Comparison of Recognition Accuracy on Dynamic Hand Gesture Using Feature Selection

2018 ◽  
Author(s):  
Adri Gabriel Sooai ◽  
Patrisius Batarius ◽  
Yovinia Carmeneja Hoar Siki ◽  
Paskalis Andrianus Nani ◽  
Natalia Magdalena Rafu Mamulak ◽  
...  
Keyword(s):  
Feature Selection ◽  
Recognition Accuracy ◽  
Hand Gesture

scholarly journals Electromyogram-based hand gesture recognition robust to various arm postures

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879075 ◽  
Author(s):  
Kiwon Rhee ◽  
Hyun-Chool Shin
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Practical Applications ◽  
Electrode Positioning ◽  
Recognition Technique

In the recognition of electromyogram-based hand gestures, the recognition accuracy may be degraded during the actual stage of practical applications for various reasons such as electrode positioning bias and different subjects. Besides these, the change in electromyogram signals due to different arm postures even for identical hand gestures is also an important issue. We propose an electromyogram-based hand gesture recognition technique robust to diverse arm postures. The proposed method uses both the signals of the accelerometer and electromyogram simultaneously to recognize correct hand gestures even for various arm postures. For the recognition of hand gestures, the electromyogram signals are statistically modeled considering the arm postures. In the experiments, we compared the cases that took into account the arm postures with the cases that disregarded the arm postures for the recognition of hand gestures. In the cases in which varied arm postures were disregarded, the recognition accuracy for correct hand gestures was 54.1%, whereas the cases using the method proposed in this study showed an 85.7% average recognition accuracy for hand gestures, an improvement of more than 31.6%. In this study, accelerometer and electromyogram signals were used simultaneously, which compensated the effect of different arm postures on the electromyogram signals and therefore improved the recognition accuracy of hand gestures.

Clustering Mass Spectral Peaks Increases Recognition Accuracy and Stability of SVM-based Feature Selection

2010 ◽  
Vol 03 (02) ◽  
pp. 048-054 ◽  
Author(s):  
Mikhail Pyatnitskiy ◽  
Maria Karpova ◽  
Sergei Moshkovskii ◽  
Andrey Lisitsa ◽  
Alexander Archakov
Keyword(s):  
Feature Selection ◽  
Recognition Accuracy ◽  
Mass Spectral ◽  
Spectral Peaks ◽  
Accuracy And Stability

Comparative analysis among feature selection of sEMG signal for hand gesture classification by armband

2020 ◽  
Vol 18 (06) ◽  
pp. 1135-1143
Author(s):  
J. Mendes ◽  
M. Freitas ◽  
H. Siqueira ◽  
A. Lazzaretti ◽  
S. Stevan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Comparative Analysis ◽  
Hand Gesture ◽  
Semg Signal ◽  
Gesture Classification ◽  
Selection Of

scholarly journals An Energy-Based Method for Orientation Correction of EMG Bracelet Sensors in Hand Gesture Recognition Systems

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6327
Author(s):  
Lorena Isabel Barona Barona López ◽  
Ángel Leonardo Valdivieso Valdivieso Caraguay ◽  
Victor H. Vimos ◽  
Jonathan A. Zea ◽  
Juan P. Vásconez ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Correction Method ◽  
Maximum Energy ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Energy Channel ◽  
Recognition Systems ◽  
Accuracy Measures ◽  
Common Architecture

Hand gesture recognition (HGR) systems using electromyography (EMG) bracelet-type sensors are currently largely used over other HGR technologies. However, bracelets are susceptible to electrode rotation, causing a decrease in HGR performance. In this work, HGR systems with an algorithm for orientation correction are proposed. The proposed orientation correction method is based on the computation of the maximum energy channel using a synchronization gesture. Then, the channels of the EMG are rearranged in a new sequence which starts with the maximum energy channel. This new sequence of channels is used for both training and testing. After the EMG channels are rearranged, this signal passes through the following stages: pre-processing, feature extraction, classification, and post-processing. We implemented user-specific and user-general HGR models based on a common architecture which is robust to rotations of the EMG bracelet. Four experiments were performed, taking into account two different metrics which are the classification and recognition accuracy for both models implemented in this work, where each model was evaluated with and without rotation of the bracelet. The classification accuracy measures how well a model predicted which gesture is contained somewhere in a given EMG, whereas recognition accuracy measures how well a model predicted when it occurred, how long it lasted, and which gesture is contained in a given EMG. The results of the experiments (without and with orientation correction) executed show an increase in performance from 44.5% to 81.2% for classification and from 43.3% to 81.3% for recognition in user-general models, while in user-specific models, the results show an increase in performance from 39.8% to 94.9% for classification and from 38.8% to 94.2% for recognition. The results obtained in this work evidence that the proposed method for orientation correction makes the performance of an HGR robust to rotations of the EMG bracelet.

New Feature Selection Method Based on SVM-RFE

2014 ◽  
Vol 926-930 ◽  
pp. 3100-3104 ◽  
Author(s):  
Xi Wang ◽  
Qiang Li ◽  
Zhi Hong Xie
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Recognition Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Testing Time ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Accuracy Rate ◽  
New Feature

This article analyzed the defects of SVM-RFE feature selection algorithm, put forward new feature selection method combined SVM-RFE and PCA. Firstly, get the best feature subset through the method of cross validation of k based on SVM-RFE. Then, the PCA decreased the dimension of the feature subset and got the independent feature subset. The independent feature subset was the training and testing subset of SVM. Make experiments on five subsets of UCI, the results indicated that the training and testing time was shortened and the recognition accuracy rate of the SVM was higher.

NEAR-OPTIMAL GEOMETRIC FEATURE SELECTION FOR VISUAL SPEECH RECOGNITION

2013 ◽  
Vol 27 (08) ◽  
pp. 1350026 ◽  
Author(s):  
PREETY SINGH ◽  
VIJAY LAXMI ◽  
MANOJ SINGH GAUR
Keyword(s):  
Feature Selection ◽  
Speech Recognition ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Feature Reduction ◽  
Visual Speech ◽  
Visual Features ◽  
Geometric Feature ◽  
Large Set ◽  
Visual Speech Recognition

To improve the accuracy of visual speech recognition systems, selection of visual features is of fundamental importance. Prominent features, which are of maximum relevance for speech classification, need to be selected from a large set of extracted visual attributes. Existing methods apply feature reduction and selection techniques on image pixels constituting region-of-interest (ROI) to reduce data dimensionality. We propose application of feature selection methods on geometrical features to select the most dominant physical features. Two techniques, Minimum Redundancy Maximum Relevance (mRMR) and Correlation-based Feature Selection (CFS), have been applied on the extracted visual features. Experimental results show that recognition accuracy is not compromised when a few selected features from the complete visual feature set are used for classification, thereby reducing processing time and storage overheads considerably. Results are compared with performance of principal components obtained by application of Principal Component Analysis (PCA) on our dataset. Our set of selected features outperforms the PCA transformed data. Results show that the center and corner segments of the mouth are major contributors to visual speech recognition. Teeth pixels are shown to be a prominent visual cue. It is also seen that lip width contributes more towards visual speech recognition accuracy as compared to lip height.

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