scholarly journals Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Dianchun Bai ◽  
Tie Liu ◽  
Xinghua Han ◽  
Hongyu Yi
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Training Model ◽  
Optimization Method ◽  
Feature Model ◽  
Long Term Memory ◽  
Semg Signal ◽  
Memory Network ◽  
Artificial Hand

The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded chip with small storage space, this paper presents a feature model construction and optimization method based on multichannel sEMG amplification unit. The feature model is established by using multidimensional sequential sEMG images by combining convolutional neural network and long-term memory network to solve the problem of multistate sEMG signal recognition. The experimental results show that under the same network structure, the sEMG signal with fast Fourier transform and root mean square as feature data processing has a good recognition rate, and the recognition accuracy of complex gestures is 91.40%, with the size of 1 MB. The model can still control the artificial hand accurately when the model is small and the precision is high.

scholarly journals A Control System of SEMG Signal Based on Deep Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Deep Learning ◽  
Control System ◽  
Training Model ◽  
Surface Emg ◽  
Cross Combination ◽  
Semg Signal ◽  
Engineering Technology ◽  
Emg Signal ◽  
Successful Design ◽  
Medical Theory

The research of control system based on sEMG signal is a popular field at present. It collects bioelectricity of human body through surface electrode. It has the new characteristic of subject fusion, and it is the combination of engineering technology and medical theory, specifically the application of cross combination of control science and electrophysiology. In this paper, the human surface EMG signal is taken as the research object, and a manipulator control system based on one-dimensional convolutional neural network (CNN) is proposed, and the functions and implementation methods of each part of the system are analyzed. The experimental results show that the recognition accuracy of the training model is 0.973, and the design scheme of EMG signal recognition and classification system with deep learning method is feasible. The successful design of the system provides technical support and theoretical basis for the further study of electrophysiological signals.

scholarly journals Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification

2019 ◽  
Vol 9 (24) ◽  
pp. 5343 ◽  
Author(s):  
Yutong Wu ◽  
Xinhui Hu ◽  
Ziwei Wang ◽  
Jian Wen ◽  
Jiangming Kan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Extraction Methods ◽  
Optimal Size ◽  
Semg Signal ◽  
Scientific Database ◽  
Feature Extraction Method ◽  
Gesture Control

It is necessary to complete the two parts of gesture recognition and wireless remote control to realize the gesture control of the automatic pruning machine. To realize gesture recognition, in this paper, we have carried out the research of gesture recognition technology based on surface electromyography signal, and discussed the influence of different numbers and different gesture combinations on the optimal size. We have calculated the 630-dimensional eigenvector from the benchmark scientific database of sEMG signals and extracted the features using principal component analysis (PCA). Discriminant analysis (DA) has been used to compare the processing effects of each feature extraction method. The experimental results have shown that the recognition rate of four gestures can reach 100.0%, the recognition rate of six gestures can reach 98.29%, and the optimal size is 516~523 dimensions. This study lays a foundation for the follow-up work of the pruning machine gesture control, and p rovides a compelling new way to promote the creative and human computer interaction process of forestry machinery.

scholarly journals A Novel Adaptive Mutation PSO Optimized SVM Algorithm for sEMG-Based Gesture Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Le Cao ◽  
Wenyan Zhang ◽  
Xiu Kan ◽  
Wei Yao
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
Recognition Rate ◽  
Low Frequency ◽  
Adaptive Mutation ◽  
Support Vector ◽  
Semg Signal ◽  
Svm Algorithm ◽  
Svm Model ◽  
Semg Signals

In the field of noncontact human-computer interaction, it is of crucial importance to distinguish different surface electromyography (sEMG) gestures accurately for intelligent prosthetic control. Gesture recognition based on low sampling frequency sEMG signal can extend the application of wearable low-cost EMG sensor (for example, MYO bracelet) in motion control. In this paper, a combination of sEMG gesture recognition consisting of feature extraction, genetic algorithm (GA), and support vector machine (SVM) model is proposed. Particularly, a novel adaptive mutation particle swarm optimization (AMPSO) algorithm is proposed to optimize the parameters of SVM; moreover, a new calculation method of mutation probability is also defined. The AMPSO-SVM model based on combination processing is successfully applied to MYO bracelet dataset, and four gesture classifications are carried out. Furthermore, AMPSO-SVM is compared with PSO-SVM, GS-SVM, and BP. The sEMG gesture recognition rate of AMPSO-SVM is 0.975, PSO-SVM is 0.9463, GS-SVM is 0.9093, and BP is 0.9019. The experimental results show that AMPSO-SVM is effective for low-frequency sEMG signals of different gestures.

scholarly journals Deep Learning-Based Character Gesture Recognition

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Husam Al-Behadili ◽  
Alaa H. Ahmed ◽  
Hasan M.A. Kadhim
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Kinect Sensor ◽  
Accuracy Rate ◽  
Hand Detection ◽  
Detection Algorithms ◽  
Text Input ◽  
Input Method ◽  
Single Sensor

The article describes a new text input method based on gesture recognition, which enables direct physical-to-digital text input. This enables hand-free and in-air writing without any need for keyboards, mice, etc. This is done with the help of state-of-the-art deep learning methods and a single Kinect sensor. The authors were able to achieve a high-accuracy recognition rate by using any wearable device, in contrast to the existing methods, and utilizing a single sensor. Furthermore, among several existing deep learning structures, the authors determined that the best deep learning structure suitable for the character-based gesture data is the DenseNet Convolutional neural network. For instance, the training loss curve shows that DenseNet has the fastest converging curve compared to the others despite maintaining the highest accuracy rate. Our proposed method allows for the improvement of the recognition rate from 96.6% (in the existing algorithms) to 98.01% when the DenseNet structure is used despite using only a single sensor instead of multiple cameras. The use of the Kinect sensor not only reduces the number of cameras to one but also overrides the necessity for any additional hand detection algorithms. These results aid in improving the speed and the efficiency of the character-based gesture recognition. The proposed system can be used in applications that require accurate decision making, such as in operation rooms.

scholarly journals Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

2021 ◽  
Vol 15 ◽  
Author(s):  
Wei Li ◽  
Ping Shi ◽  
Hongliu Yu
Keyword(s):  
Quality Of Life ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Gesture Recognition ◽  
Upper Limb ◽  
Surface Electromyography ◽  
Natural Control ◽  
Semg Signal ◽  
Prosthetic Hands

Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

Deep-Learning for Hand-Gesture Recognition with Simultaneous Thermal and Radar Sensors

2020 ◽  
Author(s):  
Sruthy Skaria ◽  
Da Huang ◽  
Akram Al-Hourani ◽  
Robin J. Evans ◽  
Margaret Lech
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Radar Sensors

scholarly journals Intersected EMG Heatmaps and Deep Learning Based Gesture Recognition

2020 ◽  
Author(s):  
Weijie Ke ◽  
Yannan Xing ◽  
Gaetano Di Caterina ◽  
Lykourgos Petropoulakis ◽  
John Soraghan
Keyword(s):  
Deep Learning ◽  
Gesture Recognition

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

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