Relevant bibliographies by topics / EMG FINGER MOVEMENTS

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  2. Dissertations / Theses
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Academic literature on the topic 'EMG FINGER MOVEMENTS'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "EMG FINGER MOVEMENTS"

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NAMAZI, HAMIDREZA. "FRACTAL-BASED CLASSIFICATION OF ELECTROMYOGRAPHY (EMG) SIGNAL IN RESPONSE TO BASIC MOVEMENTS OF THE FINGERS." Fractals 27, no. 03 (May 2019): 1950037. http://dx.doi.org/10.1142/s0218348x19500373.

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Investigating human movements is the most important issue in rehabilitation science. Movements of fingers as one of the major movements of human has been considered by many scientists. Therefore, decoding of finger movements by analysis of related biosignal is very important to consider. In this research, we do the complexity analysis on the Electromyography (EMG) signal that was recorded due to basic movements of fingers. In fact, the EMG signal was classified in case of different movements of fingers by fractal analysis. The result of analysis showed that the EMG signal has the greatest and lowest fractal dimension (complexity) in case of thumb finger flexion and little finger extension. In further attempts, the fractal theory can be applied to investigate the influence of other types of stimulation on variations of the complexity of muscles’ reactions.
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Reilly, Karen T., and Marc H. Schieber. "Incomplete Functional Subdivision of the Human Multitendoned Finger Muscle Flexor Digitorum Profundus: An Electromyographic Study." Journal of Neurophysiology 90, no. 4 (October 2003): 2560–70. http://dx.doi.org/10.1152/jn.00287.2003.

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The human flexor digitorum profundus (FDP) sends tendons to all 4 fingers. One might assume that this multitendoned muscle consists of 4 discrete neuromuscular compartments each acting on a different finger, but recent anatomical and physiological studies raise the possibility that the human FDP is incompletely subdivided. To investigate the functional organization of the human FDP, we recorded electromyographic (EMG) activity by bipolar fine-wire electrodes simultaneously from 2 or 4 separate intramuscular sites as normal human subjects performed isometric, individuated flexion, and extension of each left-hand digit. Some recordings showed EMG activity during flexion of only one of the 4 fingers, indicating that the human FDP has highly selective core regions that act on single fingers. The majority of recordings, however, showed a large amount of EMG activity during flexion of one finger and lower levels of EMG activity during flexion of an adjacent finger. This lesser EMG activity during flexion of adjacent fingers was unlikely to have resulted from recording motor units in neighboring neuromuscular compartments, and instead suggests incomplete functional subdivision of the human FDP. In addition to the greatest agonist EMG activity during flexion of a given finger, most recordings also showed EMG activity during extension of adjacent fingers, apparently serving to stabilize the given finger against unwanted extension. Paradoxically, the functional organization of the human FDP—with both incomplete functional subdivision and highly selective core regions—may contribute simultaneously to the inability of humans to produce completely independent finger movements, and to the greater ability of humans (compared with macaques) to individuate finger movements.
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Goen, Anjana, and D. C. Tiwari. "Pattern Recognition of Individual and Combined Fingers Movements Based Prosthesis Control Using Surface EMG Signals." International Journal of Electrical and Electronics Research 3, no. 4 (December 30, 2015): 70–78. http://dx.doi.org/10.37391/ijeer.030401.

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Prosthesis control system is the need for the amputees or disable person for performing their daily household work and interaction with the outside world. It is the fundamental component of modern prostheses, which uses the myoelectric signals from an individual’s muscles to control the prosthesis movements. The surface electromyogram signals (SEMG) being noninvasive has been used as a control source for multifunction powered prostheses controllers. In spite of the fact there is wide research on the myoelectric control of movements of forearm and hand movements but a little research has been carried out for control of more dexterous individual and combined fingers. With the current demands of such prostheses a challenge that exists is the ability to precisely control a large number of individual and combined finger movements and that too in a computationally efficient manner. This paper investigates accurate and correct discrimination between individual and combined fingers movements using surface myoelectric signals, in order to control the different finger postures of a prosthetic hand. We have SEMG datasets with eight electrodes located on the human forearm and fifteen classes. Various feature sets are extracted and projected in a manner to ensure that maximum separation exists between the finger movements and then fed to the four different classifiers. Practical results along with the statistical significance tests proved the feasibility of the proposed approach with mean classification accuracy greater than 95% in finger movement classification.
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Millar, Christopher, Nazmul Siddique, and Emmett Kerr. "LSTM Network Classification of Dexterous Individual Finger Movements." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 2 (March 20, 2022): 113–24. http://dx.doi.org/10.20965/jaciii.2022.p0113.

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Electrical activity is generated in the forearm muscles during muscular contractions that control dexterous movements of a human finger and thumb. Using this electrical activity as an input to train a neural network for the purposes of classifying finger movements is not straightforward. Low cost wearable sensors i.e., a Myo Gesture control armband (www.bynorth.com), generally have a lower sampling rate when compared with medical grade EMG detection systems e.g., 200 Hz vs 2000 Hz. Using sensors such as the Myo coupled with the lower amplitude generated by individual finger movements makes it difficult to achieve high classification accuracy. Low sampling rate makes it challenging to distinguish between large quantities of subtle finger movements when using a single network. This research uses two networks which enables for the reduction in the number of movements in each network that are being classified; in turn improving the classification. This is achieved by developing and training LSTM networks that focus on the extension and flexion signals of the fingers and a separate network that is trained using thumb movement signal data. By following this method, this research have increased classification of the individual finger movements to between 90 and 100%.
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Sander, Tilmann H., Stefanie Leistner, Heidrun Wabnitz, Bruno-Marcel Mackert, Rainer Macdonald, and Lutz Trahms. "Cross-Correlation of Motor Activity Signals from dc-Magnetoencephalography, Near-Infrared Spectroscopy, and Electromyography." Computational Intelligence and Neuroscience 2010 (2010): 1–8. http://dx.doi.org/10.1155/2010/785279.

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Neuronal and vascular responses due to finger movements were synchronously measured using dc-magnetoencephalography (dcMEG) and time-resolved near-infrared spectroscopy (trNIRS). The finger movements were monitored with electromyography (EMG). Cortical responses related to the finger movement sequence were extracted by independent component analysis from both the dcMEG and the trNIRS data. The temporal relations between EMG rate, dcMEG, and trNIRS responses were assessed pairwise using the cross-correlation function (CCF), which does not require epoch averaging. A positive lag on a scale of seconds was found for the maximum of the CCF between dcMEG and trNIRS. A zero lag is observed for the CCF between dcMEG and EMG. Additionally this CCF exhibits oscillations at the frequency of individual finger movements. These findings show that the dcMEG with a bandwidth up to 8 Hz records both slow and faster neuronal responses, whereas the vascular response is confirmed to change on a scale of seconds.
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Srimaneepong, Viritpon, Artak Heboyan, Azeem Ul Yaqin Syed, Hai Anh Trinh, Pokpong Amornvit, and Dinesh Rokaya. "Recent Advances in Myoelectric Control for Finger Prostheses for Multiple Finger Loss." Applied Sciences 11, no. 10 (May 14, 2021): 4464. http://dx.doi.org/10.3390/app11104464.

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The loss of one or multiple fingers can lead to psychological problems as well as functional impairment. Various options exist for replacement and restoration after hand or finger loss. Prosthetic hand or finger prostheses improve esthetic outcomes and the quality of life for patients. Myoelectrically controlled hand prostheses have been used to attempt to produce different movements. The available articles (original research articles and review articles) on myoelectrically controlled finger/hand prostheses from January 1922 to February 2021 in English were reviewed using MEDLINE/PubMed, Web of Science, and ScienceDirect resources. The articles were searched using the keywords “finger/hand loss”, “finger prosthesis”, “myoelectric control”, and “prostheses” and relevant articles were selected. Myoelectric or electromyography (EMG) signals are read by myoelectrodes and the signals are amplified, from which the muscle’s naturally generated electricity can be measured. The control of the myoelectric (prosthetic) hands or fingers is important for artificial hand or finger movement; however, the precise control of prosthetic hands or fingers remains a problem. Rehabilitation after multiple finger loss is challenging. Implants in finger prostheses after multiple finger loss offer better finger prosthesis retention. This article presents an overview of myoelectric control regarding finger prosthesis for patients with finger implants following multiple finger loss.
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Pamungkas, Daniel Sutopo, Sumantri K. Risandriya, and Adam Rahman. "Classification of Finger Movements Using EMG Signals with PSO SVM Algorithm." International Journal of Advanced Science Computing and Engineering 4, no. 3 (December 27, 2022): 210–19. http://dx.doi.org/10.30630/ijasce.4.3.100.

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Electromyography (EMG) is a signal produced by human muscles when they contract or relax. This signal is widely used as a controller, for example, to control a robotic arm. This study aims to identify the pattern of finger movement in the form of finger movement using a bracelet-shaped device that has eight EMG sensors. This tool is placed on the lower right hand of a subject to get a signal from the EMG. This study uses the support vector machine (SVM) algorithm combined with the particle swarm optimization (PSO) method. For pattern recognition, the properties of the signal in the time domain are used. From this system, the success of pattern recognition is between 68% to 86%.
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Hore, J., B. Wild, and H. C. Diener. "Cerebellar dysmetria at the elbow, wrist, and fingers." Journal of Neurophysiology 65, no. 3 (March 1, 1991): 563–71. http://dx.doi.org/10.1152/jn.1991.65.3.563.

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1. The objective was to investigate in cerebellar patients with dysmetria the kinematic and electromyographic (EMG) characteristics of large and small movements at the elbow, wrist, and finger and thereby to determine the nature of cerebellar dysmetria at distal as well as proximal joints. Flexions were made as fast as possible by moving relatively heavy manipulanda for each joint to the same end position through 5, 30, and 60 degrees. 2. In normal subjects flexions at all joints were accompanied by similar triphasic EMG activity. Movements of increasing amplitude were made with increasing movement durations and increasing durations and magnitudes of initial agonist EMG activity. Antagonist activity often appeared to have two components: one coactive with the initial agonist burst but starting later, the other reaching its peak at about peak velocity. 3. Cerebellar patients with dysmetria showed hypermetria followed by tremor at all three joints when movements were made with the manipulanda. Hypermetria was most marked for aimed movements of small amplitude (5 degrees) at all joints. 4. A characteristic of cerebellar disordered movements, which could be present at all amplitudes and all joints, was an asymmetry with decreased peak accelerations and increased peak decelerations compared to normal movements. Both the asymmetry and the hypermetria for small amplitude movements could be used clinically as sensitive indicators of cerebellar dysfunction. 5. The EMG abnormalities accompanying hypermetria and asymmetry were a more gradual buildup and a prolongation of agonist activity and delayed onset of antagonist activity.(ABSTRACT TRUNCATED AT 250 WORDS)
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Dai, Chenyun, and Xiaogang Hu. "Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings." International Journal of Neural Systems 29, no. 01 (January 10, 2019): 1850025. http://dx.doi.org/10.1142/s0129065718500259.

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The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94–100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed.
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Saikia, Angana, Nayan M. Kakoty, Nabasmita Phukan, Malarvili Balakrishnan, Nitin Sahai, Sudip Paul, and Dinesh Bhatia. "Combination of EMG Features and Stability Index for Finger Movements Recognition." Procedia Computer Science 133 (2018): 92–98. http://dx.doi.org/10.1016/j.procs.2018.07.012.

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Dissertations / Theses on the topic "EMG FINGER MOVEMENTS"

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Ali, Ali Hussein. "An investigation of electromyographic (EMG) control of dextrous hand prostheses for transradial amputees." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/2860.

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There are many amputees around the world who have lost a limb through conflict, disease or an accident. Upper-limb prostheses controlled using surface Electromyography (sEMG) offer a solution to help the amputees; however, their functionality is limited by the small number of movements they can perform and their slow reaction times. Pattern recognition (PR)-based EMG control has been proposed to improve the functional performance of prostheses. It is a very promising approach, offering intuitive control, fast reaction times and the ability to control a large number of degrees of freedom (DOF). However, prostheses controlled with PR systems are not available for everyday use by amputees, because there are many major challenges and practical problems that need to be addressed before clinical implementation is possible. These include lack of individual finger control, an impractically large number of EMG electrodes, and the lack of deployment protocols for EMG electrodes site selection and movement optimisation. Moreover, the inability of PR systems to handle multiple forces is a further practical problem that needs to be addressed. The main aim of this project is to investigate the research challenges mentioned above via non-invasive EMG signal acquisition, and to propose practical solutions to help amputees. In a series of experiments, the PR systems presented here were tested with EMG signals acquired from seven transradial amputees, which is unique to this project. Previous studies have been conducted using non-amputees. In this work, the challenges described are addressed and a new protocol is proposed that delivers a fast clinical deployment of multi-functional upper limb prostheses controlled by PR systems. Controlling finger movement is a step towards the restoration of lost human capabilities, and is psychologically important, as well as physically. A central thread running through this work is the assertion that no two amputees are the same, each suffering different injuries and retaining differing nerve and muscle structures. This work is very much about individualised healthcare, and aims to provide the best possible solution for each affected individual on a case-by-case basis. Therefore, the approach has been to optimise the solution (in terms of function and reliability) for each individual, as opposed to developing a generic solution, where performance is optimised against a test population. This work is unique, in that it contributes to improving the quality of life for each individual amputee by optimising function and reliability. The main four contributions of the thesis are as follows: 1- Individual finger control was achieved with high accuracy for a large number of finger movements, using six optimally placed sEMG channels. This was validated on EMG signals for ten non-amputee and six amputee subjects. Thumb movements were classified successfully with high accuracy for the first time. The outcome of this investigation will help to add more movements to the prosthesis, and reduce hardware and computational complexity. 2- A new subject-specific protocol for sEMG site selection and reliable movement subset optimisation, based on the amputee’s needs, has been proposed and validated on seven amputees. This protocol will help clinicians to perform an efficient and fast deployment of prostheses, by finding the optimal number and locations of EMG channels. It will also find a reliable subset of movements that can be achieved with high performance. 3- The relationship between the force of contraction and the statistics of EMG signals has been investigated, utilising an experimental design where visual feedback from a Myoelectric Control Interface (MCI) helped the participants to produce the correct level of force. Kurtosis values were found to decrease monotonically when the contraction level increased, thus indicating that kurtosis can be used to distinguish different forces of contractions. 4- The real practical problem of the degradation of classification performance as a result of the variation of force levels during daily use of the prosthesis has been investigated, and solved by proposing a training approach and the use of a robust feature extraction method, based on the spectrum. The recommendations of this investigation improve the practical robustness of prostheses controlled with PR systems and progress a step further towards clinical implementation and improving the quality of life of amputees. The project showed that PR systems achieved a reliable performance for a large number of amputees, taking into account real life issues such as individual finger control for high dexterity, the effect of force level variation, and optimisation of the movements and EMG channels for each individual amputee. The findings of this thesis showed that the PR systems need to be appropriately tuned before usage, such as training with multiple forces to help to reduce the effect of force variation, aiming to improve practical robustness, and also finding the optimal EMG channel for each amputee, to improve the PR system’s performance. The outcome of this research enables the implementation of PR systems in real prostheses that can be used by amputees.
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UTTAM, GAURAV. "NON NEGATIVE MATRIX FACTORISATION FOR IDENTIFICATION OF EMG FINGER MOVEMENTS." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16023.

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Electromyography (EMG) signals are becoming important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, etc. This work present classification of Surface Electromyography (sEMG) using four different methods, namely, Artificial Neural Network (ANN), Discriminant analysis, Multi-Support Vector Machine (m-SVM) and K-Nearest Neighbour (KNN) method and compares the accuracy of classification of these methods. Also, all the four methods use two methods, namely the nonnegative matrix factorization (NMF) and principal component analysis (PCA) for dimensionality reduction of data. MATLAB simulations show that ANN classifier gives the best accuracy among the four classifier used in this work. The percentage accuracy of ANN classifier is 95% using NMF method and 84.5% using PCA method.
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Andrews, ALEXANDER. "Finger Movement Classification Using Forearm EMG Signals." Thesis, 2008. http://hdl.handle.net/1974/1574.

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To a person with an upper limb amputation or congenital defect, a well-functioning prosthesis can open the door to many work and life opportunities. A fundamental component of many modern prostheses is the myoelectric control system, which uses the myoelectric signals from an individual's muscles to control prosthetic movements. Though much research has been done involving the myoelectric control of arm and gross hand movements, more dexterous finger control has not received the same attention. Consequently, the goal of this study was to determine an optimal approach to the myoelectric signal classification of a set of typing motions. Two different movement sets involving the fingers of the right hand were tested: one involving digits two through five (4F - "four finger"), and the other involving digits one and two (FT - "finger/thumb"). Myoelectric data were collected from the forearm muscles of twelve normally-limbed subjects as they performed a set of typing tasks. These data were then used to test a series of classification systems, each comprising a different combination of system element choices. The best classification system over all subjects and the best classification system for each subject were determined for both movement sets. The optimal subject-specific classification systems yielded classification accuracies of 92.8 ± 2.7% for the 4F movement set and 93.6 ± 6.1% for the FT movement set, whereas the optimal overall classification systems yielded significantly lower performance (p<0.05): 89.6 ± 3.4% for the 4F movement set and 89.8 ± 8.5% for the FT movement set. No significant difference in classification accuracy was found between movement sets (p=0.802). A two-way repeated measures ANOVA (α=0.05) was used to determine both significance results.
Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-10-31 14:59:43.151
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Liu, Yung-Chun, and 劉勇均. "EEG Signal Analysis System for Finger Movement Detection." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/23412415412673544100.

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碩士
國立成功大學
資訊工程學系碩博士班
92
Many neurological diseases, such as stroke and spinal cord injury, disrupt the connections between brain cortex and muscles. Besides, some other diseases may destruct the muscle and make it functionless. All these diseases interfere with the voluntary movements of the subjects and influence their ability to accomplish the attempted task. Brain-computer interface (BCI), which defines an artificial alternative output from the brain cortex to make communication with their surrounding targets, can improve above deficits. 　　The most common way of BCI is to give control signals based on the analysis of Electroencephalogram (EEG) signals. And the recognition of finger movements has been one of the most important issues in this field. In the previous researches, the length of EEG trials for analysis were usually between 4 to 10 seconds, therefore it would have difficulties in real-time applications. For this reason, we study the technique of analyzing the EEG signals which have the length of one second, and construct a real-time EEG recognition system based on it for detecting finger movements. We adopt the strategy, named Active Time Segment Selection, to pick the most appropriate time segment of the EEG trial for the recognition of finger movements. And the classifier is trained with the information of this segment in all trials. The integrated processes with the above-mentioned functions form a two-staged recognition system to classify the finger motions in real-time. Besides, we propose an automatic approach to provide statistical analysis on the results of recognition in each stage. 　　From the results of the experiment, it has shown that our system can distinguish a finger movement or a non-movement from the input EEG signal sequences, and further recognize the movement as a left or a right one successfully. We expect to use the system in controlling clinical assistive devices in the future, and benefit the subjects with neurological diseases or limb disabilities.
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Book chapters on the topic "EMG FINGER MOVEMENTS"

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Al-Timemy, Ali, Guido Bugmann, Nicholas Outram, Javier Escudero, and Hai Li. "Finger Movements Classification for the Dexterous Control of Upper Limb Prosthesis Using EMG Signals." In Advances in Autonomous Robotics, 434–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32527-4_47.

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Pan, Lizhi, Xinjun Sheng, Dingguo Zhang, and Xiangyang Zhu. "Simultaneous and Proportional Estimation of Finger Joint Angles from Surface EMG Signals during Mirrored Bilateral Movements." In Intelligent Robotics and Applications, 493–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40852-6_50.

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Krishnan, Shravan, Ravi Akash, Dilip Kumar, Rishab Jain, Karthik Murali Madhavan Rathai, and Shantanu Patil. "Finger Movement Pattern Recognition from Surface EMG Signals Using Machine Learning Algorithms." In ICTMI 2017, 75–89. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1477-3_7.

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Hasan, Bashar Awwad Shiekh. "On the Temporal Behavior of EEG Recorded during Real Finger Movement." In Machine Learning and Data Mining in Pattern Recognition, 335–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23199-5_25.

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Phukan, Nabasmita, and Nayan M. Kakoty. "Sample Entropy Based Selection of Wavelet Decomposition Level for Finger Movement Recognition Using EMG." In Advances in Intelligent Systems and Computing, 61–73. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1708-8_6.

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Ecard, V. C., L. L. Menegaldo, and L. F. Oliveira. "NNMF Analysis to Individual Identification of Fingers Movements Using Force Feedback and HD-EMG." In XXVII Brazilian Congress on Biomedical Engineering, 477–83. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-70601-2_74.

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Wang, Boyu, and Feng Wan. "Classification of Single-Trial EEG Based on Support Vector Clustering during Finger Movement." In Advances in Neural Networks – ISNN 2009, 354–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01510-6_41.

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Wafeek, Nourhan, Roaa I. Mubarak, and Mohamed E. Elbably. "A Novel EEG Classification Technique Based on Particle Swarm Optimization for Hand and Finger Movements." In Advances in Intelligent Systems and Computing, 115–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31129-2_11.

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Wafeek, Nourhan, Mohamed E. Elbably, and Roaa I. Mubarak. "FPGA Implementation of EEG Classification System for Arm and Fingers Movements Based on Particle Swarm Algorithm." In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021), 335–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76346-6_31.

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Hari, Riitta, and Aina Puce. "Motor Function." In MEG - EEG Primer, edited by Riitta Hari and Aina Puce, 336—C17P70. 2nd ed. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/med/9780197542187.003.0017.

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Abstract This chapter highlights neurophysiological recordings from the human motor system from skeletal muscles to the cerebral cortex, as well as interactions between these two systems. A brief history of the field introduces the movement-related readiness potentials and fields for upper- and lower-limb acts. Studies of the coherence between muscular and brain activity are especially illuminating and technically robust, as exemplified by cortex–muscle coherence, corticokinematic coherence, and corticovocal coherence. The discussion extends beyond simple motor acts, such as button presses and repetitive finger movements, examining more complex motor actions and the role of predictive coding and proprioceptive feedback in motor control. The reader is also reminded about the importance of motor equivalence and inhibition in selecting the best motor pattern in each situation.
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Conference papers on the topic "EMG FINGER MOVEMENTS"

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Findik, Mucahit, Seyma Yilmaz, and Mehmet Koseoglu. "Random Forest Classification of Finger Movements using Electromyogram (EMG) Signals." In 2020 IEEE SENSORS. IEEE, 2020. http://dx.doi.org/10.1109/sensors47125.2020.9278619.

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Anam, Khairul, Harun Ismail, Faruq S. Hanggara, Cries Avian, and Singgih Bekti Worsito. "Cross Validation Configuration on k-NN for Finger Movements using EMG signals." In 2021 International Conference on Instrumentation, Control, and Automation (ICA). IEEE, 2021. http://dx.doi.org/10.1109/ica52848.2021.9625699.

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Tsenov, G., A. H. Zeghbib, F. Palis, N. Shoylev, and V. Mladenov. "Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals." In 2006 8th Seminar on Neural Network Applications in Electrical Engineering. IEEE, 2006. http://dx.doi.org/10.1109/neurel.2006.341203.

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Fu, Jianting, Liang Xiong, Xiaoying Song, Zhuo Yan, and Yi Xie. "Identification of finger movements from forearm surface EMG using an augmented probabilistic neural network." In 2017 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2017. http://dx.doi.org/10.1109/sii.2017.8279278.

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Kanitz, G. R., C. Antfolk, C. Cipriani, F. Sebelius, and M. C. Carrozza. "Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090465.

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Mendez, V., L. Pollina, F. Artoni, and S. Micera. "Deep Learning with Convolutional Neural Network for Proportional Control of Finger Movements from surface EMG Recordings." In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441095.

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Malesevic, Nebojsa, Dimitrije Markovic, Gunter Kanitz, Marco Controzzi, Christian Cipriani, and Christian Antfolk. "Decoding of individual finger movements from surface EMG signals using vector autoregressive hierarchical hidden Markov models (VARHHMM)." In 2017 International Conference on Rehabilitation Robotics (ICORR). IEEE, 2017. http://dx.doi.org/10.1109/icorr.2017.8009463.

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Junlasat, Apiwat, Tanatawan Kamolklang, Peerapong Uthansakul, and Monthippa Uthansakul. "Finger Movement Detection Based on Multiple EMG Positions." In 2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2019. http://dx.doi.org/10.1109/iciteed.2019.8929980.

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Andrews, A., E. Morin, and L. McLean. "Optimal Electrode Configurations for Finger Movement Classification using EMG." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5332520.

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Haris, Mohd, Pavan Chakraborty, and B. Venkata Rao. "EMG signal based finger movement recognition for prosthetic hand control." In 2015 Communication, Control and Intelligent Systems (CCIS). IEEE, 2015. http://dx.doi.org/10.1109/ccintels.2015.7437907.

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You might also be interested in the extended bibliographies on the topic 'EMG FINGER MOVEMENTS' for particular source types:
Journal articles
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