Classification of sEMG signals of hand gestures based on energy features

2021 ◽  
Vol 70 ◽  
pp. 102948
Author(s):  
Naveen Kumar Karnam ◽  
Anish Chand Turlapaty ◽  
Shiv Ram Dubey ◽  
Balakrishna Gokaraju
Keyword(s):  
Hand Gestures ◽  
Semg Signals

WD-EEMD based Voting Classifier for hand gestures classification using sEMG signals

2021 ◽  
Author(s):  
Puru Lokendra Singh ◽  
Samidha Mridul Verma ◽  
Ankit Vijayvargiya ◽  
Rajesh Kumar
Keyword(s):  
Hand Gestures ◽  
Semg Signals

scholarly journals Classification of EMG signal for multiple hand gestures based on neural network

2020 ◽  
Vol 17 (1) ◽  
pp. 256 ◽  
Author(s):  
Mohd Azlan Abu ◽  
Syazwani Rosleesham ◽  
Mohd Zubir Suboh ◽  
Mohd Syazwan Md Yid ◽  
Zainudin Kornain ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Invasive Technique ◽  
Hand Gestures ◽  
Emg Signal ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

<span>This paper presents the classification of EMG signal for multiple hand gestures based on neural network. In this study, the Electromyography is used to measure the muscle cell’s electrical activities which is commonly represented in a function time. Every muscle has their own signals, which was produced in every movement. Surface electromyography (sEMG) is used as a non-invasive technique for acquiring the EMG signal. The development of sensors’ detection and measuring the EMG have been improved and have become more precise while maintaining a small size. In this paper, the main objective is to identify the hand gestures based on: (1) Cylindrical Grasp, (2) Supination (Twist Left), (3) Pronation (Twist Right), (4) Resting Hand and (5) Open Hand that are predefined by using Arduino IDE, CoolTerm software and Microsoft Excel before using artificial neural network for classifying purposes in MATLAB. Finally, the extraction of the EMG patterns for each movement went through features extraction of the signals which is used to train the classifier in MATLAB to classify signals in the neural network. The features extracted are using mean absolute value (MAV), median, waveform length (WL) and root mean square (RMS). The Artificial Neural Network (ANN) produced accuracy of 80% for training and testing for 10 hidden neurons layer.</span>

scholarly journals sEMG based Classification of Hand Gestures using EMD Algorithm and its Application on Control System

2019 ◽  
Vol 8 (11S) ◽  
pp. 1103-1105
Keyword(s):  
Support Vector Machine ◽  
Control System ◽  
Empirical Mode Decomposition ◽  
Time Domain ◽  
Support Vector ◽  
Hand Gestures ◽  
Mode Decomposition ◽  
Single Sensor ◽  
Mean Variance

Single sensor is employed for classifying four hand gestures from flexor carpum ulnaris. The first three IMFs that are obtained as a result of Empirical Mode Decomposition are taken into consideration. Time domain features like mean, variance, skewness, etc are taken for each IMFs. Support Vector Machine was used for classification task and the extracted model is used for making predictions

scholarly journals Feature Extraction and Classification of Lower Limb Motion Based on sEMG Signals

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 132882-132892 ◽  
Author(s):  
Xin Shi ◽  
Pengjie Qin ◽  
Jiaqing Zhu ◽  
Maqiang Zhai ◽  
Weiren Shi
Keyword(s):  
Feature Extraction ◽  
Lower Limb ◽  
Semg Signals

scholarly journals Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2370 ◽  
Author(s):  
Hyun-Joon Yoo ◽  
Hyeong-jun Park ◽  
Boreom Lee
Keyword(s):  
Dictionary Learning ◽  
Classification Accuracy ◽  
Signal Classification ◽  
Hand Movements ◽  
Myoelectric Signal ◽  
Quality Performance ◽  
Myoelectric Prostheses ◽  
Discriminative Feature ◽  
Semg Signals

Surface electromyography (sEMG) signals comprise electrophysiological information related to muscle activity. As this signal is easy to record, it is utilized to control several myoelectric prostheses devices. Several studies have been conducted to process sEMG signals more efficiently. However, research on optimal algorithms and electrode placements for the processing of sEMG signals is still inconclusive. In addition, very few studies have focused on minimizing the number of electrodes. In this study, we investigated the most effective method for myoelectric signal classification with a small number of electrodes. A total of 23 subjects participated in the study, and the sEMG data of 14 different hand movements of the subjects were acquired from targeted muscles and untargeted muscles. Furthermore, the study compared the classification accuracy of the sEMG data using discriminative feature-oriented dictionary learning (DFDL) and other conventional classifiers. DFDL demonstrated the highest classification accuracy among the classifiers, and its higher quality performance became more apparent as the number of channels decreased. The targeted method was superior to the untargeted method, particularly when classifying sEMG signals with DFDL. Therefore, it was concluded that the combination of the targeted method and the DFDL algorithm could classify myoelectric signals more effectively with a minimal number of channels.

scholarly journals Recognition and Classification of Dynamic Hand Gestures by a Wearable Data-Glove

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Francesco Pezzuoli ◽  
Dario Corona ◽  
Maria Letizia Corradini
Keyword(s):  
Hand Gestures ◽  
Data Glove
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