Support Vector Machine-Based EMG Signal Classification Techniques: A Review
Diana C. Toledo-Pérez,Juvenal Rodríguez-Reséndiz,Roberto A. Gómez-Loenzo,Juan Carlos Jáuregui-Correa +3 more
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TLDR
This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM) and summarizes the techniques used to make the classification in each reference.Abstract:
This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.read more
Citations
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Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review
TL;DR: This systematic literature review analyses the state-of-the-art of real-time hand gesture recognition models using EMG data and machine learning and identified trends and gaps that could open new directions of work for future research in the area of gesture recognition using EMg.
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A Home-Based Bilateral Rehabilitation System With sEMG-based Real-Time Variable Stiffness
TL;DR: A novel bilateral rehabilitation system that implements a surface electromyography-based stiffness control to achieve real-time stiffness adjustment based on the user's dynamic motion and shows fast adaption to the patient's dynamic movement as well as improving the comfort in robot-assisted bilateral training.
Journal ArticleDOI
Classification of Hand Movements Using MYO Armband on an Embedded Platform
Haider Ali Javaid,Mohsin I. Tiwana,Ahmed Alsanad,Javaid Iqbal,Muhammad Tanveer Riaz,Saeed Ahmad,Faisal Abdulaziz Almisned +6 more
TL;DR: An embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis and inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future.
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sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm
TL;DR: In this paper , a novel method using an improved detection algorithm was proposed to recognize limb joint motion and detect joint angle based on sEMG images, aiming to obtain a high-security and fast-processing action recognition strategy.
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Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication
TL;DR: The possibility of implementing a wearable authentication system that can be worn on the forearm to detect EMG signals and apply them for personal authentication is confirmed by measuring the EMG signal and artificial intelligence analysis algorithm presented in this study.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
A new strategy for multifunction myoelectric control
TL;DR: A novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns is described, which increases the number of functions which can be controlled by a single channel of myOElectric signal but does so in a way which does not increase the effort required by the amputee.
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A robust, real-time control scheme for multifunction myoelectric control
Kevin Englehart,B. Hudgins +1 more
TL;DR: It is shown that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible and other important characteristics for prosthetic control systems are met.
Journal ArticleDOI
Feature reduction and selection for EMG signal classification
TL;DR: In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed and it is indicated that most time domain features are superfluity and redundancy.
Journal ArticleDOI
Myoelectric control systems—A survey
TL;DR: This paper reviews recent research and development in pattern recognition- and non-pattern recognition-based myoelectric control, and presents state-of-the-art achievements in terms of their type, structure, and potential application.