Electromyography Signal Classification Using Deep Learning

Gaso, Mekia Shigute; Cankurt, Selçuk; Subaşı, Abdülhamit

doi:10.1109/icecco53203.2021.9663803

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…The Accuracy, Precision, Sensitivity, and F-score parameters were used to analyse the performance of the methods proposed in this study. Accuracy is the ratio of the number of correctly classified samples to the total number of samples (15), Precision is the ratio of correct positive values to the classified positive values (16), Sensitivity is the ratio of the number of correctly classified samples to the number of positive samples (17)…”

Section: Resultsmentioning

confidence: 99%

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Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Gursoy

2024

ELEKTRON ELEKTROTECH

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Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, F-score parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems.

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Section: Resultsmentioning

confidence: 99%

“…DWT is a conventional multiresolution analysis method for nonstationary signals. In general, wavelet-based techniques are a viable method in the analysis of variable signal types such as EMG [15].…”

Section: B Discrete Wavelet Transformmentioning

confidence: 99%

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Gursoy

2024

ELEKTRON ELEKTROTECH

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“…The feature vector to be obtained will be determined by the EMD method with 4 IMF components whose dimensions are the same as the input signal. Seven statistical methods were chosen to characterize the frequency behaviour of signals in the IMF components(Albaqami et al, 2021;Gaso et al, 2021).1. The mean of the absolute value of each IMF signal 𝜇 = 1 𝑁 ∑|𝑦 𝑖 | 𝑁…”

mentioning

confidence: 99%

Biometric Personal Classification with Deep Learning Using EMG Signals

BİLGİN,

GÜRSOY,

ALKAN

2023

Bilge International Journal of Science and Technology Research

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Biometric person recognition systems are becoming increasingly important due to their use in places requiring high security. Since it includes the physical and behavioral characteristics of people, the iris structure, which is a traditional person recognition system, is more secure than methods such as fingerprints or speech. In this study, a deep learning-based person classification/recognition model is proposed. The Gesture Recognition and Biometrics ElectroMyogram (GrabMyo) dataset from the open access PhysioNet database was used. With the 28-channel EMG device, 10 people were asked to make a fist movement with their hand. During the fist movement, data were recorded with the EMG device from the arm and wrist for 5 seconds with a sampling frequency of 2048. The EMD method was chosen to determine the spectral properties of EMG signals. With the EMD method, 4 IMF signal vectors were obtained from the high frequency components of the EMG signals. The classification performance effect of the feature vector is increased by using statistical methods for each IMF signal vector. Feature vectors are classified with CNN and LSTM methods from deep learning algorithms. Accuracy, Precision, Sensitivity and F-Score parameters were used to determine the performance of the developed model. An accuracy value of 95.57% was obtained in the model developed with the CNN method. In the LSTM method, the accuracy value was 93.88%. It is explained that the deep learning model proposed in this study can be effectively used as a biometric person recognition system for person recognition or classification problems with the EMG signals obtained during the fist movement. In addition, it is predicted that the proposed model can be used effectively in the design of future person recognition systems.

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