Comparative evaluation of EMG signal features for myoelectric controlled human arm prosthetics

Karabulut, Derya; Örteş, Faruk; Arslan, Yunus Ziya; Adlı, Mustafa

doi:10.1016/j.bbe.2017.03.001

Cited by 50 publications

(14 citation statements)

References 29 publications

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“…Using high segment lengths requires high computational load, while low segment lengths cause biases in the feature extraction. Whilst real-time activities are processed, segments greater than 200 milliseconds require some overlap [ 42 ]. Some of the most important features of time domain are Integrated EMG (IEMG), Mean Absolute Value (MAV), Root Median Square (RMS), Simple Square Integral (SSI), or Waveform Length (WL) [ 39 ].…”

Section: Signal Analysismentioning

confidence: 99%

EMG Characterization and Processing in Production Engineering

Olmo

Domingo

2020

Materials

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Electromyography (EMG) signals are biomedical signals that measure electrical currents generated during muscle contraction. These signals are strongly influenced by physiological and anatomical characteristics of the muscles and represent the neuromuscular activities of the human body. The evolution of EMG analysis and acquisition techniques makes this technology more reliable for production engineering applications, overcoming some of its inherent issues. Taking as an example, the fatigue monitoring of workers as well as enriched human–machine interaction (HMI) systems used in collaborative tasks are now possible with this technology. The main objective of this research is to evaluate the current implementation of EMG technology within production engineering, its weaknesses, opportunities, and synergies with other technologies, with the aim of developing more natural and efficient HMI systems that could improve the safety and productivity within production environments.

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Section: Signal Analysismentioning

confidence: 99%

EMG Characterization and Processing in Production Engineering

Olmo

Domingo

2020

Materials

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“…The sEMG are bioelectrical signals produced by muscle activity that contains information about various muscle activity [1][2][3] . The placement of electrodes on the muscle to extract signals from the user's muscle activity can be used to control the robot or prosthesis and can be used to assist muscle weakness and hemiplegia to control the exoskeleton for rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

“…The methods of features extraction are time domain, frequency domain and time-frequency domain. Derya et al [3] used Root Mean Square (RMS) to obtain features from the sEMG. Oluwarotimi et al [14] made use of a given Analysis Window and its Mean (ASM) to get features with accuracy of 92%.…”

Section: Introductionmentioning

confidence: 99%

A Self-Selected Movement Classification Method for Forearm Via sEMG and Attitude Sensor

Zhang¹

2020

Preprint

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Abstract Objective: The use of surface electromyography (sEMG) to realize the recognition of the movement intention can realize the control of the artificial hand or the robot, and can help the rehabilitation training for hemiplegia or muscle weakness. However, the sEMG are weak and susceptible to external interference, so the current research focuses on identifying certain types of movements. But once the subjects are changed, the recognition accuracy will greatly reduce. This study proposes a classification method which the subject could choose optional movements of forearm.Methods: Two sEMG sensors were used, and a 9-axis attitude sensor was added to the wrist. 8 different subjects participated in the experiment, and everyone selected 5 movements. The sEMG sensors were attached to the extensor pollicis brevis and the extensor digitorum. The sEMG features were: Standard Deviation (SD), Power Spectrum Density (PSD); attitude sensor features were: angle and angular acceleration in three dimensional space, and integral of angular acceleration. The results were classified and identified using Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Tree (DT) and Ensembles (En) algorithms. The results of using the sEMG, using the attitude sensor signals and combining the two were compared. Analysis of variance was conducted on the average accuracy. Features were reduced the dimension by the Principal Component Analysis (PCA), and the results of using PCA and not were compared. Results: The results showed that the combination of the two types of sensors could improve the recognition effect compared to the using sEMG sensor or the attitude sensor alone. The final recognition result was that KNN performed best, reaching 95.0%. The results of using PCA were more stable.Conclusion: The method could be used between different subjects, and the user could select the movements autonomously.Significance: This method can improve the adaptability of movement intention recognition based on sEMG, and has important significance for popularizing the use of the sEMG to control the manipulator or the prosthetic and the rehabilitation training.

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“…Electromyography (EMG) signals are a bioelectrical signal generated by muscle activity that contains information of various muscular activities . The purpose of controlling the prosthesis can be achieved by extracting signals from the patient's muscle activities . EMG signals include needle EMG signals and surface EMG signals, which are obtained by passing through a needle electrode and a surface electrode, respectively.…”

Section: Introductionmentioning

confidence: 99%

Real‐time and user‐independent feature classification of forearm using EMG signals

et al. 2018

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Electromyography (EMG) signals contain various information about human motion. How to extract the EMG signals of the human body by appropriate methods for classification is a hot issue in current research. Unfortunately, the main problem with the classification of EMG signals is that only certain actions can be identified. Once the individual is changed, the recognition accuracy rate will be greatly reduced. This study introduces a method for classifying the forearm using back propagation (BP) neural networks. This mode extracted five features of the EMG signals. Participants were required to train their own actions during the test. Six participants selected four to six actions to identify them, and the average accuracy was more than 90%. The results suggest that the method can be used among different individuals and provides a good classification method.

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Comparative evaluation of EMG signal features for myoelectric controlled human arm prosthetics

Cited by 50 publications

References 29 publications

EMG Characterization and Processing in Production Engineering

EMG Characterization and Processing in Production Engineering

A Self-Selected Movement Classification Method for Forearm Via sEMG and Attitude Sensor

Real‐time and user‐independent feature classification of forearm using EMG signals

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