Добірка наукової літератури з теми "Muscle EMG signal"
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Статті в журналах з теми "Muscle EMG signal"
Strzecha, Krzysztof, Marek Krakós, Bogusław Więcek, Piotr Chudzik, Karol Tatar, Grzegorz Lisowski, Volodymyr Mosorov, and Dominik Sankowski. "Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences." Applied Sciences 11, no. 10 (May 19, 2021): 4625. http://dx.doi.org/10.3390/app11104625.
Повний текст джерелаArifin, 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 (September 26, 2014): 1450054. http://dx.doi.org/10.4015/s1016237214500549.
Повний текст джерелаMerletti, 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.
Повний текст джерелаShiao, Yaojung, and Thang Hoang. "Exercise Condition Sensing in Smart Leg Extension Machine." Sensors 22, no. 17 (August 23, 2022): 6336. http://dx.doi.org/10.3390/s22176336.
Повний текст джерелаNeto, 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 (February 2010): 1093–103. http://dx.doi.org/10.1152/jn.00792.2009.
Повний текст джерелаOjha, Anuj. "An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview." Extensive Reviews 3, no. 1 (December 31, 2023): 24–37. http://dx.doi.org/10.21467/exr.3.1.8382.
Повний текст джерелаHAMZI, Maroua, Mohamed BOUMEHRAZ, and Rafia HASSANI. "Flexion Angle Estimation from Single Channel Forearm EMG Signals using Effective Features." Electrotehnica, Electronica, Automatica 71, no. 3 (August 15, 2023): 61–68. http://dx.doi.org/10.46904/eea.23.71.3.1108007.
Повний текст джерелаPratama, 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 (September 29, 2023): 61–70. http://dx.doi.org/10.37729/radiasi.v16i2.3373.
Повний текст джерелаRusli, 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 (October 28, 2023): 683. http://dx.doi.org/10.26858/cjpko.v15i3.53403.
Повний текст джерелаLiang, Hongbo, Yingxin Yu, Mika Mochida, Chang Liu, Naoya Ueda, Peirang Li, and Chi Zhu. "EEG-Based EMG Estimation of Shoulder Joint for the Power Augmentation System of Upper Limbs." Symmetry 12, no. 11 (November 10, 2020): 1851. http://dx.doi.org/10.3390/sym12111851.
Повний текст джерелаДисертації з теми "Muscle EMG signal"
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.
Повний текст джерелаMoss, 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.
Повний текст джерелаRahman, 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.
Повний текст джерелаGrönlund, 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.
Повний текст джерелаSahki, 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.
Повний текст джерелаThis 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
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.
Повний текст джерелаJoubert, 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/.
Повний текст джерелаKHALIL, 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.
Повний текст джерелаRojas, 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.
Повний текст джерелаVoluntary 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.
Ayachi, Fouaz Sofiane. "Étude du recrutement des unités motrices par analyse du signal EMG de surface." Compiègne, 2011. http://www.theses.fr/2011COMP1998.
Повний текст джерелаThe 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
Книги з теми "Muscle EMG signal"
Stålberg, Erik. Electromyography. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0007.
Повний текст джерелаMaximum speed of forearm flexion practice effects upon surface EMG signal characteristics. 1985.
Знайти повний текст джерелаGunjan, Vinit Kumar, and Bita Mokhlesabadifarahani. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer, 2015.
Знайти повний текст джерелаGunjan, Vinit Kumar, and Bita Mokhlesabadifarahani. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer London, Limited, 2015.
Знайти повний текст джерелаPfurtscheller, Gert, Clemens Brunner, and Christa Neuper. EEG-Based Brain–Computer Interfaces. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0047.
Повний текст джерелаShaibani, Aziz. Myotonia. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190661304.003.0021.
Повний текст джерелаShaibani, Aziz. Myotonia. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199898152.003.0021.
Повний текст джерелаNuwer, Marc R., Ronald G. Emerson, and Cecil D. Hahn. Principles and Techniques for Long-Term EEG Recording (Epilepsy Monitoring Unit, Intensive Care Unit, Ambulatory). Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0031.
Повний текст джерелаShaibani, Aziz. Proximal Arm Weakness. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199898152.003.0012.
Повний текст джерелаShaibani, Aziz. Proximal Arm Weakness. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190661304.003.0012.
Повний текст джерелаЧастини книг з теми "Muscle EMG signal"
Jauw, Veronica Lestari, and 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.
Повний текст джерелаTengshe, Richa, Anubhav Sharma, Harshbardhan Pandey, G. S. Jayant, Laveesh Pant, and 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.
Повний текст джерелаBarbero, Marco, Roberto Merletti, and 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.
Повний текст джерелаMohd Azli, Muhammad Amzar Syazani, Mahfuzah Mustafa, Rafiuddin Abdubrani, Amran Abdul Hadi, Syarifah Nor Aqida Syed Ahmad, and 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.
Повний текст джерелаIbrahim, A. F. T., V. R. Gannapathy, L. W. Chong, and 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.
Повний текст джерелаJemaa, Olfa, Sami Bennour, David Daney, and 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.
Повний текст джерелаMishra, Ram Kinker, and 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.
Повний текст джерелаPamungkas, Agung, Lobes Herdiman, and 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.
Повний текст джерелаTriantaphyllou, 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.
Повний текст джерелаNakajima, Y., S. Yoshinari, and 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.
Повний текст джерелаТези доповідей конференцій з теми "Muscle EMG signal"
Forshaw, Robert V., Nicholas W. Snow, Jared M. Wolff, Mansour Zenouzi, and 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.
Повний текст джерелаCakir, Ozlem, Mehmet Engin, Erkan Zeki Engin, and 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.
Повний текст джерелаAlim, Onsy Abdul, Mohamed Moselhy, and 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.
Повний текст джерелаStanek, Kyle, Nathan Barnhart, and 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.
Повний текст джерелаKang, Kimoon, Kiwon Rhee, and 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.
Повний текст джерелаJain, R. K., S. Datta, S. Majumder, S. Mukherjee, D. Sadhu, S. Samanta, and 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.
Повний текст джерелаAzab, Ahmed M., Ahmed Onsy, and 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.
Повний текст джерелаMarri, Kiran, and 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.
Повний текст джерелаMatsushita, Misato, Momoyo Ito, Shin-Ichi Ito, and 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.
Повний текст джерелаVelásquez, Esteban, Jan Cornelis, Lubos Omelina, and 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.
Повний текст джерела