Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

Chen, Lin; Fu, Jianting; Wu, Yuheng; Li, Haochen; Zheng, Bin

doi:10.3390/s20030672

Cited by 146 publications

(69 citation statements)

References 23 publications

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“…Zhai [15] proposed a self-calibrating CNN to improve stability and performance over time on the NinaPro dataset and achieved an improvement of 10.18% on DB2 (intact, 50 motions) and 2.99% on DB3 (amputee, 10 motions) compared to an uncalibrated classifier. Chen [16] proposed a "compact" strategy (EMGNet) to reduce the number of parameters involved in the designing of CNN on NinaPro DB5 and achieved slightly better performance compared to classical machine learning algorithms. Huang [17] utilized spectrogram in conjunction with a CNN-LSTM (Long Short-Term Memory network) combination and showed improved classification (from 77.167% to 79.329%) on the NinaPro dataset.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG

Asif

Waris

Gilani

et al. 2020

Sensors

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Electromyography (EMG) is a measure of electrical activity generated by the contraction of muscles. Non-invasive surface EMG (sEMG)-based pattern recognition methods have shown the potential for upper limb prosthesis control. However, it is still insufficient for natural control. Recent advancements in deep learning have shown tremendous progress in biosignal processing. Multiple architectures have been proposed yielding high accuracies (>95%) for offline analysis, yet the delay caused due to optimization of the system remains a challenge for its real-time application. From this arises a need for optimized deep learning architecture based on fine-tuned hyper-parameters. Although the chance of achieving convergence is random, however, it is important to observe that the performance gain made is significant enough to justify extra computation. In this study, the convolutional neural network (CNN) was implemented to decode hand gestures from the sEMG data recorded from 18 subjects to investigate the effect of hyper-parameters on each hand gesture. Results showed that the learning rate set to either 0.0001 or 0.001 with 80-100 epochs significantly outperformed (p < 0.05) other considerations. In addition, it was observed that regardless of network configuration some motions (close hand, flex hand, extend the hand and fine grip) performed better (83.7% ± 13.5%, 71.2% ± 20.2%, 82.6% ± 13.9% and 74.6% ± 15%, respectively) throughout the course of study. So, a robust and stable myoelectric control can be designed on the basis of the best performing hand motions. With improved recognition and uniform gain in performance, the deep learning-based approach has the potential to be a more robust alternative to traditional machine learning algorithms.

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG

Asif

Waris

Gilani

et al. 2020

Sensors

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“…The commercial Myo TM armband was chosen for data acquisition, and is presented in Figure 2 a. This device is widely used in gesture recognition research [ 46 , 47 ]. Myo TM has eight equidistant channels, with 200 samples/s, 8-bit resolution from ADC (Analog to Digital Converter), and wireless communication via Bluetooth [ 48 ].…”

Section: Methodsmentioning

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 researchers replaced the 3*3 convolution kernel in the VGG-Net model with 3*3*3, and then directly performed convolution operations on the video stream. This work has changed the recognition mode of the traditional video human motion gesture recognition method, and directly realized the video end-to-end classification and recognition, but the training of the entire network is very time-consuming and memory resources [14,15]. Based on the VGG-Net model, combined with the traditional optical flow characteristics, the optical flow graph is also regarded as an image, and a dual data stream deep convolutional neural network is proposed [16,17].…”

Section: Introductionmentioning

confidence: 99%

Human Motion Gesture Recognition Algorithm in Video Based on Convolutional Neural Features of Training Images

2020

IEEE Access

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The main work of human motion gesture recognition is to recognize and analyze the behavior of human objects in the video. Although the current research in the field of human motion gesture recognition has achieved certain results, the human motion gesture recognition in real life scenes has great effects due to factors such as camera movement, target scale transformation, dynamic background, viewing angle, and illumination. This article first proposes a new method of constructing human motion posture features to describe human behavior. This method is based on deep convolutional neural network features and topic models. Experiments have verified that compared with the traditional feature map extracted from the convolutional neural network fully connected layer, the feature map extracted from the convolutional neural network convolutional layer is not only lower in dimension but also has higher discrimination. Secondly, based on the feature map of the convolutional neural network, the training map downsampling strategy is used to overcome the interference caused by the object's scale change and shape change. Finally, based on the basketball gesture recognition method, the behavior performance of the legs and arms in 9 basketball actions of walking, running, jumping, standing dribbling, walking dribbling, running dribbling, shooting, passing and receiving is analyzed. As well as the corresponding signal waveform characteristics, a two-stage data division method for basketball is proposed. The unit action data is extracted for analysis to realize feature extraction. In order to select the most suitable classifier for basketball gesture recognition, the constructed feature vector uses four Different classifiers are trained to construct different classifiers to realize the division of actions. INDEX TERMS Convolutional neural networks; training Images; human motion gesture recognition; classifier; feature vector Xiangui Bu was born in WeiShan, China, in 1981. Master of Physical Education, master of Education doctor, associate professor, master's supervisor, head of boxing course of Shandong Sport University, the first famous teacher of Shan dong Sport University. Visiting scholar of

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Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

Cited by 146 publications

References 23 publications

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG

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

Human Motion Gesture Recognition Algorithm in Video Based on Convolutional Neural Features of Training Images

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