A low cost signal acquisition board design for myopathy's EMG database construction

Barioul, Rim; Ghribi, Sameh Fakhfakh; Kanoun, Olfa

doi:10.1109/ssd.2016.7473767

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…The extracted features are used to train and test the offline classifiers using numerical tools. There are three different phases data collection, data processing, and feature extraction [34,35].…”

Section: Databasementioning

confidence: 99%

Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet

et al. 2020

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Electrical biosignals have the potential for use as biometric authenticators, owing to their ability to facilitate liveness detection and concealed nature. In this work, the viability of using surface electromyogram (sEMG) as a biometric modality for users verification is investigated. A database of multi-channel sEMG signals is created using a wearable armband from able-bodied users. Each user used his/her muscles to form a password that consists of a unique combination of specific hand gestures. A total of 18 features are extracted from the signals in order to distinguish between the users. Several features are extracted in the frequency domain after estimating the power spectral density while using the Welch’s method. Specifically, average frequency, signal power, median frequency, Kurtosis, Deciles, coefficient of dissymmetry, and the peak frequency of the sEMG signal are considered. To further increase the accuracy of the classifier, time domain features are also extracted through segmentation of the signal into 10 segments, and then calculating both the root mean square and length of the signal. Several classifiers that are based on K-nearest Neighbors (KNN), Linear Discernment Analysis (LDA), and Ensemble of Classifiers are constructed, trained, and statistically compared, resulting in an average accuracy in 97.4%, 98.3%, and 98.5%, respectively. False acceptance rate (FAR) and False Rejection Rate (FRR) are estimated for each classifier in order to determine the effectiveness of the biometrics verification system. Although the ensemble classifier accuracy was found to be the highest, the results show that the KNN classifier exhibits a FAR of 0.2% and FRR of 2.9%. Thus, the KNN classifier was found to he the optimum classifier after the extraction of all 18 features. This work demonstrates the usefulness of sEMG as a biometric authenticator in user verification.

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Section: Databasementioning

confidence: 99%

Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet

et al. 2020

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“…In general terms, an EMG signal acquisition process requires two main activities: signal sensing and signal pre-processing. Several authors agree in dividing pre-processing of EMG signals into three stages: amplification, filtering, and analog-todigital conversion [51], [21], [57], [59], [60]. Hence, we will consider that EMG signal acquisition systems are comprised by four stages: (1) sensing, (2) amplifying, (3) filtering, and (4) analog-to-digital conversion.…”

Section: B Stage Analysis Of the Emg Signal Acquisition Systemmentioning

confidence: 99%

“…Number of channels used for each application. 1 channel[5],[24],[21],[4]; 3 channels[48]; 4 channels Exoskeletons[25], Electric powered wheelchair control[27],[28], Myoelectric bracelets[37], Handwriting recognition[44], Silent speech recognition[49]; 6 channels[42] and[43]; 8 channels Exoskeletons[24] and[60], Myoelectric bracelets[63], Handwriting recognition…”

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confidence: 99%

Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing–A Systematic Review

et al. 2020

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The myoelectric interfaces are being used in rehabilitation technology, assistance and as an input device. This review focuses on an insightful analysis of the data acquisition system of EMG signals from these interfaces. According to applications reported in research articles of the last five years, the properties of the sensors, the number of channels, the pre-processing of the EMG signal, as well as the software and hardware used were identified. This analysis was performed for the following applications: monitoring of muscular activation for rehabilitation, muscle activation plans, and identification of possible pathologies, exoskeletons, electric of wheelchairs, prosthetics control, myoelectric bracelets, handwriting recognition and silent speech recognition. The results presented in this review become a guide of recommendations for the myoelectric signal processing according to the application of the interface. The main developments, degrees of research and open challenges are also presented in this direction.

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“…In this way, EMG signal processing is a useful tool for the detection of movement intent through the recording of action potentials produced by the activation of skeletal muscles, where the EMG signal is made up of consecutive discharges known as motor unit action potentials (MUAPs) [2], [3]. The EMG signal is the result of the sum of overlapping MUAPs; therefore, it can be broken down into the activity of each of them [4].…”

Section: Introductionmentioning

confidence: 99%

Classification of the Angular Position During Wrist Flexion-extension Based on EMG Signals

Fajardo-Perdomo

Guardo-Gómez

Orjuela-Cañón

et al. 2021

IyU

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Objective: To evaluate a group of features in a myoelectric pattern recognition algorithm to differentiate between five angular positions of the wrist during flexion-extension movements. Materials and Methods: An experimental configuration was made to capture the EMG and wrist joint angle related to flexion-extension movements. After that, a myoelectric pattern recognition algorithm based on a multilayer perceptron artificial neural network (ANN) was implemented. Three different groups were used: Time domain characteristics, autoregressive (AR) model parameters, and representation of time frequency using Wavelet transform (WT). Results and Discussion: The experimental results of 10 healthy subjects indicate that the coefficients of the AR models offer the best parameters for classification, with a differentiation rate of 78 % for the five angular positions studied. The combination of frequency and time frequency resulted in a differentiation rate that reached 82 %. Conclusions: An algorithm based on pattern recognition of EMG signals was used to carry out a comparative study of groups of features that allow for the differentiation of the angular position of the wrist in terms of flexion-extension movements. The method has the potential for application in the field of rehabilitation engineering to detect the user’s movement intent.

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A low cost signal acquisition board design for myopathy's EMG database construction

Cited by 9 publications

References 11 publications

Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet

Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet

Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing–A Systematic Review

Classification of the Angular Position During Wrist Flexion-extension Based on EMG Signals

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