Letteratura scientifica selezionata sul tema "Muscle EMG signal"
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Articoli di riviste sul tema "Muscle EMG signal":
1
Strzecha, Krzysztof, Marek Krakós, Bogusław Więcek, Piotr Chudzik, Karol Tatar, Grzegorz Lisowski, Volodymyr Mosorov e Dominik Sankowski. "Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences". Applied Sciences 11, n. 10 (19 maggio 2021): 4625. http://dx.doi.org/10.3390/app11104625.
Abstract (sommario):
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 effectiveness of the proposed algorithm for EMG signal measurement was validated by a portable EMG system developed as a part of the EU research project and EMG raw measurement sets. Examples of removing the parasitic interferences are presented for each stage of signal processing. Finally, it is shown that the proposed processing of EMG signals enables cleaning of the EMG signal with minimal loss of the diagnostic content.
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Arifin, Fatchul, Tri Arief Sardjono e 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, n. 05 (26 settembre 2014): 1450054. http://dx.doi.org/10.4015/s1016237214500549.
Abstract (sommario):
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, previous researches have focused on utilization of electromyography (EMG) signal of neck muscle for only pitch control. In this research, the relationship between human voice intonation (i.e. frequency and magnitude) and EMG signals of neck muscles was studied by looking for their correlation and their mutual information. Human voice signal and EMG signal of neck muscle were recorded simultaneously while subjects were saying "A" with varying intonation. The EMG signal of neck muscle was processed using amplifying, filtering, rectifying and "moving average" process. On the other hand, the human voice was processed by FFT Algorithm to obtain magnitude and fundamental frequency. The result shows that the correlation coefficient between human voice magnitudes and EMG signal of neck muscle is 0.93, while the correlation coefficient between human voice frequency and EMG signal of neck muscle is 0.88. Moreover, the mutual information between human voice magnitudes and EMG signal of neck muscle is 1.07, while the mutual information between human voice frequency and EMG signal of neck muscle is 0.65. These results show that the relationship between human voice magnitudes and EMG signal of neck muscle is stronger than the relationship between human voice frequencies and EMG signal of neck muscle. Therefore, it is more appropriate to use the EMG signal of neck muscle for controlling loudness of electrolarynx than that of the pitch of electrolarynx.
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Merletti, R., B. Indino, T. Graven-Nielsen e D. Farina. "Surface EMG Crosstalk Evaluated from Experimental Recordings and Simulated Signals". Methods of Information in Medicine 43, n. 01 (2004): 30–35. http://dx.doi.org/10.1055/s-0038-1633419.
Abstract (sommario):
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 with the spike triggered averaging approach. Moreover, surface EMG signals were recorded from electrically stimulated muscles. Results: From the simulation and experimental analysis it was clear that the main determinants of crosstalk are non-propagating signal components, generated by the extinction of the intracellular action potentials at the tendons. Thus, crosstalk signals have a different shape with respect to the signals detected over the active muscle and contain high frequency components. Conclusions: Since crosstalk has signal components different from those dominant in case of detection from near sources, commonly used methods to quantify and reduce crosstalk, such as the cross-correlation coefficient and high-pass temporal filtering, are not reliable. Selectivity of detection systems must be discussed separately as selectivity with respect to propagating and non-propagating signal components. The knowledge about the origin of crosstalk signal constitutes the basis for crosstalk interpretation, quantification, and reduction.
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Shiao, Yaojung, e Thang Hoang. "Exercise Condition Sensing in Smart Leg Extension Machine". Sensors 22, n. 17 (23 agosto 2022): 6336. http://dx.doi.org/10.3390/s22176336.
Abstract (sommario):
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 distinguish the muscle between non-fatigue and fatigue. The results show the EMG changing in the non-fatigue, fatigue, and calf muscle conditions. Additionally, we created algorithms that can successfully sense a user’s muscle conditions during exercise in a leg extension machine, and an evaluation of condition sensing was also conducted. This study provides proof of concept that EMG signals for the sensing of muscle fatigue. Therefore, muscle conditions can be further monitored in exercise or rehabilitation exercise. With these results and experiences, the sensing methods can be extended to other smart exercise machines in the future.
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Neto, Osmar Pinto, e Evangelos A. Christou. "Rectification of the EMG Signal Impairs the Identification of Oscillatory Input to the Muscle". Journal of Neurophysiology 103, n. 2 (febbraio 2010): 1093–103. http://dx.doi.org/10.1152/jn.00792.2009.
Abstract (sommario):
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 surface of the first dorsal interosseus muscle and were manipulated to have an oscillatory input or common input (for pairs of reconstructed EMG signals) at various frequency bands (in Hz: 0–12, 12–30, 30–50, 50–100, 100–150, 150–200, 200–250, 250–300, and 300–400), one at a time. The absolute integral and normalized integral of power, peak power, and peak coherence (for pairs of EMG signals) were quantified from each frequency band. The power spectrum of the interference EMG accurately detected the changes to the oscillatory input to the reconstructed EMG signal, whereas the power spectrum of the rectified EMG did not. Similarly, the EMG–EMG coherence between two interference EMG signals accurately detected the common input to the pairs of reconstructed EMG signals, whereas the EMG–EMG coherence between two rectified EMG signals did not. The frequency band from 12 to 30 Hz in the power spectrum of the rectified EMG and the EMG–EMG coherence between two rectified signals was influenced by the input from 100 to 150 Hz but not from the input from 12 to 30 Hz. The study concludes that the power spectrum of the EMG and EMG–EMG coherence should be performed on interference EMG signals and not on rectified EMG signals because rectification impairs the identification of the oscillatory input to a single EMG signal and the common oscillatory input between two EMG signals.
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Ojha, Anuj. "An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview". Extensive Reviews 3, n. 1 (31 dicembre 2023): 24–37. http://dx.doi.org/10.21467/exr.3.1.8382.
Abstract (sommario):
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 assessments. ML tools such as SVMs, neural networks, KNNs, and decision trees play a crucial role in sorting out complex EMG signals for different pattern recognition tasks. Clustering algorithms also help analyze and interpret muscle activity. EMG and ML find diverse uses in rehabilitation, prosthetics, and human-computer interfaces, though real-time applications come with challenges. They bring significant changes to prosthetic control, human-computer interfaces, and rehabilitation, playing a vital role in pattern recognition. They make prosthetic control more intuitive by understanding user intent from muscle signals, enhance human-computer interaction with responsive interfaces, and support personalized rehabilitation for those with motor impairments. The combination of EMG and ML opens doors for further research into understanding muscle behavior, improving feature extraction, and advancing classification algorithms.
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HAMZI, Maroua, Mohamed BOUMEHRAZ e Rafia HASSANI. "Flexion Angle Estimation from Single Channel Forearm EMG Signals using Effective Features". Electrotehnica, Electronica, Automatica 71, n. 3 (15 agosto 2023): 61–68. http://dx.doi.org/10.46904/eea.23.71.3.1108007.
Abstract (sommario):
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 absolute value (MAV) were computed and selected as representative features. These features were then fed into an LDA classifier to estimate forearm flexion angles. The study aims to compare the effectiveness of features calculated from the EMG signal and those derived from its discrete wavelet decomposition. The experimental results demonstrate the proposed method's efficiency in estimating forearm flexion angles using a single channel of EMG signals, achieving an average classification accuracy of 97.50 % across four gesture classes.
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Pratama, Destra Andika, Yeni Irdayanti e Satrio Aditiyas Sukardi. "EMG Signal Analysis on Flexion Extension Movements of The Hand and Leg Using Matlab". Radiasi : Jurnal Berkala Pendidikan Fisika 16, n. 2 (29 settembre 2023): 61–70. http://dx.doi.org/10.37729/radiasi.v16i2.3373.
Abstract (sommario):
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 wave activity can help in various fields, such as medicine, psychology, and sports. Currently, an electromyograph has been developed which functions as a voltage meter for all muscles to detect muscles in a state of tension and relaxation with the help of a microcontroller. On the Electromyography signal output then to the Arduino uno microcontroller. When using the Muscle Spiker Shield tool with MATLAB, the signals recorded by the tool are imported into the MATLAB software. Then, the data can be processed using various signal analysis techniques, such as filtering, peak detection and statistical processing. Some of the applications that can be done are monitoring leg and hand muscle wave activity during meditation, monitoring muscle wave activity to determine a person's movements, and monitoring muscle wave activity during exercise.
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Rusli, Rusli Ully, Ruslan Ruslan, Sarifin G., Arimbi Arimbi e Mariyal Qibtiyah. "Measurement of Medial Head Gastrocnemius Muscle Contraction Strength in Basic Sepak Takraw Techniques Using Electromyogram Signals". COMPETITOR: Jurnal Pendidikan Kepelatihan Olahraga 15, n. 3 (28 ottobre 2023): 683. http://dx.doi.org/10.26858/cjpko.v15i3.53403.
Abstract (sommario):
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 first EMG signal from the right gastrocnemius medial head muscle, 0.44631mV with an RMS value of 93.009mV, and the largest left gastrocnemius medial head muscle, 0.33889mV with an RMS value of 61.302mV. (2). The subject produced the second largest right gastrocnemius medial head muscle EMG signal of 1.66238mV with an RMS value of 38.7856mV, and the largest left gastrocnemius medial head muscle of 1.37871mV with an RMS value of 25.6827mV. (3). The subject produced the third largest right gastrocnemius medial head muscle EMG signal of 2.02191mV with an RMS value of 76.7969mV, and the largest left gastrocnemius medial head muscle of 0.37397mV with an RMS value of 47.3252mV. It was concluded that the third subject produced the highest muscle EMG strength which occurred in the right gastrocnemius medial head muscle signal and the smallest EMG signal in the left gastrocnemius medial head.
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Liang, Hongbo, Yingxin Yu, Mika Mochida, Chang Liu, Naoya Ueda, Peirang Li e Chi Zhu. "EEG-Based EMG Estimation of Shoulder Joint for the Power Augmentation System of Upper Limbs". Symmetry 12, n. 11 (10 novembre 2020): 1851. http://dx.doi.org/10.3390/sym12111851.
Abstract (sommario):
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 muscle. In addition, the EMG signal of the deltoid muscle is used as the virtual EMG signal to establish the EMG estimation model and evaluate the experimental results. Thus, the shoulder’s power can be augmented by estimated virtual EMG signals for the people wearing an EMG-based power augmentation exoskeleton robot. The estimated EMG signal is expressed via a linear combination of the features of EEG signals extracted by Independent Component Analysis, Short-time Fourier Transform, and Principal Component Analysis. The proposed method was verified experimentally, and the average of the estimation correlation coefficient across different subjects was 0.78 (±0.037). These results demonstrate the feasibility and potential of using EEG signals to provide power augmentation through BMI technology.
Tesi sul tema "Muscle EMG signal":
1
Portero, Pierre. "Adaptation du muscle humain à la microgravité simulée : apport de l'analyse spectrale du signal EMG". Compiègne, 1993. http://www.theses.fr/1993COMP566S.
Abstract (sommario):
Les modifications des paramètres spectraux du signal électromyographique de surface (EMGS) des muscles Triceps Surae (T. S. ) et Tibialis Anterior (T. A. ), au cours d'une épreuve de fatigue isométrique, sont étudiées en relation avec une situation de microgravité simulée chez l'homme, c'est-à-dire lors d'une période de Bed Rest (B. R. ). La revue de la littérature a permis de montrer que : d'une part, lors d'une période de microgravité réelle ou simulée, les muscles à fonction antigravitaire (T. S) sont plus affectés que les muscles à fonction phasique (T. A. ) ; d'autre part, les paramètres spectraux EMGS évoluent différemment lors d'épreuves de fatigue et ceci en fonction de leurs caractéristiques métaboliques musculaires. L'étude a comporté deux phases principales : la première a consisté en la validation du protocole expérimental, la caractérisation des réponses des différents muscles en terme d'évolution des paramètres spectraux EMGS, et l'établissement d'une relation entre ces paramètres spectraux et certains paramètres du métabolisme musculaire exploré par spectroscopie RMN 31P ; la deuxième a été de caractériser l'évolution des paramètres spectraux EMGS en fonction du statut fonctionnel du T. S. Et du T. A. Lors d'une période de B. R. (4 semaines), avec et sans contre-mesures d'exercice musculaire. Les résultats montrent que : grâce à la méthode proposée (épreuve isométrique à 50% de la force maximale volontaire et analyse spectrale du signal EMGS), il est possible de différencier les évolutions des muscles en fonction de leur résistance à la fatigue grâce à l'établissement d'un débit de la fréquence moyenne (MPF) du spectre EMGS (% de diminution de la valeur initiale de la MPF par minute de temps de contraction). Ce débit constitue un index de fatigabilité d'un point de vue EMGS : il existe une relation entre le glissement spectral vers les basses fréquences de L'EMGS et la concentration musculaire en H2PO4 d'une part et H+ d'autre part ; il est possible de différencier ces évolutions par rapport à une situation de microgravité simulée, les différents chefs musculaires du T. S. (les gastrocnemii et le soleus) présentant une augmentation du débit de MPF contrairement au T. A. ; et enfin, lorsqu'un entraînement musculaire est non spécifique de la fonction des muscles étudiés, celui-ci n'est pas suffisant pour contrecarrer les effets du déconditionnement exprimés en terme EMGS. En conclusion, l'analyse spectrale du signal EMGS, lors d'épreuves de fatigue isométrique, apparaît comme étant un outil fiable pour discriminer les muscles par rapport à leur fonction antigravitaire (ou non) et en situation de microgravité simulée. L'aspect non invasif de cette méthode en fait une technique de choix pour le suivi de l'adaptation du muscle dans les domaines de la physiologie spatiale, sportive et de la médecine
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Moss, Christa Wheeler. "INVESTIGATION OF BELOW INJURY MUSCLE SIGNALS AS A COMMAND SOURCE FOR A MOTOR NEUROPROSTHESIS". Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1315924472.
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Rahman, Md Arifur. "A comparative study to explore the advantages of passive exoskeletons by monitoring the muscle activity of workers". Thesis, Högskolan i Gävle, Avdelningen för elektroteknik, matematik och naturvetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-35150.
Abstract (sommario):
Manufacturing and construction workers undertake physically strenuous activities increasing the risk of health problems, disability, and sick leave, leading to lower job attractiveness and job candidate scarcity. In the EU, up to 44 million workers are affected by workplace-related musculoskeletal disorders (MSDs), representing a total annual cost of more than €240 billion. Exoskeleton use could alleviate muscle peak loads and reduce the risks of injury of workers. This work is related to the INTERREG's project "EXSCALLERATE" which aimed to accelerate the adoption of exoskeletons among SMEs. This research presents a comparative study of using exoskeletons by workers while performing different tasks related to their job. The tests evaluate the advantages of using exoskeletons in reducing human muscle activity, thereby, reducing the fatigue and tiredness. The study uses two commercially available exoskeletons, (1) upper body exoskeleton known as Eksovest and (2) lower body exoskeleton known as LegX. For upper body, the study performed drilling tasks at shoulder height and roof drilling positions, whereas, for the lower body, virtual chair position and squatting positions are tested which involved frequent bending of knees. Besides, the experiments based on accuracies of the data collection techniques and compare three volunteer’s body muscle data acquired by EMG sensor. From these comparisons, it is found that the muscle activity can be reduced up to 60% by using these exoskeletons, hence, increasing the work life of the workforce. The results of this study will help create awareness among SMEs towards the adoption of exoskeletons.
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Grönlund, Christer. "Spatio-temporal processing of surface electromyographic signals : information on neuromuscular function and control". Doctoral thesis, Umeå universitet, Institutionen för strålningsvetenskaper, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-958.
Abstract (sommario):
During muscle contraction, electrical signals are generated by the muscle cells. The analysis of those signals is called electromyography (EMG). The EMG signal is mainly determined by physiological factors including so called central factors (central nervous system origin) and peripheral factors (muscle tissue origin). In addition, during the acquisition of EMG signals, technical factors are introduced (measurement equipment origin). The aim of this dissertation was to develop and evaluate methods to estimate physiological properties of the muscles using multichannel surface EMG (MCsEMG) signals. In order to obtain accurate physiological estimates, a method for automatic signal quality estimation was developed. The method’s performance was evaluated using visually classified signals, and the results demonstrated high classification accuracy. A method for estimation of the muscle fibre conduction velocity (MFCV) and the muscle fibre orientation (MFO) was developed. The method was evaluated with synthetic signals and demonstrated high estimation precision at low contraction levels. In order to discriminate between the estimates of MFCV and MFO belonging to single or populations of motor units (MUs), density regions of so called spatial distributions were examined. This method was applied in a study of the trapezius muscle and demonstrated spatial separation of MFCV (as well as MFO) even at high contraction levels. In addition, a method for quantification of MU synchronisation was developed. The performance on synthetic sEMG signals showed high sensitivity on MU synchronisation and robustness to changes in MFCV. The method was applied in a study of the biceps brachii muscle and the relation to force tremor during fatigue. The results showed that MU synchronisation accounted for about 40 % of the force tremor. In conclusion, new sEMG methods were developed to study muscle function and motor control in terms of muscle architecture, muscle fibre characteristics, and processes within the central nervous system.
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Sahki, Nassim. "Méthodologie data-driven de détection séquentielle de ruptures pour des signaux physiologiques". Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0185.
Abstract (sommario):
Cette thèse traite de la problématique de la détection de rupture dans le cadre séquentiel où le signal est supposé être observé en temps réel et le phénomène passe de son état de départ "normal" à un état post-changement "anormal". Le défi de la détection séquentielle est de minimiser le délai de détection, soumis à une limite tolérable de fausse alarme. L'idée est de tester séquentiellement l'existence d'une rupture par l'écriture récursive de la statistique de détection en fonction du score, qui remplace le Log-Likelihood Ratio lorsque la distribution des données est inconnue. La procédure de détection repose ainsi sur une statistique récursive, un seuil de détection et une règle d'arrêt. Dans un premier travail, nous considérons la statistique score-CUSUM et proposons d'évaluer la performance de détection de certains seuils de détection. Deux seuils sont issus de la littérature, et trois nouveaux seuils sont construits par une méthode basée sur la simulation: le premier est constant, le second instantané et le troisième est une version dynamique "data-driven" du précédent. Nous définissons rigoureusement chacun des seuils en mettant en évidence les différentes notions du risque de fausse alarme contrôlé suivant le seuil. Par ailleurs, nous proposons une nouvelle règle d'arrêt corrigée pour réduire le taux de fausse alarme. Nous effectuons ensuite une étude de simulation pour comparer les différents seuils et évaluer la règle d'arrêt corrigée. Nous constatons que le seuil empirique conditionnel est le meilleur pour minimiser le délai de détection tout en maintenant le risque toléré de fausse alarme. Cependant, sur des données réelles, nous recommandons d'utiliser le seuil data-driven car c'est le plus simple à construire et à utiliser pour une implémentation pratique. Dans la seconde partie, nous appliquons notre méthodologie de détection data-driven sur des signaux physiologiques, à savoir des signaux temporels enregistrés au niveau du faisceau supérieur du trapèze de 30 sujets effectuant différentes activités bureautiques. La méthodologie est sujet-activité dépendante; elle inclut l'estimation on-line des paramètres du signal et la construction du seuil data-driven sur le début du signal de chaque activité de chaque sujet. L'objectif était d'identifier des changements de régimes au cours d'une activité afin d'évaluer le niveau de sollicitation du muscle et la variabilité du signal EMG, qui sont liés à la fatigue musculaire. Les résultats obtenus ont confirmé l'aisance de notre méthodologie et la performance et praticité du seuil data-driven proposé. Par la suite, les résultats ont permis la caractérisation de chaque type d'activité en utilisant des modèles mixtesThis thesis deals the problem of change-point detection in the sequential framework where the signal is assumed to be observed in real time and the phenomenon changes from its "normal" starting state to an "abnormal" post-change state. The challenge of sequential detection is to minimize the detection delay, subject to a tolerable false alarm limit. The idea is to sequentially test for the existence of a change-point by recursively writing the detection statistic as a function of the score, which replaces the Log-Likelihood Ratio when the data distribution is unknown. The detection procedure is thus based on a recursive statistic, a detection threshold and a stopping rule. In a first work, we consider the score-CUSUM statistic and propose to evaluate the detection performance of some detection thresholds. Two thresholds come from the literature, and three new thresholds are constructed by a method based on simulation: the first is constant, the second instantaneous and the third is a dynamic "data-driven" version of the previous one. We rigorously define each of the thresholds by highlighting the different notions of the controlled false alarm risk according to the threshold. Moreover, we propose a new corrected stopping rule to reduce the false alarm rate. We then perform a simulation study to compare the different thresholds and evaluate the corrected stopping rule. We find that the conditional empirical threshold is the best to minimize the detection delay while maintaining the tolerated risk of false alarms. However, on real data, we recommend using the data-driven threshold as it is the easiest to build and use for practical implementation. In the second part, we apply our data-driven detection methodology to physiological signals, namely temporal signals recorded at the level of the upper trapezium beam of 30 subjects performing different office activities. The methodology is subject-activity dependent; it includes the on-line estimation of the signal parameters and the construction of the data-driven threshold on the start of the signal of each activity of each subject. The objective was to identify regime changes during an activity in order to assess the level of muscle solicitation and EMG signal variability, which are associated with muscle fatigue. The results obtained confirmed the ease of our methodology and the performance and practicality of the proposed data-driven threshold. Subsequently, the results allowed the characterization of each type of activity using mixed models
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Liu, Ming Ming. "Dynamic muscle force prediction from EMG signals using artificial neural networks". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq20875.pdf.
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Joubert, Michelle. "A finite element model for the investigation of surface EMG signals during dynami contraction". Pretoria : [s.n.], 2007. http://upetd.up.ac.za/thesis/available/etd-09042008-105943/.
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KHALIL, ULLAH XXX. "Extraction of Muscle Anatomical and Physiological Information from Multi-Channel Surface EMG Signals: Applications in Obstetrics". Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2642318.
Abstract (sommario):
Motor Unit (MU) innervation zones (IZs) localization is an important step in several clinical and non-clinical applications including 1) Acquisition of sEMG signal for accurate estimation of its amplitude and other parameters by avoiding placing the electrodes on IZs, 2) Accurate estimation of the EMG-Force relationship, 3) Effective injection of Botulinum Toxin in Post-stroke Spasticity near the IZs, and 4) Guiding the obstetricians to perform episiotomy during child delivery by avoiding cutting near the IZs of External Anal Sphincter (EAS) muscle. The minimal invasive way to identify the location of the IZs generally for any muscle and specifically for EAS muscle is to use multi-channel EMG signals. MU IZs can be detected from the multi-channel sEMG signals, for a fusiform muscle if the signal is acquired with an array of electrodes placed parallel to the muscle fibers, using digital signal and image processing algorithms. As most of the signal processing algorithms work on an adequate quality of the signal, thus before detecting the innervation zone it is made sure that the signal is of good quality. For this purpose, a method based on statistical thresholding of various parameters is proposed to detect the bad channels in the sEMG signals. If the number of the bad consecutive channels are more than 2 then it is suggested to acquire the signal again, otherwise each bad channel is approximated by the interpolation of its neighbor channels. As some background noise is always acquired with the EMG signal so further image enhancement techniques are used to enhance the MUAP propagation region in the spatio-temporal images and suppress the background noise. The MUAP pattern is then detected in the spatio-temporal sEMG images using multi-scale Hessian based filtering and the corresponding MU IZs are identified as the starting point of propagation of the MUAP. A software is also developed which can be used to visualize the signals acquired from EAS, detect and display the IZs and more importantly compute and display the histogram of the IZs and generate reports which will help the obstetrician while performing episiotomy during child delivery to avoid cutting vulnerable regions that may lead to fecal incontinence at later age.
9
Rojas, Martínez Mónica. "Analysis of forearm muscles activity by means of new protocols of multichannel EMG signal recording and processing". Doctoral thesis, Universitat Politècnica de Catalunya, 2012. http://hdl.handle.net/10803/124507.
Abstract (sommario):
Los movimientos voluntarios del cuerpo son controlados por el sistema nervioso central y periférico a través de la contracción de los músculos esqueléticos. La contracción se inicia al liberarse un neurotransmisor sobre la unión neuromuscular, iniciando la propagación de un biopotencial sobre la membrana de las fibras musculares que se desplaza hacia los tendones: el Potencial de Acción de la Unidad Motora (MUAP). La señal electromiográfica de superficie registra la activación continua de dichos potenciales sobre la superficie de la piel y constituye una valiosa herramienta para la investigación, diagnóstico y seguimiento clínico de trastornos musculares, así como para la identificación de la intención movimiento tanto en términos de dirección como de potencia.
En el estudio de las enfermedades del sistema neuromuscular es necesario analizar el nivel de actividad, la capacidad de producción de fuerza, la activación muscular conjunta y la predisposición a la fatiga muscular, todos ellos asociados con factores fisiológicos que determinan la resultante contracción mioeléctrica. Además, el uso de matrices de electrodos facilita la investigación de las propiedades periféricas de las unidades motoras activas, las características anatómicas del músculo y los cambios espaciales en su activación,
ocasionados por el tipo de tarea motora o la potencia de la misma.
El objetivo principal de esta tesis es el diseño e implementación de protocolos experimentales y algoritmos de procesado para extraer información fiable de señales sEMG multicanal en 1 y 2 dimensiones del espacio. Dicha información ha sido interpretada y relacionada con dos patologías específicas de la extremidad superior: Epicondilitis Lateral y Lesión de Esfuerzo Repetitivo. También fue utilizada para identificar la dirección de movimiento y la fuerza asociada a la contracción muscular, cuyos patrones podrían ser de utilidad en aplicaciones donde la señal electromiográfica se utilice para controlar interfaces hombre-máquina como es el caso de terapia física basada en robots, entornos virtuales de rehabilitación o realimentación de la actividad muscular.
En resumen, las aportaciones más relevantes de esta tesis son:
* La definición de protocolos experimentales orientados al registro de señales sEMG en una región óptima del músculo.
* Definición de índices asociados a la co-activación de diferentes músculos
* Identificación de señales artefactuadas en registros multicanal
* Selección de los canales mas relevantes para el análisis
Extracción de un conjunto de características que permita una alta exactitud en la identificación de tareas motoras
Los protocolos experimentales y los índices propuestos permitieron establecer que diversos desequilibrios entre músculos extrínsecos del antebrazo podrían desempeñar un papel clave en la fisiopatología de la epicondilitis lateral. Los resultados fueron consistentes en diferentes ejercicios y pueden definir un marco de evaluación para el seguimiento y evaluación de pacientes en programas de rehabilitación motora.
Por otra parte, se encontró que las características asociadas con la distribución espacial de los MUAPs mejoran la exactitud en la identificación de la intención de movimiento. Lo que es más, las características extraídas de registros sEMG de alta densidad son más
robustas que las extraídas de señales bipolares simples, no sólo por la redundancia de contacto implicada en HD-EMG, sino también porque permite monitorizar las regiones del músculo donde la amplitud de la señal es máxima y que varían con el tipo de ejercicio, permitiendo así una mejor estimación de la activación muscular mediante el análisis de los canales mas relevantes.Voluntary movements are achieved by the contraction of skeletal muscles controlled by the Central and Peripheral Nervous system. The contraction is initiated by the release of a neurotransmitter that promotes a reaction in the walls of the muscular fiber, producing a biopotential known as Motor Unit Action Potential (MUAP) that travels from the neuromuscular junction to the tendons. The surface electromyographic signal records the continuous activation of such potentials over the surface of the skin and constitutes a valuable tool for the diagnosis, monitoring and clinical research of muscular disorders as well as to infer motion intention not only regarding the direction of the movement but also its power. In the study of diseases of the neuromuscular system it is necessary to analyze the level of activity, the capacity of production of strength, the load-sharing between muscles and the probably predisposition to muscular fatigue, all of them associated with physiological factors determining the resultant muscular contraction. Moreover, the use of electrode arrays facilitate the investigation of the peripheral properties of the active Motor Units, the anatomical characteristics of the muscle and the spatial changes induced in their activation of as product of type of movement or power of the contraction.The main objective of this thesis was the design and implementation of experimental protocols, and algorithms to extract information from multichannel sEMG signals in 1 and 2 dimensions of the space. Such information was interpreted and related to pathological events associated to two upper-limb conditions: Lateral Epicondylitis and Repetitive Strain Injury. It was also used to identify the direction of movement and contraction strength which could be useful in applications concerning the use of biofeedback from EMG like in robotic- aided therapies and computer-based rehabilitation training.In summary, the most relevant contributions are:§The definition of experimental protocols intended to find optimal regions for the recording of sEMG signals. §The definition of indices associated to the co- activation of different muscles. §The detection of low-quality signals in multichannel sEMG recordings.§ The selection of the most relevant EMG channels for the analysis§The extraction of a set of features that led to high classification accuracy in the identification of tasks.The experimental protocols and the proposed indices allowed establishing that imbalances between extrinsic muscles of the forearm could play a key role in the pathophysiology of lateral epicondylalgia. Results were consistent in different types of motor task and may define an assessment framework for the monitoring and evaluation of patients during rehabilitation programs.On the other hand, it was found that features associated with the spatial distribution of the MUAPs improve the accuracy of the identification of motion intention. What is more, features extracted from high density EMG recordings are more robust not only because it implies contact redundancy but also because it allows the tracking of (task changing) skin surface areas where EMG amplitude is maximal and a better estimation of muscle activity by the proper selection of the most significant channels.
10
Ayachi, Fouaz Sofiane. "Étude du recrutement des unités motrices par analyse du signal EMG de surface". Compiègne, 2011. http://www.theses.fr/2011COMP1998.
Abstract (sommario):
Le système nerveux central contrôle des mouvements par l’activation des unités de motrices (UM), les plus petites structures fonctionnelles du muscle. Les UM produisent une activité électrique qui peut être détectée par la technique de l’électromyographie de surface (EMGs). Le caractère stochastique du signal EMGs est dû principalement à la superposition des trains de potentiels d’action d’UM (TPAUM) (recrutement spatial), les TPAUM sont caractérisés par leurs instants de décharge (recrutement temporel), ainsi que par la forme des potentiels d’action (PA), qui dépend de certains facteurs méthodologiques et de facteurs intrinsèques au muscle. Le but de cette thèse sera d’étudier les possibilités et les limites d’utilisation de l’analyse de forme de la densité de probabilité des amplitudes (DP) du signal EMGs comme indicateur sur les stratégies de recrutement des UM et du contrôle moteur. Cette analyse semble pertinente puisque le signal EMGs est la somme de processus aléatoires ; les TPAUM. Des modifications sur ces variables devraient être perçues sur le signal composite. La contribution apportée par cette thèse se scinde en deux parties : la proposition d’un modèle complet de génération qui s’inspire de travaux récents issus de la littérature. Ce modèle prend en considération, pour la génération du signal EMGs, de nombreux paramètres physiologiques, anatomiques et nerveux, ainsi que la génération de la force. Cette prise en compte permet d’avoir un meilleur réalisme lors de la simulation. La deuxième partie concerne plusieurs études, en simulation et en expérimental, sur l’analyse des signaux EMGs monopolaires détectés sur le biceps brachial lors de contractions isométriques isotonique (force constante)/anisotonique (force graduée). L’objectif est d’extraire de l’information sur le patron de recrutement des UM à partir de ces signaux. Dans ce contexte, nous avons testé deux approches d’analyse de forme de la DP du signal EMGs qui sont la Statistique d’Ordre Supérieur (SOS), et un algorithme récent, le modèle de forme noyau (CSM : Core Shape Modeling). Les résultats indiquent une forte sensibilité des descripteurs proposés pour la séparation des classes de signaux (force, niveau de synchronisation de décharge), à l’effet filtrant du tissu adipeux et de la composante non propagée. L’efficacité de la classification dépend d’autre part de l’anatomie et du nombre d’UM composant le muscle. Pour les facteurs neuronaux, les deux stratégies de recrutement testées donnent les mêmes tendances avec plus de réalisme physiologique pour l’une d’entre elles. De plus, l’analyse de forme (par SOS), dans certains cas, nous donne des informations sur l’anatomie du muscle considéré, en termes de position de l’UM par rapport à l’électrode. En termes de performance de classification, l’algorithme CSM, donne un résultat relativement meilleur que l’approche SOS, que ce soit en simulation ou en expérimentation. Pour résumer, ce travail de thèse s’inscrit comme une démarche exploratoire du potentiel de l’analyse de forme de la DP du signal EMGs dans l’extraction d’information sur les modalités d’activation musculaire. De nombreux efforts restent à fournir en accord avec les perspectives proposéesThe central nervous system control the movement through the activation of the motors units (MUs), the smallest muscle functional structure. The MU produce electrical activity that can be detected by the technique of surface electromyography (sEMG). The stochastic nature of EMGs signal is mainly due to the superposition of trains of MU action potentials ( MUAPT) (spatial recruitment), the MUAPT are characterized by their discharge frequency (temporal recruitment) and the shape of the action potential (PA), which depends on some factors methodological and intrinsic to the muscle. The aim of this thesis is to study the possibilities and limitations of using the shape analysis of the EMGs signal’s probability density function (DP) as an indicator on MU recruitment strategies and motor control. This analysis seems relevant since the EMGs signal is the sum of random processes, the MUAPT. The contribution of this thesis is divided into two parts : the proposal of a complete model generation inspired by recent work from the literature. This model takes into consideration, for the EMGs signal generation, many physiological, anatomical and nervous parameters, as well as the force generation. Such consideration allows for greater realism in the simulation. The second part concerns several studies, simulation and experimental analysis of EMGs monopolar signals detected on the biceps brachii during isometric contractions isotonic (constant force) / anisotonique (graduated force). The aim is to extract information on the pattern of MU recruitment from these signals. In this context, we tested two approaches based on the shape analysis of the EMGs signal’s DP which are the Higher Order Statistics (HOS), and a recent algorithm, the Core Shape Modeling (CSM). The results indicate a high sensitivity of the proposed descriptors for separating classes of signals (force, sync level of the discharge), the filtering effect of adipose tissue and non propagating component. The efficiency of the classification depends the other hand of the anatomy and the number of MU which composed the muscle. For neuronal factors, both recruitment strategies tested give similar trends with one of them is physiologically more realistic. In addition, analysis of shape (SOS), in some cases, gives us information about muscle anatomy of the concerned muscle, in terms of MU position relative to the electrode. Concerning performance of classification, the algorithm CSM gives a result relatively better than SOS approach, either in simulation or experimentation. To summarize, this thesis is listed as an exploratory process of the shape analysis potential of the EMGs signal’s DP in order to extract the information on the muscular activation’s modalities. A lot of efforts are still required in accordance with the perspectives offered
Libri sul tema "Muscle EMG signal":
1
Stålberg, Erik. Electromyography. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0007.
Abstract (sommario):
Electromyography (EMG) has been used since the 1940s in the diagnosis of neuromuscular disorders. It has particularly developed with the advent of computers and recording equipment with integrated software. This has made methods of analysis fast, robust, and precise, helping to deal with increasing numbers of patients. Indications have changed dynamically over the years, with the development of new EMG methods themselves and complementary methods used in this field for diagnosis such as histochemistry, genetics, and imaging techniques. This chapter focuses mainly on the routine methods based on recordings with concentric or monopolar needle electrodes, but will also briefly review some of the other EMG methods. There is an increasing understanding of the relationship between the generators (muscle fibres) and the recorded signal that helps interpretation of the recordings. The parameters used for quantitation of the EMG signal are discussed. The findings in pathological conditions are discussed and some practical hints on EMG studies given.
2
Maximum speed of forearm flexion practice effects upon surface EMG signal characteristics. 1985.
3
Gunjan, Vinit Kumar, e Bita Mokhlesabadifarahani. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer, 2015.
4
Gunjan, Vinit Kumar, e Bita Mokhlesabadifarahani. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer London, Limited, 2015.
5
Pfurtscheller, Gert, Clemens Brunner e Christa Neuper. EEG-Based Brain–Computer Interfaces. A cura di Donald L. Schomer e Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0047.
Abstract (sommario):
A brain–computer interface (BCI) offers an alternative to natural communication and control by recording brain activity, processing it online, and producing control signals that reflect the user’s intent or the current user state. Therefore, a BCI provides a non-muscular communication channel that can be used to convey messages and commands without any muscle activity. This chapter presents information on the use of different electroencephalographic (EEG) features such as steady-state visual evoked potentials, P300 components, event-related desynchronization, or a combination of different EEG features and other physiological signals for EEG-based BCIs. This chapter also reviews motor imagery as a control strategy, discusses various training paradigms, and highlights the importance of feedback. It also discusses important clinical applications such as spelling systems, neuroprostheses, and rehabilitation after stroke. The chapter concludes with a discussion on different perspectives for the future of BCIs.
6
Shaibani, Aziz. Myotonia. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190661304.003.0021.
Abstract (sommario):
Myotonia is a slow relaxation phase after normal contraction. Patients report dystonia as muscle stiffness and sometimes pain. They usually adapt to it well. Falls due to myotonia may lead to accidents. Examination for percussion myotonia should be part of neuromuscular examination. Percussion of the thenar muscles with the reflex hammer is the most productive method. Electrically silent myotonia is a sign of Brody myopathy. Myotonia may be incidentally discovered during electromyography (EMG). The most important task is to differentiate between myotonia from paramyotonia clinically and electrically. There has been a significant understanding of the underlying channelopathies lately. Severe myotonia respond well to mexiletine.
7
Shaibani, Aziz. Myotonia. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199898152.003.0021.
Abstract (sommario):
Myotonia is a slow relaxation phase of a muscle after normal contraction. Patients report myotonia as muscle stiffness and sometimes pain. They usually adapt to it well. Falls due to myotonia may lead to accidents. Checking for percussion and action myotonia should be part of neuromuscular examination. Electrically silent myotonia is a sign of Brody’s syndrome. Myotonia may be incidentally discovered during EMG. The most important task is to differentiate between myotonia and paramyotonia clinically and electromyographically. Most myotonic disorders are caused by mutations of sodium, and chloride channels. There has been a significant understanding of the underlying channelopathies recently. Severe myotonia respond well to Mexiletine.
8
Nuwer, Marc R., Ronald G. Emerson e Cecil D. Hahn. Principles and Techniques for Long-Term EEG Recording (Epilepsy Monitoring Unit, Intensive Care Unit, Ambulatory). A cura di Donald L. Schomer e Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0031.
Abstract (sommario):
Long-term monitoring is a set of methods for recording electroencephalographic (EEG) signals over a period of 24 hours or longer. Patient video recording is often synchronized to the EEG. Interpretation aids help physicians to identify events, which include automated spike and seizure detection and various trending displays of frequency EEG content. These techniques are used in epilepsy monitoring units for presurgical evaluations and differential diagnosis of seizures versus nonepileptic events. They are used in intensive care units to identify nonconvulsive seizures, to measure the effectiveness of therapy, to assess depth and prognosis in coma, and other applications. The patient can be monitored at home with ambulatory monitoring equipment. Specialized training is needed for competent interpretation of long-term monitoring EEGs. Problems include false-positive events flagged by automated spike and seizure detection software, and muscle and movement artifact contamination during seizures.
9
Shaibani, Aziz. Proximal Arm Weakness. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199898152.003.0012.
Abstract (sommario):
Proximal arm muscles include supra and infra spinatii, pectoralis major and minor, teres major and minor, rhomboids, serratus anterior, deltoids, biceps, and triceps. The main function of these muscles is to lift the arms. The first sign of proximal weakness is difficulty in raising the arms above a horizontal level. Shoulder conditions like supraspinatus tendonitis are often confused as proximal weakness. In myopathies, usually proximal arm weakness is associated with proximal leg weakness. Motor neuron diseases like ALS and SMA and neuropathies like CIDP may present with symmetrical proximal weakness. For differentiation, EMG/NCS is crucial.
10
Shaibani, Aziz. Proximal Arm Weakness. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190661304.003.0012.
Abstract (sommario):
Proximal arm muscles include supra and infra spinatii, pectoralis major and minor, teres major and minor, rhomboids, serratus anterior, deltoids, biceps, and triceps. The main function of these muscles is to abduct the arms. The first sign of proximal weakness is difficulty raising arms above the horizontal level. Shoulder conditions like supraspinatus tendonitis are often confused as proximal weakness. In myopathies, usually proximal arm weakness is associated with proximal leg weakness. Motor neuron diseases (MNDs) like amyotrophic lateral sclerosis (ALS) and spinal muscular atrophy (SMA) and neuropathies like chronic inflammatory demyelinating polyneuropathy (CIDP) may present with symmetrical proximal weakness. For differentiation, electromyography/nerve conduction study (EMG/NCS) is crucial.
Capitoli di libri sul tema "Muscle EMG signal":
1
Jauw, Veronica Lestari, e S. Parasuraman. "Investigation on Upper Limb’s Muscle Utilizing EMG Signal". In Communications in Computer and Information Science, 216–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35197-6_24.
2
Tengshe, Richa, Anubhav Sharma, Harshbardhan Pandey, G. S. Jayant, Laveesh Pant e Binish Fatimah. "Automated Detection for Muscle Disease Using EMG Signal". In Lecture Notes in Networks and Systems, 157–65. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8563-8_16.
3
Barbero, Marco, Roberto Merletti e Alberto Rainoldi. "Features of the Two-Dimensional sEMG Signal: EMG Feature Imaging". In Atlas of Muscle Innervation Zones, 61–69. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2463-2_6.
4
Mohd Azli, Muhammad Amzar Syazani, Mahfuzah Mustafa, Rafiuddin Abdubrani, Amran Abdul Hadi, Syarifah Nor Aqida Syed Ahmad e Zarith Liyana Zahari. "Electromyograph (EMG) Signal Analysis to Predict Muscle Fatigue During Driving". In Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 405–20. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3708-6_35.
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Ibrahim, A. F. T., V. R. Gannapathy, L. W. Chong e I. S. M. Isa. "Analysis of Electromyography (EMG) Signal for Human Arm Muscle: A Review". In Lecture Notes in Electrical Engineering, 567–75. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24584-3_49.
6
Jemaa, Olfa, Sami Bennour, David Daney e Lotfi Romdhane. "Experimental Analysis of Electromyography (EMG) Signal for Evaluation of Isometric Muscle Force". In Lecture Notes in Mechanical Engineering, 183–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27146-6_20.
7
Mishra, Ram Kinker, e Rina Maiti. "Non-Linear Signal Processing Techniques Applied on EMG Signal for Muscle Fatigue Analysis During Dynamic Contraction". In CIRP Design 2012, 193–203. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4507-3_19.
8
Pamungkas, Agung, Lobes Herdiman e Susy Susmartini. "EMG Signal Measurement of Flexor Carpi Radialis Muscle in Post Stroke Patients and Normal Individuals Using Time Domain and Frequency Domain Feature Extraction". In Proceedings of the 4th Borobudur International Symposium on Science and Technology 2022 (BIS-STE 2022), 365–74. Dordrecht: Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-284-2_42.
9
Triantaphyllou, Evangelos. "First Case Study: Predicting Muscle Fatigue from EMG Signals". In Data Mining and Knowledge Discovery via Logic-Based Methods, 277–87. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1630-3_14.
10
Nakajima, Y., S. Yoshinari e S. Tadano. "Surface Conduction Analysis of EMG Signal from Forearm Muscles". In IFMBE Proceedings, 1904–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92841-6_472.
Atti di convegni sul tema "Muscle EMG signal":
1
Forshaw, Robert V., Nicholas W. Snow, Jared M. Wolff, Mansour Zenouzi e Douglas E. Dow. "Electromyography (EMG) Controlled Assistive Rehabilitation System". In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-40238.
Abstract (sommario):
Electromyography (EMG) is a method for monitoring the electrical activity of skeletal muscles. The EMG signal is used to diagnose neuromuscular diseases and muscular injuries. EMG can also be utilized as an indicator of user intent for a muscle contraction for a specific motion. This input signal could be used to control powered exoskeleton prostheses. Limbs with impaired motor function tend to have increased disuse that may result in further muscle weakness. Therapy and other physical activities that increase the use of an impaired limb may contribute to some recovery of motor function. A device that helps to perform activities of daily living may increase usage and enhance recovery. The objective of this project is to make developments toward an EMG controlled assistive rehabilitation system that monitors EMG signals of the bicep and triceps muscles, and drives a motor to assist with arm motion. A motor is used to develop torque that would assist rotations of the arm about the elbow. A pair of EMG sensors (one pair near the biceps and the other near the triceps muscle) transmits electrical activity of the arm to a microcontroller (Raspberry Pi, Raspberry Pi Foundation, United Kingdom). For the prototype, the EMG signal is sampled and rectified within a moving time window to determine the root mean squared (VRMS) value. This value is used by the microcontroller to generate a pulse-width modulated (PWM) signal that controls the motor. Sensors for the motor provide information to an algorithm on the microcontroller. The generated PWM signal is based on the Vrms values for the EMG signal. Testing and analysis has shown a correlation between the EMG Vrms amplitude and muscle generated torque. The EMG controlled assistive rehabilitation system shows promise for assisting motor function for rotations about the elbow. Further algorithmic development is needed to determine the appropriate amount of assistance from the motor for the motor function indicated by user intent.
2
Cakir, Ozlem, Mehmet Engin, Erkan Zeki Engin e Ufuk Yumrukaya. "Investigation of Muscle Fatigue by Processing EMG Signal". In 2009 14th National Biomedical Engineering Meeting. IEEE, 2009. http://dx.doi.org/10.1109/biyomut.2009.5130354.
3
Alim, Onsy Abdul, Mohamed Moselhy e Fatima Mroueh. "EMG signal processing and diagnostic of muscle diseases". In 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). IEEE, 2012. http://dx.doi.org/10.1109/ictea.2012.6462866.
4
Stanek, Kyle, Nathan Barnhart e Yong Zhu. "Control of a Robotic Prosthetic Hand Using an EMG Signal Based Counter". In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86032.
Abstract (sommario):
This research is intended to create a prototype to generate controllable finger movement of a robotic prosthetic hand using Electromyography (EMG) signals. The instrumentation used in this project includes a Bitalino bio-signal sensor kit, skin electrodes, Arduino Uno microcontroller and a prosthetic hand. The Bitalino’s primary function is to serve as a means to obtain the EMG signal. The Arduino Uno’s function is to implement the control algorithm and actuate the robotic hand to move as intended. Using an EMG signal based counter, the method of control deemed fairly reliable since there was proportional control over the hand but it was based on the duration of the muscle in tension rather than how tense the muscle was. The overall control of the hand was generally responsive to the biological signal.
5
Kang, Kimoon, Kiwon Rhee e Hyun-Chool Shin. "A Precise Muscle Activity Onset/Offset Detection via EMG Signal". In 2021 International Conference on Information Networking (ICOIN). IEEE, 2021. http://dx.doi.org/10.1109/icoin50884.2021.9333969.
6
Jain, R. K., S. Datta, S. Majumder, S. Mukherjee, D. Sadhu, S. Samanta e K. Banerjee. "Bio-mimetic Behaviour of IPMC Artificial Muscle Using EMG Signal". In 2010 International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom). IEEE, 2010. http://dx.doi.org/10.1109/artcom.2010.49.
7
Azab, Ahmed M., Ahmed Onsy e Mohamed H. El-Mahlawy. "Design and Development of a Low Cost Prosthetic Arm Control System Based on sEMG Signal". In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51006.
Abstract (sommario):
The aim of this paper is to design and develop a low-cost prosthetic arm based on surface electromyography (sEMG) signal activities of the biceps muscle during upper-limb movement. Different methods are described in the literature, but many problems are encountered in dealing with the online processing of raw EMG (rEMG) signals, such as signal sampling and memory requirements. In this paper, the enveloped EMG (eEMG) signal is used as a control signal that reduces signal sampling rate and memory requirements. The relationship between elbow motion and the activity level of the biceps muscle is characterized using relevant extracted features (root mean square (RMS)). Validation of the proposed low-cost system is conducted using comparison with a professional biomedical system (Bioback MP150). In addition, the estimated equation of movements of each subject is estimated based on the recorded data. From this equation, the angle of motion is calculated as the control of the movement of the robotic arm. Finally, the system proposed in this paper considers the eEMG signal rather than the rEMG signal and deals with the signal based on a sample of 1 KHz rather than 10 KHz. This system reduces our target cost (reduction in hardware requirements and processing time) with acceptable accuracy. The experimental results illustrate that the eEMG signal has the same features-print as that of the rEMG signal, and the eEMG signal can generate the control signal required to move the prosthetic arm.
8
Marri, Kiran, e Ramakrishnan Swaminathan. "Classification of Muscular Nonfatigue and Fatigue Conditions Using Surface EMG Signals and Fractal Algorithms". In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9828.
Abstract (sommario):
The application of surface electromyography (sEMG) technique for muscle fatigue studies is gaining importance in the field of clinical rehabilitation and sports medicine. These sEMG signals are highly nonstationary and exhibit scale-invariant self-similarity structure. The fractal analysis can estimate the scale invariance in the form of fractal dimension (FD) using monofractal (global single FD) or multifractal (local varying FD) algorithms. A comprehensive study of sEMG signal for muscle fatigue using both multifractal and monofractal FD features have not been established in the literature. In this work, an attempt has been made to differentiate sEMG signals recorded nonfatigue and fatigue conditions using monofractal and multifractal algorithms, and machine learning methods. For this purpose, sEMG signals have been recorded from biceps brachii muscles of fifty eight healthy subjects using a standard protocol. The signals of nonfatigue and fatigue region were subjected to eight monofractal (Higuchi, Katz, Petrosian, Sevcik, box counting, multi-resolution length, Hurst and power spectrum density) and two multifractal (detrended fluctuating and detrended moving average) algorithms and 28 FD features were extracted. The features were ranked using conventional and genetic algorithms, and a subset of FD features were further subjected to Naïve Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP) classifiers. The results show that all fractal features are statistically significant. The classification accuracy using feature subset of conventional method is observed to be from 83% to 88%. The highest accuracy of 93.96% was achieved using genetic algorithm and LR classifier combination. The result demonstrated that the performance of multifractal FD features to be more suitable for sEMG signals as compared to monofractal FD features. The fractal analysis of sEMG signals appears to be a very promising biomarker for muscle fatigue classification and can be extended to detection of fatigue onset in varied neuromuscular conditions.
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Matsushita, Misato, Momoyo Ito, Shin-Ichi Ito e Minoru Fukumi. "Verification of Regression Analysis of Muscle Fatigue Using Wrist EMG". In 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2019. http://dx.doi.org/10.1109/ispacs48206.2019.8986350.
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Velásquez, Esteban, Jan Cornelis, Lubos Omelina e Bart Jansen. "Muscle Classification Via Hybrid CNN-LSTM Architecture from Surface EMG Signals". In 2023 24th International Conference on Digital Signal Processing (DSP). IEEE, 2023. http://dx.doi.org/10.1109/dsp58604.2023.10167918.