Artificial Human Arm Driven by EMG Signal

Al-Faiz, Mohammed Z.; Miry, Abbas H.

doi:10.5772/48531

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Cene provides an open-source dataset of the upper limb for an Extreme Learning Machine (ELM) limit learning machine for intention detection [50]. Mohammed provided a method for simulating and generating EMG data from healthy individuals [114].…”

Section: Datasetsmentioning

confidence: 99%

Review of sEMG for Robot Control: Techniques and Applications

Song,

Yan,

Guo

et al. 2023

Applied Sciences

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Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks.

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Section: Datasetsmentioning

confidence: 99%

Review of sEMG for Robot Control: Techniques and Applications

Song,

Yan,

Guo

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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Pattern recognition of EMG signal during load lifting using Artificial Neural Network (ANN)

Jali

Izzuddin

Bohari

et al. 2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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This paper describes pattern recognition of electromyography (EMG) signal during load lifting using Artificial Neural Network (ANN). EMG is a method to measure and record the muscle activity when individuals perform certain operation and actions. This research will classify the EMG signal based on force apply to the arm due to the gravity act on it during load lifting. Recognizing pattern based on EMG signal is not an easy task because of the nonlinearities behavior of the signal. It required a good classifier to distinguish each pattern. The motivation of this project is to help the person suffer with hemiparesis to perform daily activities as well as to improve the lifestyle. It is important for patients to realize the hopes of hemiparesis after experiencing their inability to do activity as a normal human. Recognizing EMG pattern is crucially important for rehabilitation control that enables the patients to lift the heavy load despite of their muscle weaknesses. Therefore, a proper analysis of muscle behavior is necessary. The objectives of this research are to extract the important features of EMG signal using time domain analysis and to classify EMG signal based on load lifting using ANN. The experiment was performed to five subjects that were selected mainly based on criteria specified. The EMG signals are acquired at long head biceps brachii. Then, the subjects were asked to lift the loads of 2kg, 5kg, and 7kg. It is expected an accurate classifier which can recognize the pattern precisely and could be further used for arm rehabilitation control.

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Artificial Human Arm Driven by EMG Signal

Cited by 2 publications

References 9 publications

Review of sEMG for Robot Control: Techniques and Applications

Review of sEMG for Robot Control: Techniques and Applications

Pattern recognition of EMG signal during load lifting using Artificial Neural Network (ANN)

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