Electromyography (EMG) signal classification for wrist movement using naïve bayes classifier

Abstract

Electromyography (EMG) signal is an myoelectric signal in the muscle layer. It occurs caused by contraction and relaxation muscle activity. This article provide numerical study of the classifying the electromyography signal for wrist movement combined with open and grasping finger flexor. The EMG signal has recorded using a device called electromyography. It has acquired by attaching an surface electrode in the skin then the electrode was capturing the raw signal. The volunteer involved were six where each volunteer has ten datasets the EMG signal. The surface electrode are sticked in the lower arm muscle. The EMG raw signal was processed using zero-mean normalization. The feature extraction method is root mean square (rms), mean absolute value (mav), variance (var), and standard deviation (std). This EMG signal has been classified by naïve bayes classifier. Training and testing data was using 5-cross validation. The result indicates that the classification accuracy for classifying the EMG signal for wrist movement combined open finger flexor (OFF) and grasping finger flexor (GFF) is 70% and 75% respectively. Therefore, the EMG signal can be applied for identificating of muscle disorder, prostheses hand and biometric system.

Effective Quantization Evaluation Method of Functional Movement Screening with Improved Gaussian Mixture Model

“…Next, the feature data with unknown scores are modeled using each of the 3 Gaussian mixture models, and finally, maximum likelihood estimation is performed to obtain the evaluation results. We evaluated the performance of the new classifier by comparing it with three basic classifiers, including the traditional Gaussian mixture model [26], Naïve Bayes [27], and AdaBoost.M1 classifier [15].…”

SVM-based Real-Time Classification of Prosthetic Fingers using Myo Armband-acquired Electromyography Data

“…sEMG is the technique that acquires complex signals from a group of muscles for individual action, and requires classification techniques for motor movements. For prosthesis, several techniques have been used such as Wigner-Ville distribution [2], SVM [3], Naïve Bayes Classifier [4], Higher-Order Statistics [5], Artificial Neural Network [6], etc. The Support Vector Machine (SVM) is a method for locating a hyperplane in an N-dimensional space that classifies data points explicitly.…”

“…The Nave Bayes classifier is another machine learning model that classifies signals based on probability. The classifier's core is built on the Bayes theorem [4]. In this paper, SVM has been employed on real-time sEMG signals.…”

“…The proposed framework employed Myo Armband to acquire sEMG data. SVM was employed as it is efficient as compared to classification methods such as LDA [14], Naïve Bayes classifier [4], [14] , random forest [14], bicoherence method [9]. In myoelectric control, mostly LDA has been used for real time application due to it simple structure and linearity property and hence with minimal delay time.…”