sEMG-Based Hand-Gesture Classification Using a Generative Flow Model

Sun, Wentao; Liu, Huaxin; Tang, Rongyu; Lang, Yiran; He, Jiping; Huang, Qiang

doi:10.3390/s19081952

Cited by 23 publications

(9 citation statements)

References 24 publications

(26 reference statements)

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“…Hand motion recognition [9][10][11][12][13][14][15][16][17], Muscle activity recognition [18][19][20][21][22][23] ECG Heartbeat signal classification , Heart disease classification [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], Sleep-stage classification [64][65][66][67][68], Emotion classification [69], age and gender prediction [70] EEG Brain functionality classification , Brain disease classification , Emotion classification [122][123][124][125][126][127][128][129], Sleep-stage classification [130][131][132][133]…”

Section: Emgmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

160

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Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

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Section: Emgmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

160

View full text Add to dashboard Cite

show abstract

“…In [39], the authors evaluate different configurations of RNNs and their results show that a classifier with a bidirectional recurrent layer composed of long short-term memory (LSTM) units followed by attention mechanism performs best in an application classifying 18 gestures from the Ninapro database. The authors of [46] use an unsupervised generative flow model to learn comprehensible features classified by a softmax layer that achieves about 64% accuracy on classifying 53 gestures. RNNs are important for sequence problems where successive inputs are dependent on each other.…”

Section: Related Workmentioning

confidence: 99%

Hilbert sEMG data scanning for hand gesture recognition based on deep learning

Tsinganos

Cornelis

et al. 2020

Neural Comput & Applic

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Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed toward surface electromyography (sEMG)-based gesture recognition, often addressed as an image classification problem using convolutional neural networks (CNNs). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals, which allows the application of typical image processing pipelines such as CNNs on sequence data. The proposed method is evaluated on different state-of-the-art network architectures and yields a significant classification improvement over the approach without the Hilbert curve. Additionally, we develop a new network architecture (MSHilbNet) that takes advantage of multiple scales of an initial Hilbert curve representation and achieves equal performance with fewer convolutional layers.

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“…W. Geng et al [5] built a neural network for gesture recognition on the basis of a single sEMG frame and achieved a recognition accuracy of 77.8% for 52 gestures by performing majority voting on multiple results within a time window. Sun et al [6] achieved a recognition accuracy of 63.86% for 52 gestures with a generative flow model (GFM). A few models relying on auxiliary inertial sensors and feature engineering can achieve high recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

Surface-Electromyography-Based Gesture Recognition Using a Multistream Fusion Strategy

2021

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Gestures are an imortant way to conduct human-computer interaction. The key problem of gesture recognition depending on sEMG (surface electromyography) is how to achieve high recognition accuracy when there are many types of gestures to classify. To solve this problem, first, two basic models were constructed. One is the ConvEMG model based on dense connectivity, the Inception module and depthwise separable convolution; and the other is the LSTMEMG model based on a bidirectional LSTM (Long Short-Term Memory). Then, the basic models were improved with a multistream fusion strategy which utilizes the correlation between gestures and muscles and the complementary advantages of models. To facilitate comparison with others' models, the models proposed in this paper were tested on the public dataset NinaPro DB5, and the improved model named MultiConvEMG achieves an accuracy of 92.83% for 41 gestures, which is superior to its counterparts in the literature on the same dataset. In addition, experiments containing signal acquisition and gesture recognition were carried out for further testing and evaluation. Experimental results show that all models can achieve an accuracy of more than 95% for 31 gestures, and these models have their own strengths in accuracy, immediacy or training cost. All models built in the paper support using sEMG for end-to-end recognition, which means that artificial features are not needed in the processes and data augmentation or IMU devices are not relied on. In other words, our models outperform and have lower application costs than many known models.

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sEMG-Based Hand-Gesture Classification Using a Generative Flow Model

Cited by 23 publications

References 24 publications

Deep Learning in Physiological Signal Data: A Survey

Deep Learning in Physiological Signal Data: A Survey

Hilbert sEMG data scanning for hand gesture recognition based on deep learning

Surface-Electromyography-Based Gesture Recognition Using a Multistream Fusion Strategy

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