Gesture recognition method based on a single-channel sEMG envelope signal

Wu, Yanxia; Liang, Shili; Zhang, Ling; Chai, Zongqian; Cao, Can; Wang, Shuangwei

doi:10.1186/s13638-018-1046-0

Cited by 42 publications

(20 citation statements)

References 21 publications

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“…ANN was applied as a classifier and achieved an overall accuracy up to 93.25%. The previous study was carried out for five types of hand gesture recognition in a single-channel sEMG [15]. Fourteen-dimensional features were reduced into two-dimensional feature space using the PCA dimensionality reduction.…”

Section: Related Workmentioning

confidence: 99%

Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements

et al. 2020

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A number of researchers prefer using multi-channel surface electromyography (sEMG) pattern recognition in hand gesture recognition to increase classification accuracy. Using this method can lead to computational complexity. Hand gesture classification by employing single channel sEMG signal acquisition is quite challenging, especially for low-rate sampling frequency. In this paper, a study on the pattern recognition method for sEMG signals of nine finger movements is presented. Common surface single channel electromyography (sEMG) was used to measure five different subjects with no neurological or muscular disorder by having nine hand movements. This research had several sequential processes (i.e., feature extraction, feature reduction, and feature classification). Sixteen time-domain features were employed for feature extraction. The features were then reduced using principal component analysis (PCA) into two and three-dimensional feature space. The artificial neural network (ANN) classifier was tested on two different feature sets: (1) using all principal components obtained from PCA (PC1–PC3) and (2) using selected principal components (PC2 and PC3). The third best principal components were then used for classification using ANN. The average accuracy using all subject signals was 86.7% to discriminate the nine finger movements.

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Section: Related Workmentioning

confidence: 99%

Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements

et al. 2020

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“…In this work, the following classifiers were applied: k-nearest neighbor (KNN), linear discriminant analysis (LDA), probabilistic naïve Bayes (NB), quadratic discriminant analysis (QDA), extreme learning machine (ELM) and multi-layer perceptron (MLP) neural networks, random forest (RF), and support vector machines based on linear discrimination (SVMLin) and Gaussian kernel-type based on radial basis function (SVMRBF). These classifiers were selected based on the classifiers listed in related works (Section 2) and presented high accuracies in gesture classification [27,53,60,61]. The k-fold cross-validation method was used, using 10 folds.…”

Section: Feature Extractionmentioning

confidence: 99%

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

Mendes

Freitas

Campos

et al. 2020

Sensors

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Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.

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“…The PCA algorithm plays an important role in the feature extraction of sEMG. It can reduce features dimension, speed up the calculation, and improve the overall accuracy [32][33][34] . The overall weight was set to 0.99, and the PCA automatically selects five features columns.…”

Section: B Using Pca or Notmentioning

confidence: 99%

A Self-Selected Movement Classification Method for Forearm Via sEMG and Attitude Sensor

Zhang¹

2020

Preprint

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Abstract Objective: The use of surface electromyography (sEMG) to realize the recognition of the movement intention can realize the control of the artificial hand or the robot, and can help the rehabilitation training for hemiplegia or muscle weakness. However, the sEMG are weak and susceptible to external interference, so the current research focuses on identifying certain types of movements. But once the subjects are changed, the recognition accuracy will greatly reduce. This study proposes a classification method which the subject could choose optional movements of forearm.Methods: Two sEMG sensors were used, and a 9-axis attitude sensor was added to the wrist. 8 different subjects participated in the experiment, and everyone selected 5 movements. The sEMG sensors were attached to the extensor pollicis brevis and the extensor digitorum. The sEMG features were: Standard Deviation (SD), Power Spectrum Density (PSD); attitude sensor features were: angle and angular acceleration in three dimensional space, and integral of angular acceleration. The results were classified and identified using Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Tree (DT) and Ensembles (En) algorithms. The results of using the sEMG, using the attitude sensor signals and combining the two were compared. Analysis of variance was conducted on the average accuracy. Features were reduced the dimension by the Principal Component Analysis (PCA), and the results of using PCA and not were compared. Results: The results showed that the combination of the two types of sensors could improve the recognition effect compared to the using sEMG sensor or the attitude sensor alone. The final recognition result was that KNN performed best, reaching 95.0%. The results of using PCA were more stable.Conclusion: The method could be used between different subjects, and the user could select the movements autonomously.Significance: This method can improve the adaptability of movement intention recognition based on sEMG, and has important significance for popularizing the use of the sEMG to control the manipulator or the prosthetic and the rehabilitation training.

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Gesture recognition method based on a single-channel sEMG envelope signal

Cited by 42 publications

References 21 publications

Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements

Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

A Self-Selected Movement Classification Method for Forearm Via sEMG and Attitude Sensor

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