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Academic literature on the topic 'Analyse des signaux HD-sEMG'

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Published: 7 September 2022

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Journal articles on the topic "Analyse des signaux HD-sEMG"

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ALI, MD ASRAF, KENNETH SUNDARAJ, R. BADLISHAH AHMAD, NIZAM UDDIN AHAMED, MD ANAMUL ISLAM, and SEBASTIAN SUNDARAJ. "sEMG ACTIVITIES OF THE THREE HEADS OF THE TRICEPS BRACHII MUSCLE DURING CRICKET BOWLING." Journal of Mechanics in Medicine and Biology 16, no. 05 (August 2016): 1650075. http://dx.doi.org/10.1142/s0219519416500755.

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The aim of the present study was to analyze the surface electromyography (sEMG) activities generated by the three heads of the triceps brachii (TB) muscle among the different phases during fast and spin bowling. sEMG signals from the lateral, long and medial heads of the TB from 20 bowlers were measured individually during bowling. To analyze the sEMG activities, the root mean square (RMS) value in each bowling phase for every trial per bowler was calculated from the sEMG signals from the three heads of the TB. Higher sEMG activities at the three heads of the TB were found during the fifth phase followed by the sixth, seventh, third, fourth, second and first phases in both types of bowling. sEMG activities were significantly different among the three heads of the TB and among the seven bowling phases for both bowling types at an alpha level of [Formula: see text]. These findings will be of particular importance for assessing different physical therapies for the three headed TB muscle which can improve the performance in ball delivery of cricket bowlers.
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Herrera, Efrén V., Edgar M. Vela, Victor A. Arce, Katherine G. Molina, Nathaly S. Sánchez, Paúl J. Daza, Luis E. Herrera, and Douglas A. Plaza. "Temperature Influences at the Myoelectric Level in the Upper Extremities of the Human Body." Open Biomedical Engineering Journal 14, no. 1 (October 23, 2020): 28–42. http://dx.doi.org/10.2174/1874120702014010028.

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Objective: Nowadays, surface electromyography (sEMG) signals are used for a variety of medical interaction applications along with hardware and software interfaces. These signals require advanced techniques with different approaches that enable processing the sEMG signals acquired in the upper limb muscles of a person. Methods: The purpose of this article is to analyze the sEMG signals of the upper limb of a person exposed to temperature changes to envisage its behavior and its nature. The anticipated diagnostic is a key factor in the health field. Therefore, it is very important to develop more precise methods and techniques. For the present study, a heat chamber that allows controlling the temperature of the area where the patient rests his or her hand was designed and implemented. With the appropriate hardware interfaces, the sEMG signals of the hand were registered with MatLab/Simulink software for further analysis. The article explains the analysis and develops knowledge, through a probabilistic approach regarding the change in the sEMG signals. Results: The results show that there is an activity in the sEMG signal response due to changes in temperature and it is feasible to detect them using the proposed method. Conclusion: This finding contributes to research that seeks to characterize temperature’s effect in the biomedical field.
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Dorgham, Osama, Ibrahim Al-Mherat, Jawdat Al-Shaer, Sulieman Bani-Ahmad, and Stephen Laycock. "Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network." Future Internet 11, no. 1 (January 21, 2019): 25. http://dx.doi.org/10.3390/fi11010025.

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Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.
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KARTHICK, P. A., G. VENUGOPAL, and S. RAMAKRISHNAN. "ANALYSIS OF SURFACE EMG SIGNALS UNDER FATIGUE AND NON-FATIGUE CONDITIONS USING B-DISTRIBUTION BASED QUADRATIC TIME FREQUENCY DISTRIBUTION." Journal of Mechanics in Medicine and Biology 15, no. 02 (April 2015): 1540028. http://dx.doi.org/10.1142/s021951941540028x.

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In this paper, an attempt has been made to analyze surface electromyography (sEMG) signals under non-fatigue and fatigue conditions using time-frequency based features. The sEMG signals are recorded from biceps brachii muscle of 50 healthy volunteers under well-defined protocol. The pre-processed signals are divided into six equal epochs. The first and last segments are considered as non-fatigue and fatigue zones respectively. Further, these signals are subjected to B-distribution based quadratic time-frequency distribution (TFD). Time frequency based features such as instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are extracted. The expression of spectral entropy is modified to obtain instantaneous spectral entropy (ISPEn) from the time-frequency spectrum. The results show that all the extracted features are distinct in both conditions. It is also observed that the values of all features are higher in non-fatigue zone compared to fatigue condition. It appears that this method is useful in analysing various neuromuscular conditions using sEMG signals.
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Hari, Lakshmi M., Gopinath Venugopal, and Swaminathan Ramakrishnan. "Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 236, no. 2 (October 11, 2021): 208–17. http://dx.doi.org/10.1177/09544119211048011.

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In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.
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Pietraszewski, Przemysław, Artur Gołaś, Michał Krzysztofik, Marta Śrutwa, and Adam Zając. "Evaluation of Lower Limb Muscle Electromyographic Activity during 400 m Indoor Sprinting among Elite Female Athletes: A Cross-Sectional Study." International Journal of Environmental Research and Public Health 18, no. 24 (December 14, 2021): 13177. http://dx.doi.org/10.3390/ijerph182413177.

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The purpose of this cross-sectional study was to analyze changes in normalized surface electromyography (sEMG) signals for the gastrocnemius medialis, biceps femoris, gluteus maximus, tibialis anterior, and vastus lateralis muscles occurring during a 400 m indoor sprint between subsequent curved sections of the track. Ten well-trained female sprinters (age: 21 ± 4 years; body mass: 47 ± 5 kg; body height: 161 ± 7 cm; 400 m personal best: 52.4 ± 1.1 s) performed an all-out 400 m indoor sprint. Normalized sEMG signals were recorded bilaterally from the selected lower limb muscles. The two-way ANOVA (curve × side) revealed no statistically significant interaction. However, the main effect analysis showed that normalized sEMG signals significantly increased in subsequent curves run for all the studied muscles: gastrocnemius medialis (p = 0.003), biceps femoris (p < 0.0001), gluteus maximus (p = 0.044), tibialis anterior (p = 0.001), and vastus lateralis (p = 0.023), but differences between limbs were significant only for the gastrocnemius medialis (p = 0.012). The results suggest that the normalized sEMG signals for the lower limb muscles increased in successive curves during the 400 m indoor sprint. Moreover, the gastrocnemius medialis of the inner leg is highly activated while running curves; therefore, it should be properly prepared for high demands, and attention should be paid to the possibility of the occurrence of a negative adaptation, such as asymmetries.
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Wu, Na, Hao JIN, Xiachuan Pei, Shurong Dong, Jikui Luo, Ruijian Yan, and Gang Feng. "Gesture recognition system based on CNN-IndRNN and OpenBCI." MATEC Web of Conferences 336 (2021): 06003. http://dx.doi.org/10.1051/matecconf/202133606003.

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Surface electromyography (sEMG), as a key technology of non-invasive muscle computer interface, is an important method of human-computer interaction. We proposed a CNN-IndRNN (Convolutional Neural Network-Independent Recurrent Neural Network) hybrid algorithm to analyse sEMG signals and classify hand gestures. Ninapro’s dataset of 10 volunteers was used to develop the model, and by using only one time-domain feature (root mean square of sEMG), an average accuracy of 87.43% on 18 gestures is achieved. The proposed algorithm obtains a state-of-the-art classification performance with a significantly reduced model. In order to verify the robustness of the CNN-IndRNN model, a compact real¬time recognition system was constructed. The system was based on open-source hardware (OpenBCI) and a custom Python-based software. Results show that the 10-subject rock-paper-scissors gesture recognition accuracy reaches 99.1%.
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Zhu, Jianfei, Chunzhi Yi, Baichun Wei, Chifu Yang, Zhen Ding, and Feng Jiang. "The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy." Applied Sciences 11, no. 9 (April 23, 2021): 3821. http://dx.doi.org/10.3390/app11093821.

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Surface Electromyography (sEMG) enables an intuitive control of wearable robots. The muscle fatigue-induced changes of sEMG signals might limit the long-term usage of the sEMG-based control algorithms. This paper presents the performance deterioration of sEMG-based gait phase classifiers, explains the deterioration by analyzing the time-varying changes of the extracted features, and proposes a training strategy that can improve the classifiers’ robustness against muscle fatigue. In particular, we first select some features that are commonly used in fatigue-related studies and use them to classify gait phases under muscle fatigue. Then, we analyze the time-varying characteristics of extracted features, with the aim of explaining the performance of the classifiers. Finally, we propose a training strategy that effectively improves the robustness against muscle fatigue, which contributes to an easy-to-use method. Ten subjects performing prolonged walking are recruited. Our study contributes to a novel perspective of designing gait phase classifiers under muscle fatigue.
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Feng, Fabo, R. Paul Butler, Steven S. Vogt, Matthew S. Clement, C. G. Tinney, Kaiming Cui, Masataka Aizawa, et al. "3D Selection of 167 Substellar Companions to Nearby Stars." Astrophysical Journal Supplement Series 262, no. 1 (August 26, 2022): 21. http://dx.doi.org/10.3847/1538-4365/ac7e57.

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Abstract We analyze 5108 AFGKM stars with at least five high-precision radial velocity points, as well as Gaia and Hipparcos astrometric data, utilizing a novel pipeline developed in previous work. We find 914 radial velocity signals with periods longer than 1000 days. Around these signals, 167 cold giants and 68 other types of companions are identified, through combined analyses of radial velocity, astrometry, and imaging data. Without correcting for detection bias, we estimate the minimum occurrence rate of the wide-orbit brown dwarfs to be 1.3%, and find a significant brown-dwarf valley around 40 M Jup. We also find a power-law distribution in the host binary fraction beyond 3 au, similar to that found for single stars, indicating no preference of multiplicity for brown dwarfs. Our work also reveals nine substellar systems (GJ 234 B, GJ 494 B, HD 13724 b, HD 182488 b, HD 39060 b and c, HD 4113 C, HD 42581 d, HD 7449 B, and HD 984 b) that have previously been directly imaged, and many others that are observable at existing facilities. Depending on their ages, we estimate that an additional 10–57 substellar objects within our sample can be detected with current imaging facilities, extending the imaged cold (or old) giants by an order of magnitude.
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Anders, Christoph, Klaus Sander, Frank Layher, Steffen Patenge, and Raimund W. Kinne. "Temporal and spatial relationship between gluteal muscle Surface EMG activity and the vertical component of the ground reaction force during walking." PLOS ONE 16, no. 5 (May 26, 2021): e0251758. http://dx.doi.org/10.1371/journal.pone.0251758.

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Background Optimized temporal and spatial activation of the gluteal intermuscular functional unit is essential for steady gait and minimized joint loading. Research question To analyze the temporal relationship between spatially resolved surface EMG (SEMG) of the gluteal region and the corresponding ground reaction force (GRF). Methods Healthy adults (29♀; 25♂; age 62.6±7.0 years) walked at their self-selected slow, normal, and fast walking speeds on a 10 m walkway (ten trials/speed). Bilateral paired eight-electrode strips were horizontally aligned at mid-distance of the vertical line between greater trochanter and iliac crest. Concerning the ventral to dorsal direction, the center of each strip was placed on this vertical line. Initially, these signals were monopolarly sampled, but eight vertically oriented bipolar channels covering the whole gluteal region from ventral to dorsal (P1 to P8) were subsequently calculated by subtracting the signals of the corresponding electrodes of each electrode strip for both sides of the body. Three vertical bipolar channels represented the tensor fasciae latae (TFL; P2), gluteus medius (Gmed, SENIAM position; average of P4 and P5), and gluteus maximus muscles (Gmax; P7). To determine the interval between SEMG and corresponding GRF, the time delay (TD) between the respective first amplitude peaks (F1) in SEMG and vertical GRF curves was calculated. Results Throughout the grand averaged SEMG curves, the absolute amplitudes significantly differed among the three walking speeds at all electrode positions, with the amplitude of the F1 peak significantly increasing with increasing speed. In addition, when normalized to slow, the relative SEMG amplitude differences at the individual electrode positions showed an impressively homogeneous pattern. In both vertical GRF and all electrode SEMGs, the F1 peak occurred significantly earlier with increasing speed. Also, the TD between SEMG and vertical GRF F1 peaks significantly decreased with increasing speed. Concerning spatial activation, the TD between the respective F1 peaks in the SEMG and vertical GRF was significantly shorter for the ventral TFL position than the dorsal Gmed and Gmax positions, showing that the SEMG F1 peak during this initial phase of the gait cycle occurred earlier in the dorsal positions, and thus implying that the occurrence of the SEMG F1 peak proceeded from dorsal to ventral. Significance Tightly regulated spatial and temporal activation of the gluteal intermuscular functional unit, which includes both speed- and position-dependent mechanisms, seems to be an essential requirement for a functionally optimized, steady gait.
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Dissertations / Theses on the topic "Analyse des signaux HD-sEMG"

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Imrani, Sallak Loubna. "Evaluation of muscle aging using high density surface electromyography." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2647.

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Avec le vieillissement de la population, préserver la fonction musculaire est important pour éviter la perte de mobilité et d'autonomie. De nos jours, la prévention de la maladie musculaire, la sarcopénie, est une préoccupation majeure et des facteurs de risque importants tels que l'âge avancé ainsi que des facteurs modifiables, notamment une faible activité physique et une alimentation déséquilibrée ont été identifiés. Compte tenu de la croissance des populations plus âgées et de la diminution de l'activité physique, qui touche également les jeunes citoyens, la sensibilisation à la qualité musculaire peut être cruciale pour promouvoir un vieillissement en bonne santé dans nos sociétés. Les besoins en évaluations fonctionnelles musculaires ont été exprimés par les chercheurs et les cliniciens. Le groupe de travail européen sur la sarcopénie chez les personnes âgées (EWGSOP) recommande de définir la sarcopénie comme la présence à la fois d'une faible masse musculaire et d'une faible fonction musculaire (force et performance physique). Pour cela, nous avons développé une méthode d’évaluation du vieillissement musculaire, en utilisant une technologie ambulatoire et non invasive, appelée technologie d'électromyographie de surface haute densité (HD-sEMG), à travers un projet de recherche clinique sur cinq catégories d'âge (25 à 74 ans), actifs et sédentaires. Nous avons réalisé une étude comparative avec une analyse complète et multimodale du rectus femoris (RF), muscle impliqué dans les mouvements de la vie quotidienne, pour dévoiler le potentiel prometteur de la technique HD sEMG, par rapport aux techniques cliniques classiques, l’objectif étant de détecter les changements précoces de la qualité de la fonction musculaire impactée par le vieillissement et le niveau d'activité physique. La partie clinique de ce projet de thèse a été financée par une subvention européenne, EIT Health. En analysant principalement la dynamique de contraction musculaire et l'intensité du rectus femoris, nos résultats ont montré que la technique HD-sEMG, était capable de discriminer entre les cinq catégories d'âge de sujets sains physiquement actifs. Plus intéressant encore, les scores HD-sEMG proposés discriminaient entre les participants actifs et sédentaires, de la même catégorie d'âge (45-54 ans), contrairement aux paramètres cliniques et aux autres techniques couramment utilisées (absortiométrie biphotonique par rayons X, DXA et échographie). De plus, ces scores pour les participants sédentaires de cette catégorie d'âge étaient significativement plus proches de ceux des participants actifs des catégories d'âge supérieures (55-64 ans et 65-74 ans). Cela suggère fortement qu'un mode de vie sédentaire semble accélérer le processus de vieillissement musculaire au niveau anatomique et fonctionnel, et ce processus accéléré subtil peut être détecté par la technique HD-sEMG. Ces résultats préliminaires prometteurs pourraient contribuer au développement d’un outil intéressant aux cliniciens pour améliorer à la fois la précision et la sensibilité de l'évaluation musculaire utile pour les programmes de prévention et de réadaptation afin d'éviter ou de retarder la sarcopénie, problème de santé publique actuel alerté par l'Organisation Mondiale de la Santé (OMS) et promouvoir un vieillissement en bonne santé
With the aging of the population, preserving muscle function is important to prevent loss of mobility and autonomy. Nowadays, the prevention of the muscle disease, sarcopenia, is a major concern and important risk factors such as older age as well as modifiable factors including low physical activity and unhealthy diet have been identified. Considering the growth of older populations and the decreased physical activity, which also includes young citizens, muscle quality awareness can be crucial in promoting a healthy aging process in our societies. Muscle functional assessments needs were expressed by researchers and clinicians, The European Working Group on Sarcopenia in Older People (EWGSOP) recommends defining sarcopenia as the presence of both low muscle mass and low muscle function (strength, and physical performance). For this purpose, we have developed a method for muscle aging evaluation, using an ambulatory and non-invasive technology, called high-density surface electromyography (HDsEMG), through a clinical research project on five age categories (25 to 74 yrs.). We performed a comparative study with a complete and multimodal analysis of the rectus femoris, muscle involved in daily life motions, in order to reveal the promising potential of the HD-sEMG technique, compared to conventional clinical techniques, to detect early changes in the quality of muscle function impacted by aging and physical activity level. The clinical part of this thesis project was funded by a European grant, EITH Health. By analyzing both muscle contraction dynamics and intensity of the rectus femoris, our results showed that the HD-sEMG technique, was able to discriminate between the five age categories of healthy physically active subjects. More interestingly, the proposed HD-sEMG scores discriminated between active and sedentary participants, from the same age category(45-54 yrs.), in contrary to clinical parameters and others usual techniques (dual-energy x-ray absorptiometry, DXA and ultrasonography). In addition, these scores for sedentary participants from this age category were significantly closer to those of active participants from higher age categories (55-64 yrs. and 65-74 yrs.). This strongly suggests that sedentary lifestyle seems to accelerate the muscle aging process at both anatomical and functional level, and this subtle accelerated process can be detected by the HD-sEMG technique. These promising preliminary results can contribute to the development of an interesting tool for clinicians to improve both accuracy and sensitivity of functional muscle evaluation useful for prevention and rehabilitation to avoid the effects of unhealthy lifestyle that can potentially lead to sarcopenia. This can support also the actual public health concern alerted by Word Health Organization (WHO) regarding aging and sarcopenia, to promote healthy aging
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Lienhard, Karin. "Effet de l'exercice physique par vibration du corps entier sur l'activité musculaire des membres inférieurs : approche méthodologique et applications pratiques." Thesis, Nice, 2014. http://www.theses.fr/2014NICE4080/document.

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L’objectif de cette thèse a été d’analyser l’effet de l’exercice physique réalisé sur plateforme vibrante (whole-body vibration, WBV) sur l’activité musculaire des membres inférieurs, de développer des outils d’analyse méthodologiques et de proposer des recommandations pratiques d’utilisation. Deux études méthodologiques ont été menées pour identifier la méthode optimale permettant de traiter les signaux d'électromyographie de surface (sEMG) recueillis pendant la vibration et d'analyser l'influence de la méthode de normalisation de l'activité sEMG. Une troisième étude visait à mieux comprendre si les pics sEMG observés dans le spectre de puissance du signal contiennent des artéfacts de mouvement et/ou de l'activité musculaire réflexe. Les trois études suivantes avaient pour but de quantifier l’effet de la WBV sur l’activité musculaire en fonction de différents paramètres tels que, la fréquence de vibration, l'amplitude de la plateforme, une charge supplémentaire, le type de plateforme, l'angle articulaire du genou, et la condition physique du sujet. En outre, l'objectif a été de déterminer l'accélération verticale minimale permettant de stimuler au mieux l'activité musculaire des membres inférieurs. En résumé, les recherches menées au cours de cette thèse fournissent des solutions pour de futures études sur : i) comment supprimer les pics dans le spectre du signal sEMG et, ii) comment normaliser l'activité musculaire pendant un exercice WBV. Enfin, les résultats de cette thèse apportent à la littérature scientifique de nouvelles recommandations pratiques liées à l’utilisation des plateformes vibrantes à des fins d’exercice physique
The aim of this thesis was to analyze the effect of whole-body vibration (WBV) exercise on lower limb muscle activity and to give methodological implications and practical applications. Two methodological studies were conducted that served to evaluate the optimal method to process the surface electromyography (sEMG) signals during WBV exercise and to analyze the influence of the normalization method on the sEMG activity. A third study aimed to gain insight whether the isolated spikes in the sEMG spectrum contain motion artifacts and/or reflex activity. The subsequent three investigations aimed to explore how the muscle activity is affected by WBV exercise, with a particular focus on the vibration frequency, platform amplitude, additional loading, platform type, knee flexion angle, and the fitness status of the WBV user. The final goal was to evaluate the minimal required vertical acceleration to stimulate the muscle activity of the lower limbs. In summary, the research conducted for this thesis provides implication for future investigations on how to delete the excessive spikes in the sEMG spectrum and how to normalize the sEMG during WBV. The outcomes of this thesis add to the current literature in providing practical applications for exercising on a WBV platform
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Al, Harrach Mariam. "Modeling of the sEMG / Force relationship by data analysis of high resolution sensor network." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2298/document.

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Les systèmes neuromusculaires et musculo-squelettique sont considérés comme un système de systèmes complexe. En effet, le mouvement du corps humain est contrôlé par le système nerveux central par l'activation des cellules musculaires squelettiques. L'activation du muscle produit deux phénomènes différents : mécanique et électrique. Ces deux activités possèdent des propriétés différentes, mais l'activité mécanique ne peut avoir lieu sans l'activité électrique et réciproquement. L'activité mécanique de la contraction du muscle squelettique est responsable du mouvement. Le mouvement étant primordial pour la vie humaine, il est crucial de comprendre son fonctionnement et sa génération qui pourront aider à détecter des déficiences dans les systèmes neuromusculaire et musculo-squelettique. Ce mouvement est décrit par les forces musculaires et les moments agissant sur une articulation particulière. En conséquence, les systèmes neuromusculaires et musculo-squelettique peuvent être évalués avec le diagnostic et le management des maladies neurologiques et orthopédiques à travers l'estimation de la force. Néanmoins, la force produite par un seul muscle ne peut être mesurée que par une technique très invasive. C'est pour cela, que l'estimation de cette force reste l'un des grands challenges de la biomécanique. De plus, comme dit précédemment, l'activation musculaire possède aussi une réponse électrique qui est corrélée à la réponse mécanique. Cette résultante électrique est appelée l'électromyogramme (EMG) et peut être mesurée d'une façon non invasive à l'aide d'électrodes de surface. L'EMG est la somme des trains de potentiel d'action d'unité motrice qui sont responsable de la contraction musculaire et de la génération du mouvement. Ce signal électrique peut être mesuré par des électrodes à la surface de la peau et est appelé I'EMG de surface {sEMG). Pour un muscle unique, en supposant que la relation entre l'amplitude du sEMG et la force est monotone, plusieurs études ont essayé d'estimer cette force en développant des modèles actionnés par ce signal. Toutefois, ces modèles contiennent plusieurs limites à cause des hypothèses irréalistes par rapport à l'activation neurale. Dans cette thèse, nous proposons un nouveau modèle de relation sEMG/force en intégrant ce qu'on appelle le sEMG haute définition (HD-sEMG), qui est une nouvelle technique d'enregistrement des signaux sEMG ayant démontré une meilleure estimation de la force en surmontant le problème de la position de l'électrode sur le muscle. Ce modèle de relation sEMG/force sera développé dans un contexte sans fatigue pour des contractions isométriques, isotoniques et anisotoniques du Biceps Brachii (BB) lors une flexion isométrique de l'articulation du coude à 90°
The neuromuscular and musculoskeletal systems are complex System of Systems (SoS) that perfectly interact to provide motion. This interaction is illustrated by the muscular force, generated by muscle activation driven by the Central Nervous System (CNS) which pilots joint motion. The knowledge of the force level is highly important in biomechanical and clinical applications. However, the recording of the force produced by a unique muscle is impossible using noninvasive procedures. Therefore, it is necessary to develop a way to estimate it. The muscle activation also generates another electric phenomenon, measured at the skin using electrodes, namely the surface electromyogram (sEMG). ln the biomechanics literature, several models of the sEMG/force relationship are provided. They are principally used to command musculoskeletal models. However, these models suffer from several important limitations such lacks of physiological realism, personalization, and representability when using single sEMG channel input. ln this work, we propose to construct a model of the sEMG/force relationship for the Biceps Brachii (BB) based on the data analysis of a High Density sEMG (HD-sEMG) sensor network. For this purpose, we first have to prepare the data for the processing stage by denoising the sEMG signals and removing the parasite signals. Therefore, we propose a HD-sEMG denoising procedure based on Canonical Correlation Analysis (CCA) that removes two types of noise that degrade the sEMG signals and a source separation method that combines CCA and image segmentation in order to separate the electrical activities of the BB and the Brachialis (BR). Second, we have to extract the information from an 8 X 8 HD-sEMG electrode grid in order to form the input of the sEMG/force model Thusly, we investigated different parameters that describe muscle activation and can affect the relationship shape then we applied data fusion through an image segmentation algorithm. Finally, we proposed a new HDsEMG/force relationship, using simulated data from a realistic HD-sEMG generation model of the BB and a Twitch based model to estimate a specific force profile corresponding to a specific sEMG sensor network and muscle configuration. Then, we tested this new relationship in force estimation using both machine learning and analytical approaches. This study is motivated by the impossibility of obtaining the intrinsic force from one muscle in experimentation
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Mishra, Ram Kinker. "Muscle Fatigue Analysis During Dyanamic Conraction." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2556.

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In the field of ergonomics, biomechanics, sports and rehabilitation muscle fatigue is regarded as an important aspect since muscle fatigue is considered to be one of the main reasons for musculoskeletal disorders. Classical signal processing techniques used to understand muscle behavior are mainly based on spectral based parameters estimation, and mostly applied during static contraction and the signal must be stationary within the analysis window; otherwise, the resulting spectrum will make little physical sense. Furthermore, the shape and size of the analysis window also directly affect the spectral estimation. But fatigue analysis in dynamic conditions is of utmost requirement because of its daily life applicability. It is really difficult to consistently find the muscle fatigue during dynamic contraction due to the inherent non-stationary nature and associated noise in the signal along with complex physiological changes in muscles. Nowadays, in addition to linear signal processing, different non-linear signal processing techniques are adopted to find out the consistent and robust indicator for muscle fatigue under dynamic condition considering the high degree of non-linearity (caused by functional interference between different muscles, changes of signal sources and paths to recording electrodes, variable electrode interface etc.) in the signal. In this work, various linear and nonlinear-non-stationary signal processing methods, applied on surface EMG signal for muscular fatigue analysis under dynamic contraction are studied. In present study, surface EMG (sEMG) signals are recorded from Biceps Brachii muscles from eight (N=8) physically active college students during dynamic lifting 7 kg load at the rate of 20 lifts/min till they become fatigue. EMG data is processed in two ways -1. taking the whole EMG response and 2. breaking into three ranges of contraction (0-45)o, (45-90)o and >90o, to study better response region. It is observed that in spectral estimation techniques auto-regressive (AR) based spectral estimation technique gives better frequency resolution than periodogram for small epochs, as AR is based on parametric estimation. Both the previous methods provide only the frequency information in the signal. In order to estimate the time varying nature of frequency content in a signal various time-frequency signal processing techniques are used like – Short Time-Fourier Transform (STFT), Smoothed pseudo Wigner-Ville (SPWD), Choi-William distribution (CWD), Continuous Wavelet Transform (CWT), Huang-Hilbert Transform (HHT) and Recurrence Quantification Analysis (RQA) are used. The last two techniques are used by considering the EMG signal as non-linear and non-stationary signals. Among these techniques, STFT is the simplest time-frequency analysis technique. But tradeoff between time and frequency resolution is the major constraint in STFT, therefore, a window length of 256 samples are considered in this study. In order to tackle time-frequency resolution problem different Cohen-class distribution techniques are used like SPWD and CWD, where the result is severely affected by the presence of interference terms which make its interpretation really difficult. Different adaptive filters are used in SPWD and CWD to suppress these interference terms during analysis. Among these time-frequency analysis techniques continuous wavelet transform provides the most accurate results in comparison to other time-frequency analysis techniques. Similar result is obtained in present study. This fatigue response is further improved using non-linear and non-stationary techniques like HHT and RQA. HHT shows less variation in frequency response than CWT analysis result. Percentage of determinism calculated using recurrence quantification analysis method is found to be more sensitive than mean frequency estimation. Therefore, non-linear and non-stationary signal processing techniques are to be better indicator of muscle fatigue during dynamic contraction.
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Book chapters on the topic "Analyse des signaux HD-sEMG"

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Varghese, Aiswarya, K. B. Akshaya, S. Akshay Prakash, S. Sreehari, Divya Sasidharan, and G. Venugopal. "Analysis of Motorcycle Rider’s Posture Using sEMG Signals." In Lecture Notes in Electrical Engineering, 471–81. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0336-5_39.

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Strazza, Annachiara, Federica Verdini, Alessandro Mengarelli, Stefano Cardarelli, Andrea Tigrini, Sandro Fioretti, and Francesco Di Nardo. "Wavelet Analysis-Based Reconstruction for sEMG Signal Denoising." In IFMBE Proceedings, 245–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31635-8_29.

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Banerjee, Swati, Loubna Imrani, Kiyoka Kinugawa, Jeremy Laforet, and Sofiane Boudaoud. "Analysis of HD-sEMG Signals Using Channel Clustering Based on Time Domain Features For Functional Assessment with Ageing." In Biomedical Engineering and Computational Intelligence, 83–92. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21726-6_8.

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Shamli Fathima, P., C. Sandhra, Dolbin Jojo, A. V. Gayathri, N. Sidharth, and G. Venugopal. "Fatigue Analysis of Biceps Brachii Muscle Using sEMG Signal." In Lecture Notes in Electrical Engineering, 307–14. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0336-5_25.

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Guerrero, F. N., P. A. García, and E. M. Spinelli. "Signal modes for design-oriented analysis of active sEMG spatial filter electrodes." In VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016, 504–7. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4086-3_127.

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Chang, Xin, Xinyi Li, Jian Li, Guihua Tian, Hongcai Shang, Jingbo Hu, Jiahao Shi, and Yue Lin. "Muscle Tension Analysis Based on sEMG Signal with Wearable Pulse Diagnosis Device." In Intelligent Robotics and Applications, 756–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89092-6_69.

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Quizhpe-Cárdenas, Carlos, Francisco Ortiz-Ortiz, Freddy Bueno-Palomeque, and Marco Vinicio Vásquez Cabrera. "Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals." In Advances in Physical Ergonomics & Human Factors, 94–101. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94484-5_10.

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Chen, Chao, Farong Gao, Chunling Sun, and Qiuxuan Wu. "Muscle Synergy Analysis for Stand-Squat and Squat-Stand Tasks with sEMG Signals." In Biometric Recognition, 545–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_58.

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Jing, Liuwen, Tie Liu, Haoming Shi, Yinming Shi, Shiyu Yao, Junyou Yang, Xia Yang, and Dianchun Bai. "Analysis of Continuous Motion Angle for Lower Limb Exoskeleton Robot Based on sEMG Signal." In Communications in Computer and Information Science, 50–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4929-2_4.

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Conforto, S. "The role of the sEMG signal processing in the field of the Human Movement Analysis." In IFMBE Proceedings, 523–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03889-1_140.

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Conference papers on the topic "Analyse des signaux HD-sEMG"

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Sebastian, Anish, Parmod Kumar, Marco P. Schoen, Alex Urfer, Jim Creelman, and D. Subbaram Naidu. "Analysis of EMG-Force Relation Using System Identification and Hammerstein-Wiener Models." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4185.

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Surface Electromyographic (sEMG) signals have been exploited for almost a century, for various clinical and engineering applications. One of the most compelling and altruistic applications being, control of prosthetic devices. The study conducted here looks at the modeling of the force and sEMG signals, using nonlinear Hammerstein-Weiner System Identification techniques. This study involved modeling of sEMG and corresponding force data to establish a relation which can mimic the actual force characteristics for a few particular hand motions. Analysis of the sEMG signals, obtained from specific Motor Unit locations corresponding to the index, middle and ring finger, and the force data led to the following deductions; a) Each motor unit location has to be treated as a separate system, (i.e. extrapolation of models for different fingers cannot be done) b) Fatigue influences the Hammerstein-Wiener model parameters and any control algorithm for implementing the force regimen will have to be adaptive in nature to compensate for the changes in the sEMG signal and c) The results also manifest the importance of the design of the experiments that need to be adopted to comprehensively model sEMG and force.
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Marri, 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.

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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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Kushwah, Kavita, Rakesh Narvey, and Ashish Singhal. "Head Posture Analysis using sEMG Signal." In 2018 International Conference on Advanced Computation and Telecommunication (ICACAT). IEEE, 2018. http://dx.doi.org/10.1109/icacat.2018.8933731.

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Bonilla, V. F., A. V. Litvin, M. H. Moya, and G. K. Moskera. "THE INTELLIGENT ROBOT MITSUBISHI RV-2JA CONTROL SYSTEM." In INNOVATIVE TECHNOLOGIES IN SCIENCE AND EDUCATION. DSTU-Print, 2020. http://dx.doi.org/10.23947/itno.2020.42-46.

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The Mitsubishi RV-2JA intelligent robot control system (ISMS) was synthesized using surface electromyographic signals (pEMG). The sEMG signals were recording using the Myo bracelet and transmitted to the intelligent control system via the Bluetooth interface. The IRCS was synthesized in Simulink environment of the Matlab platform. IRCS performs the processing and analysis of sEMIG signals to identify, verify and control the robot using an artificial neural networks.
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Tatt Cheah, Yeok, Ka Wing Frances Wan, and Joanne Yip. "Prediction of Muscle Fatigue During Dynamic Exercises based on Surface Electromyography Signals Using Gaussian Classifier." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002597.

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Muscle fatigue is shown to be associated with incidence of musculoskeletal injuries found with sports training and competition. The real-time detection of fatigue onset allows preventative measures to be taken in time to minimize injuries. In this paper, we aim to provide a framework that classifies muscle fatigue based on surface electromyography (sEMG) features extracted during dynamic exercises. This includes the use of data segmentation, real-time-compatible data normalization, a principal component analysis (PCA) based feature reduction and Gaussian classifier methods.An experiment has been carried out to acquire the sEMG signals of the upper two pairs of rectus abdominis muscles of four healthy adult volunteers during weighted decline and bench-assisted sit-ups. The collected sEMG signals are then segmented into concentric and eccentric segments by using the inertial measurement unit (IMU) data. Eight commonly used sEMG features are extracted from each segment. We fit two Gaussian models (GMs) on the distribution of fatigued and non-fatigued data samples and show that the GM can utilize this information to predict the number of repetitions possible before task failure. We fit another set of GM on a reduced feature space by projecting the data onto principal component axes obtained through singular value decomposition (SVD). By projecting the features onto the first two principal axes, we achieve similar accuracy and f1-scores compared to the GM by using 6 handpicked features. This reduction in the feature space greatly reduces the training samples necessary for such class-imbalanced datasets. This classifier can also be directly used in the real-time detection of muscle fatigue during dynamic movements, which can be adopted in applications in sports, workplaces, and rehabilitation sciences. These frequency-time characteristics also provide insight into the function of low-level feature extractors when developing deep learning models to identify muscle fatigue.
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Viscuso, Stefano, and Simone Pittaccio. "An EMG-Controlled Device Managing Transition From Passive to Active Exercise in the Acute Rehabilitation of the Ankle Joint." In ASME 2012 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/sbc2012-80237.

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The capacity of flexing one’s ankle is an indispensible segment of gait re-learning, as imbalance, wrong compensatory use of other joints and risk of falling may depend on the so-called drop-foot. The rehabilitation of ankle dorsiflexion may be achieved through active exercising of the relevant musculature (especially tibialis anterior, TA). This can be troublesome for patients affected by weakness and flaccid paresis. Thus, as needs evolve during patient’s improvements, a therapeutic device should be able to guide and sustain gradual recovery by providing commensurate aid. This includes exploiting even initial attempts at voluntary motion and turn those into effective workout. This paper presents an active orthosis powered by two rotary actuators containing shape memory alloy (SMA) wire that promote passive ankle dorsiflexion. A computer routine that analyses the electromyographic (sEMG) signal from TA muscle is used to control the orthosis and trigger its activation when appropriate sEMG signal is recorded.
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El-Daydamony, Eman M., Mona El-Gayar, and Fatma Abou-Chadi. "A computerized system for SEMG signals analysis and classifieation." In 2008 National Radio Science conference (NRSC). IEEE, 2008. http://dx.doi.org/10.1109/nrsc.2008.4542388.

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Contreras-Ortiz, Sonia H., and Luis A. Flórez-Prias. "Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection." In 13th International Symposium on Medical Information Processing and Analysis, edited by Jorge Brieva, Juan David García, Natasha Lepore, and Eduardo Romero. SPIE, 2017. http://dx.doi.org/10.1117/12.2285950.

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Chakraborty, Monisha, and Debanjan Parbat. "Fractal analysis of sEMG signal under varying load conditions." In 2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC). IEEE, 2016. http://dx.doi.org/10.1109/ciec.2016.7513833.

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O. Coelho, Fabrício, Guilherme R. Moreira, Milena F. Pinto, and André M. Marcato. "sEMG Signals Classification using CNN Features Extraction as a Reliable Method." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1306.

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sEMG (Surface electromyography) signals are essential in several applications, such as in prosthetic control. These signals are collected and analyzed to produce the expected actions through corresponding pattern recognition. In this sense, feature extraction plays a critical role in achieving good accuracy during the classification process. In recent years, with the interesting results obtained through convolutional filters and supervised learning, it is possible to extract properties that best distinguish and classify images. Therefore, this research work uses a CNN network to extract these features that will be later applied in conventional classifiers. The obtained results allowed to verify that the proposed methodology guarantees better results when compared to the works that use traditional characteristics for the classification process.
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