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Analysis of the effectiveness of machine learning methods in the problem of gesture recognition based on the data of electromyographic signals

机译：基于电拍摄信号数据的手势识别问题的机器学习方法的有效性分析

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Polina Sergeevna Kozyr; Anton Igorevich Saveliev;
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1. Classification of electromyographic hand gesture signals using machine learning techniques [J] . Jia Guangyu, Lam Hak-Keung, Liao Junkai, Neurocomputing . 2020,第Auga11期

机译：使用机器学习技术进行电拍摄手势信号的分类
2. Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach [J] . Jia Guangyu, Lam Hak-Keung, Ma Shichao, IEEE transactions on neural systems and rehabilitation engineering . 2020,第6期

机译：使用改进的模糊C-MERIAL聚类和两步机学习方法的电拍摄手势信号分类
3. Hand Body Language Gesture Recognition Based on Signals From Specialized Glove and Machine Learning Algorithms [J] . Paweł Pławiak, Tomasz Sośnicki, Michał Niedźwiecki, IEEE transactions on industrial informatics . 2016,第3期

机译：基于专业手套信号和机器学习算法的手势语言手势识别
4. Real-Time Electromyographic Hand Gesture Signal Classification Using Machine Learning Algorithm Based on Bispectrum Feature [C] . Deepa Nair, R. Rajapriya, K. Rajeswari International Conference on Communication, Circuits, and Systems . 2020

机译：基于BISPectrum特征的机器学习算法实时电拍摄手势信号分类
5. Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction [D] . Trigueiros, Paulo José de Albuquerque Cardoso. 2013

机译：基于计算机视觉和机器学习的手势识别系统：对人机交互的应用
6. RoCoLe: A robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases recognition [O] . Jorge Parraga-Alava, Kevin Cusme, Angélica Loor, 2019

机译：RoCoLe：罗布斯塔咖啡叶图像数据集用于评估基于机器学习的植物病害识别方法
7. Gestures Recognition Method Based on Electromyographic Signal [O] . Yucheng Tian, Mo Wang, Xing Zhang, 2015

机译：手势基于电拍摄信号的识别方法
8. Graphical Geometric and Learning/Optimization-Based Methods in Statistical Signal and Image Processing Object Recognition and Data Fusion [R] . Willsky, A. S. 2008

机译：基于图形几何和学习/优化的统计信号和图像处理方法识别和数据融合方法

Analysis of the effectiveness of machine learning methods in the problem of gesture recognition based on the data of electromyographic signals

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