STCN-GR: Spatial-Temporal Convolutional Networks for Surface-Electromyography-Based Gesture Recognition

Lai, Zhiping; Kang, Xiaoyang; Wang, Hongbo; Zhang, Weiqi; Zhang, Xueze; Gong, Peixian; Niu, Lan; Huang, Huijie

doi:10.1007/978-3-030-92238-2_3

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…The CapgMyo dataset consists of 3 sub-datasets (DB-a, DB-b, and DB-c). Until now, many researches have achieved good results (which are even saturated) on supervised learning [ 40 , 41 , 42 ]. However, the task of inter-subject gesture recognition still has much room for improvement [ 31 , 43 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning

Fan

Zou

Liu

et al. 2022

Sensors

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Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human–computer interaction. However, subject specificity of sEMG along with the offset of the electrode makes it challenging to develop a model that can quickly adapt to new subjects. In view of this, we introduce a new deep neural network called CSAC-Net. Firstly, we extract the time-frequency feature from the raw signal, which contains rich information. Secondly, we design a convolutional neural network supplemented by an attention mechanism for further feature extraction. Additionally, we propose to utilize model-agnostic meta-learning to adapt to new subjects and this learning strategy achieves better results than the state-of-the-art methods. By the basic experiment on CapgMyo and three ablation studies, we demonstrate the advancement of CSAC-Net.

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Section: Experiments and Resultsmentioning

confidence: 99%

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning

Fan

Zou

Liu

et al. 2022

Sensors

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“…As we all know, deep learning develops rapidly. In the last few years, some novel models have been developed and used to classify the movements of the hand and wrist joints, for example, CNN-BiLSTM (Nguyen-Trong et al, 2021 ; Tripathi et al, 2022 ) and Graph Convolutional Network (GCN; Lai et al, 2021 ; Yang et al, 2022 ). However, to the best of our knowledge, they have not been used in studies to classify ankle movements.…”

Section: Discussionmentioning

confidence: 99%

“…The introduction of deep learning methods in motion intention recognition using sEMG signals has led to good classification results (Huang et al, 2019 ; Gautam et al, 2020 ; Lai et al, 2021 ; Nguyen-Trong et al, 2021 ; Tripathi et al, 2022 ; Yang et al, 2022 ). Regarding ankle movement classification, Chen et al ( 2019 ) used a cerebellar model neural network (CMNN) to classify two ankle movements (IV and EV).…”

Section: Introductionmentioning

confidence: 99%

“…They tested this model on three healthy subjects with an average accuracy of 97.55%, which is a great improvement over traditional machine learning classifiers. However, many previous studies mainly focused on improving the accuracy of classification rather than the real-time performance of online classification (Cheng et al, 2020 ; Gautam et al, 2020 ; Lai et al, 2021 ; Nguyen-Trong et al, 2021 ; Tripathi et al, 2022 ; Yang et al, 2022 ), which is extremely important for bilateral training. The original signals are often directly fed into the deep learning models (Cheng et al, 2020 ; Gautam et al, 2020 ; Lai et al, 2021 ; Nguyen-Trong et al, 2021 ; Tripathi et al, 2022 ; Yang et al, 2022 ); therefore, even a slightly large amount of data can seriously affect the real-time performance (Joshi et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

“…However, many previous studies mainly focused on improving the accuracy of classification rather than the real-time performance of online classification (Cheng et al, 2020 ; Gautam et al, 2020 ; Lai et al, 2021 ; Nguyen-Trong et al, 2021 ; Tripathi et al, 2022 ; Yang et al, 2022 ), which is extremely important for bilateral training. The original signals are often directly fed into the deep learning models (Cheng et al, 2020 ; Gautam et al, 2020 ; Lai et al, 2021 ; Nguyen-Trong et al, 2021 ; Tripathi et al, 2022 ; Yang et al, 2022 ); therefore, even a slightly large amount of data can seriously affect the real-time performance (Joshi et al, 2013 ). Moreover, these deep learning models have a large number of parameters when raw sEMG signals are used, which leads to high computational costs, making their application difficult for occasions requiring real-time performance (Xia et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A CNN-LSTM model for six human ankle movements classification on different loads

Wang²,

Yang³

et al. 2023

Front. Hum. Neurosci.

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This study aims to address three problems in current studies in decoding the ankle movement intention for robot-assisted bilateral rehabilitation using surface electromyogram (sEMG) signals: (1) only up to four ankle movements could be identified while six ankle movements should be classified to provide better training; (2) feeding the raw sEMG signals directly into the neural network leads to high computational cost; and (3) load variation has large influence on classification accuracy. To achieve this, a convolutional neural network (CNN)—long short-term memory (LSTM) model, a time-domain feature selection method of the sEMG, and a two-step method are proposed. For the first time, the Boruta algorithm is used to select time-domain features of sEMG. The selected features, rather than raw sEMG signals are fed into the CNN-LSTM model. Hence, the number of model’s parameters is reduced from 331,938 to 155,042, by half. Experiments are conducted to validate the proposed method. The results show that our method could classify six ankle movements with relatively good accuracy (95.73%). The accuracy of CNN-LSTM, CNN, and LSTM models with sEMG features as input are all higher than that of corresponding models with raw sEMG as input. The overall accuracy is improved from 73.23% to 93.50% using our two-step method for identifying the ankle movements with different loads. Our proposed CNN-LSTM model have the highest accuracy for ankle movements classification compared with CNN, LSTM, and Support Vector Machine (SVM).

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STGNN-LMR: A Spatial–Temporal Graph Neural Network Approach Based on sEMG Lower Limb Motion Recognition

Mao,

Ma,

et al. 2023

J Bionic Eng

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STCN-GR: Spatial-Temporal Convolutional Networks for Surface-Electromyography-Based Gesture Recognition

Cited by 9 publications

References 20 publications

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning

A CNN-LSTM model for six human ankle movements classification on different loads

STGNN-LMR: A Spatial–Temporal Graph Neural Network Approach Based on sEMG Lower Limb Motion Recognition

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