MFFNet: Multi-dimensional Feature Fusion Network based on attention mechanism for sEMG analysis to detect muscle fatigue

Zhang, Yongqing; Chen, Siyu; Cao, Wenpeng; Guo, Peng; Gao, Dongrui; Wang, Manqing; Zhou, Jiliu; Wang, Ting

doi:10.1016/j.eswa.2021.115639

Cited by 33 publications

(12 citation statements)

References 25 publications

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“… Zhang et al (2021) detected muscle fatigue based on the Multidimensional Feature Fusion Network (MFFNet), which is composed of Attention Frequency domain Network (AFNet) and Attention Time-domain Network (ATNet). The result shows 77.37% higher than other classifiers.…”

Section: Classificationmentioning

confidence: 99%

Application of Surface Electromyography in Exercise Fatigue: A Review

Sun

Liu

Lin

et al. 2022

Front. Syst. Neurosci.

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Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

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Section: Classificationmentioning

confidence: 99%

Application of Surface Electromyography in Exercise Fatigue: A Review

Sun

Liu

Lin

et al. 2022

Front. Syst. Neurosci.

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“…With the research of attention mechanisms, some researchers try to use attention mechanisms to fuse essential features of different modal signals to improve the classification performance of HRI (Khushaba et al, 2020 ; Tao et al, 2020 ; Zhang et al, 2021 ; Zhao and Chen, 2021 ). Zhang et al .…”

Section: Related Studymentioning

confidence: 99%

“…Zhang et al . used a multi-dimensional feature fusion network framework to detect muscle fatigue, which used a time-domain attention network and a frequency-domain attention network to extract important time-domain features and frequency-domain features in sEMG signals, respectively, and then Feature fusion is performed, and the results show that the proposed framework can effectively improve the detection performance of muscle fatigue (Zhang et al, 2021 ). After extracting the time-series features of EEG data in different frequency bands, Zhao et al used the self-attention mechanism to extract the essential features of EEG signals in different frequency bands to extract the accuracy of HRI in emotion recognition (Zhao and Chen, 2021 ).…”

Section: Related Studymentioning

confidence: 99%

Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism

Shi

Huang

et al. 2022

Front. Neurosci.

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A challenging task for the biological neural signal-based human-exoskeleton interface is to achieve accurate lower limb movement prediction of patients with hemiplegia in rehabilitation training scenarios. The human-exoskeleton interface based on single-modal biological signals such as electroencephalogram (EEG) is currently not mature in predicting movements, due to its unreliability. The multimodal human-exoskeleton interface is a very novel solution to this problem. This kind of interface normally combines the EEG signal with surface electromyography (sEMG) signal. However, their use for the lower limb movement prediction is still limited—the connection between sEMG and EEG signals and the deep feature fusion between them are ignored. In this article, a Dense con-attention mechanism-based Multimodal Enhance Fusion Network (DMEFNet) is proposed for predicting lower limb movement of patients with hemiplegia. The DMEFNet introduces the con-attention structure to extract the common attention between sEMG and EEG signal features. To verify the effectiveness of DMEFNet, an sEMG and EEG data acquisition experiment and an incomplete asynchronous data collection paradigm are designed. The experimental results show that DMEFNet has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 82.96 and 88.44%, respectively.

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“…Guoqi Chen et al added spatial attention after reducing pooling, retaining a lot of ancillary information and finding out critical information quickly [ 29 ], with which their TDACAPS model achieved the state-of-the-art result. Moreover, Yongqing et al combined Channel Attention and Spatial Attention to process the sEMG signal, providing new ideas for muscle fatigue detection [ 30 ].…”

Section: Related Workmentioning

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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MFFNet: Multi-dimensional Feature Fusion Network based on attention mechanism for sEMG analysis to detect muscle fatigue

Cited by 33 publications

References 25 publications

Application of Surface Electromyography in Exercise Fatigue: A Review

Application of Surface Electromyography in Exercise Fatigue: A Review

Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism

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

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