biology 16, no. 06 (2016): 1650078. http://dx.doi.org/10.1142/s0219519416500780.

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The biomechanical model of the human elbow joint is extensively studied. In the model, the surface electromyography (sEMG) is used as the input signal, whereas the muscle force or muscle torque is commonly considered as the output signal. The estimation of the actual muscle force or torque is important to effectively modulate the tremor suppression. However, the measurement of the muscle force or torque in vivo is difficult. In this paper, a new angle-to-EMG biomechanical model of the elbow joint was developed and evaluated by comparing the measured sEMG with the calculated sEMG. Three sources

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Sarangi, Animesh, Bal Gopal Mishra, and Satyabhama Dash. "Singular Spectrum Analysis Based EMG Artifact Removal from ECG Signal." YMER Digital 21, no. 08 (2022): 400–407. http://dx.doi.org/10.37896/ymer21.08/36.

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Electromyogram (EMG) or muscle artifacts frequently affect electrocardiogram (ECG) readings. These artifacts make the required information in the ECG signal difficult to see. In this study, we introduced the singular spectrum analysis (SSA), a powerful subspace-based method for removing EMG artifacts from ECG data. In order to effectively extract the desired component from the tainted ECG data, we presented a new grouping approach and set a threshold. First, a process known as embedding converts a single channel signal into several channels of signals or data. The orthogonal eigenvectors are t

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Isezaki, Takashi, Hideki Kadone, Arinobu Niijima, et al. "Sock-Type Wearable Sensor for Estimating Lower Leg Muscle Activity Using Distal EMG Signals." Sensors 19, no. 8 (2019): 1954. http://dx.doi.org/10.3390/s19081954.

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Lower leg muscle activity contributes to body control; thus, monitoring lower leg muscle activity is beneficial to understand the body condition and prevent accidents such as falls. Amplitude features such as the mean absolute values of electromyography (EMG) are used widely for monitoring muscle activity. Garment-type EMG measurement systems use electrodes and they enable us to monitor muscle activity in daily life without any specific knowledge and the installation for electrode placement. However, garment-type measurement systems require a high compression area around the electrodes to prev

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Jeon, Bu Il, Byung Jun Kang, Hyun Chan Cho, and Jongwon Kim. "Motion Recognition and an Accuracy Comparison of Left and Right Arms by EEG Signal Analysis." Applied Sciences 9, no. 22 (2019): 4885. http://dx.doi.org/10.3390/app9224885.

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An electromyogram (EMG) is a signal for muscle output that indicates the degree of muscle contraction and relaxation. For these muscle signals to be output, certain signals must be received from the brain. To analyze these relations, electroencephalograms (EEGs) of the brain are measured to extract brain waves that are active at that time, although it is difficult to identify or distinguish expression patterns of the brain signal through EMG output. However, the brain signal operates via a partially reached signal and transmits the results of the operation. In this study, we analyze signals tr

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Putra, Darma Setiawan, and Yuril Umbu WW. "Feature Extraction of Facial Electromyograph (EMG) Signal for Aceh Languages Speech using Discrete Wavelet Transform (DWT)." Jurnal Inotera 4, no. 1 (2019): 31. http://dx.doi.org/10.31572/inotera.vol4.iss1.2019.id73.

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The facial electromyograph (FEMG) signal is a signal that occurs in the muscles of the contracted human face. This FEMG signal is one of the techniques used to study human speech recognition. It can be acquired by placing an electrode surface on the skin around the facial articulation muscle. Three types of muscles in this study are the masseter, risorius and depressor muscle. This study aims to extract and analyze the features in the FEMG signal. The extraction method is the discrete wavelet transform (DWT). The type of wavelet transform is Daubechies2 with level 5. After extraction and analy

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PHINYOMARK, ANGKOON, FRANCK QUAINE, YANN LAURILLAU, SIRINEE THONGPANJA, CHUSAK LIMSAKUL, and PORNCHAI PHUKPATTARANONT. "EMG AMPLITUDE ESTIMATORS BASED ON PROBABILITY DISTRIBUTION FOR MUSCLE–COMPUTER INTERFACE." Fluctuation and Noise Letters 12, no. 03 (2013): 1350016. http://dx.doi.org/10.1142/s0219477513500168.

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To develop an advanced muscle–computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb

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Badanova, Emma, and Ekaterina Zubareva. "MORPHOFUNCTIONAL AND BIOELECTRIC CHARACTERISTICS OF SOME MUSCLES OF THE WRIST JOINT IN A DOG: PILOT STUDY." Bulletin of KSAU, no. 12 (January 27, 2025): 176–82. https://doi.org/10.36718/1819-4036-2023-12-176-182.

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The paper presents the morphofunctional characteristics of some muscles of the dog's wrist joint, as well as their bioelectrical characteristics. The purpose of the study is to describe the bioelectrical characteristics of the radial and ulnar extensor carpi of the dog, taking into account the morphofunctional characteristics of these muscles. Objectives of the study: to conduct a morphological and electrophysiological study of the radial and ulnar extensor wrist muscles in dogs; identify patterns of EMG formation depending on the internal structure and innervation of muscles, and conduct a co

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Oo, Thandar, and Pornchai Phukpattaranont. "Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals." Fluctuation and Noise Letters 19, no. 03 (2020): 2050027. http://dx.doi.org/10.1142/s0219477520500273.

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When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used

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Deslivia, Maria, Hyun-Joo Lee, Rizki Zulkarnain, et al. "The Effect of Split Nerve on Electromyography Signal Pattern in a Rat Model." Journal of Reconstructive Microsurgery 34, no. 02 (2017): 095–102. http://dx.doi.org/10.1055/s-0037-1606539.

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Background Recent developments of prosthetic arm are based on the use of electromyography (EMG) signals. To provide improvements, such as coordinated movement of multiple joints and greater control intuitiveness, higher variability of EMG signals is needed. By splitting a nerve lengthwise, connecting each half to new target muscles, and employing a program to assign each biosignal pattern to a specific movement, we hope to enrich the number of biosignal sites on amputees' stump. Methods We split the gastrocnemius muscle of 12 Sprague-Dawley rats into two muscle heads, searched for the peroneal

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Kamal, Shahul Mujib, Sue Sim, Rui Tee, Visvamba Nathan, and Hamidreza Namazi. "Complexity-Based Analysis of the Relation between Human Muscle Reaction and Walking Path." Fluctuation and Noise Letters 19, no. 03 (2020): 2050025. http://dx.doi.org/10.1142/s021947752050025x.

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Legs are the contact point of humans during walking. In fact, leg muscles react when we walk in different conditions (such as different speeds and paths). In this research, we analyze how walking path affects leg muscles’ reaction. In fact, we investigate how the complexity of muscle reaction is related to the complexity of path of movement. For this purpose, we employ fractal theory. In the experiment, subjects walk on different paths that have different fractal dimensions and then we calculate the fractal dimension of Electromyography (EMG) signals obtained from both legs. The result of our

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Mukhtar Alam, Mohd, and Abid Ali Khan. "Electromyography-based Fatigue Assessment During Endurance Testing by Different Vibration Training Protocols." Iranian Rehabilitation Journal 19, no. 1 (2021): 85–98. http://dx.doi.org/10.32598/irj.19.1.1150.1.

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Objectives: This study presents a method of assessing muscle fatigue during endurance testing at 50% maximal voluntary contraction (MVC) using electromyography (EMG) information as indirect indices of fatigability in the forearm muscles, namely, flexor digitorum superficialis (FDS); flexor carpi ulnaris (FCU); extensor carpi ulnaris (ECU) and extensor carpi radialis brevis (ECRB)." This study presents a method of assessing muscle fatigue during endurance testing at 50% maximal voluntary contraction (MVC) using electromyography (EMG) information as indirect indices of fatigability in the forear

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Chen, Wei, Ruizhi Chen, Xiang Chen, et al. "Comparison of EMG-based and Accelerometer-based Speed Estimation Methods in Pedestrian Dead Reckoning." Journal of Navigation 64, no. 2 (2011): 265–80. http://dx.doi.org/10.1017/s0373463310000391.

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In low-cost self-contained pedestrian navigation systems, traditional Pedestrian Dead Reckoning (PDR) solutions utilize accelerometers to derive the speed as well as the distance travelled, and obtain the walking heading from magnetic compasses or gyros. However, these measurements are sensitive to instrument errors and disturbances from ambient environment. To be totally different from these signals in nature, the electromyography (EMG) signal is a typical kind of biomedical signal that measures electrical potentials generated by muscle contractions from the human body. This kind of signal wo

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Sadikoglu, Fahreddin, Cemal Kavalcioglu, and Berk Dagman. "Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease." Procedia Computer Science 120 (2017): 422–29. http://dx.doi.org/10.1016/j.procs.2017.11.259.

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Zukro Aini, Rasyida Shabihah. "EMG Instrumentation Modeling and Feature Processing Based On Discrete Wavelet Transform." Indonesian Applied Physics Letters 5, no. 1 (2024): 1–13. http://dx.doi.org/10.20473/iapl.v5i1.56245.

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Electromyography (EMG) instrumentation is essential in generating electrical signals from skeletal muscles. EMG sensors are helpful in various cases requiring the detection of human muscle contractions, neuromuscular disorders, and rehabilitation. EMG instrumentation is divided into two parts, namely, the analogue part and the digital part. The EMG instrumentation design comprises a digital-to-analog converter (DAC), instrumentation amplifier, filter, and analog-to-digital converter (ADC). Meanwhile, in digital signal processing adopting the Discrete Wavelet Transform (DWT) method, frequency a

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Lin, B., S. F. Wong, and A. Baca. "Comparison of Different Time-Frequency Analyses Techniques Based on sEMG-Signals in Table Tennis: A Case Study." International Journal of Computer Science in Sport 17, no. 1 (2018): 77–93. http://dx.doi.org/10.2478/ijcss-2018-0004.

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Abstract The surface EMG signal in the action of dynamic contraction has more movement interference compared to sustained static contractions. In addition, the recruitment and de-recruitment of motor units causes a faster change in the surface EMG signal’s proprieties. Therefore, more complex techniques are required to extract information from the surface EMG signal. The standardized protocol for surface myoelectric signal measurement in table tennis was a case study in this research area. The Autoregressive method based on the Akaike Information Criterion, the Wavelet method based on intensit

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KEERATIHATTAYAKORN, Saran, and Shigeru TADANO. "1B08 Relationship between EMG signal and muscle acceleration during elbow flexion/extension." Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME 2013.25 (2013): 73–74. http://dx.doi.org/10.1299/jsmebio.2013.25.73.

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Dorgham, Osama, Ibrahim Al-Mherat, Jawdat Al-Shaer, Sulieman Bani-Ahmad, and Stephen Laycock. "Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network." Future Internet 11, no. 1 (2019): 25. http://dx.doi.org/10.3390/fi11010025.

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Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with variou

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Ridzuan, Nursyazana, Aizreena Azaman, Soeed K, Izwyn Zulkapri, and Asnida Abd Wahab. "Evaluation of muscle fatigue using infrared thermal imaging technique with assisted electromyography." Malaysian Journal of Fundamental and Applied Sciences 13, no. 4-2 (2017): 509–14. http://dx.doi.org/10.11113/mjfas.v13n4-2.823.

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Muscle fatigue in sports science is an established research area where various techniques and types of muscles have been studied in order to understand the fatigue condition. It can be used as an indicator for predicting muscle injury and other muscle problems which can decrease athletes’ performance. Muscle fatigue usually occurs after a long lasting or repeated muscular activity. Electromyography (EMG) assessment method is a standard tool used to evaluate muscle fatigue based on the signals from the neuromuscular activation during fatigue condition. However, additional time for equipment set

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Liu, Shing-Hong, Chuan-Bi Lin, Ying Chen, Wenxi Chen, Tai-Shen Huang, and Chi-Yueh Hsu. "An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise." Sensors 19, no. 14 (2019): 3108. http://dx.doi.org/10.3390/s19143108.

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In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop

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Soundirarajan, Mirra, Mohammad Hossein Babini, Sue Sim, Visvamba Nathan, and Hamidreza Namazi. "Decoding of the Relationship between Brain and Facial Muscle Activities in Response to Dynamic Visual Stimuli." Fluctuation and Noise Letters 19, no. 04 (2020): 2050041. http://dx.doi.org/10.1142/s0219477520500418.

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In this research, for the first time, we analyze the relationship between facial muscles and brain activities when human receives different dynamic visual stimuli. We present different moving visual stimuli to the subjects and accordingly analyze the complex structure of electromyography (EMG) signal versus the complex structure of electroencephalography (EEG) signal using fractal theory. Based on the obtained results from analysis, presenting the stimulus with greater complexity causes greater change in the complexity of EMG and EEG signals. Statistical analysis also supported the results of

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