Development of a software module for feature extraction and classification of EMG signals

Garg, C. K.; Narayan, Yogendra; Mathew, Lini

doi:10.1109/ccintels.2015.7437917

Cited by 13 publications

(4 citation statements)

References 6 publications

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“…The FL output might be the wrist's assistive torque or desired angular velocity because EMG signals directly connect to muscle activation as higher muscle activation levels produce more force. Since the admittance controller, one of the most widely used control theories for robotic rehabilitation systems, accepts force as an input and allows velocity as an output, joint angle and angular velocity were chosen as FL outputs in most studies [36].…”

Section: Recent Development Of Emg Control Methodsmentioning

confidence: 99%

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Karis,

Kasdirin,

Abas

et al. 2023

Journal of Robotics and Control

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Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer.

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Section: Recent Development Of Emg Control Methodsmentioning

confidence: 99%

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Karis,

Kasdirin,

Abas

et al. 2023

Journal of Robotics and Control

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show abstract

“…In ref. [55], the authors used DWT coefficients with fuzzy logic to classify EMG signals for various movements. An accuracy of 89.15% was achieved.…”

Section: Fusing Multi-channel Data In Physiological Signalsmentioning

confidence: 99%

BIO‐inspired fuzzy inference system—For physiological signal analysis

Suppiah

Kim

Abidi

et al. 2023

IET Cyber-Syst and Robotics

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When a person's neuromuscular system is affected by an injury or disease, Activities‐for‐Daily‐Living (ADL), such as gripping, turning, and walking, are impaired. Electroencephalography (EEG) and Electromyography (EMG) are physiological signals generated by a body during neuromuscular activities embedding the intentions of the subject, and they are used in Brain–Computer Interface (BCI) or robotic rehabilitation systems. However, existing BCI or robotic rehabilitation systems use signal classification technique limitations such as (1) missing temporal correlation of the EEG and EMG signals in the entire window and (2) overlooking the interrelationship between different sensors in the system. Furthermore, typical existing systems are designed to operate based on the presence of dominant physiological signals associated with certain actions; (3) their effectiveness will be greatly reduced if subjects are disabled in generating the dominant signals. A novel classification model, named BIOFIS is proposed, which fuses signals from different sensors to generate inter‐channel and intra‐channel relationships. It explores the temporal correlation of the signals within a timeframe via a Long Short‐Term Memory (LSTM) block. The proposed architecture is able to classify the various subsets of a full‐range arm movement that performs actions such as forward, grip and raise, lower and release, and reverse. The system can achieve 98.6% accuracy for a 4‐way action using EEG data and 97.18% accuracy using EMG data. Moreover, even without the dominant signal, the accuracy scores were 90.1% for the EEG data and 85.2% for the EMG data. The proposed mechanism shows promise in the design of EEG/EMG‐based use in the medical device and rehabilitation industries.

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“…Threefold cross-validation technique was applied for achieving the classification accuracy [33]. In threefold cross-validation technique, the whole dataset was divided into three equal parts in which two parts were used for training the classifier whereas one part of data was utilized for testing purpose and no part of data was used for validation the classifiers [34], [35]. Fig.…”

Section: Classifiersmentioning

confidence: 99%

Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers

Narayan¹

2021

TURCOMAT

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Electroencephalogram (EEG)signals based brain-computer interfacing (BCI) is the current technology trends in the field of rehabilitation robotic. This study compared the performance of support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) classifier with the combination of eight different features as a feature vector. EEG data were acquired from 20 healthy human subjects with predefined protocols. After the EEG signals acquisition, it was pre-processed followed by feature extraction and classification by using SVM MLP and LDA classifiers. The results exhibited that the SVM method was the best approach with 98.8% classification accuracy followed by MLP classifier. Finally, the SVM classifier and Arduino Mega controller was employed for offline controlling of the gripper of the robotic arm prototype. The finding of this study may be useful for online controlling as well as multi-degree of freedom with multi-class EEG dataset.

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Development of a software module for feature extraction and classification of EMG signals

Cited by 13 publications

References 6 publications

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

BIO‐inspired fuzzy inference system—For physiological signal analysis

Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers

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