An end-to-end lower limb activity recognition framework based on sEMG data augmentation and enhanced CapsNet

Zhang, Changhe; Li, Yangan; Yu, Zhen‐Qiang; Huang, Xiaolin; Xu, Jiang; Deng, Chao

doi:10.1016/j.eswa.2023.120257

Cited by 10 publications

(1 citation statement)

References 40 publications

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“…It dynamically adjusts the parameters of both the main capsule layer and the digital capsule layer through an iterative process to enhance feature extraction and classification efficiency. If the prediction results of all sub-capsules are consistent, the corresponding parent capsule activates, thus generating the corresponding feature vector [26]. The dynamic routing mechanism process is shown in Figure 4.…”

Section: Convolutional Block Attention Capsule Network 221 Capsule Ne...mentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

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The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.

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Section: Convolutional Block Attention Capsule Network 221 Capsule Ne...mentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

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An end-to-end hand action recognition framework based on cross-time mechanomyography signals

Zhang,

Li,

Zhang

et al. 2024

Complex Intell. Syst.

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The susceptibility of mechanomyography (MMG) signals acquisition to sensor donning and doffing, and the apparent time-varying characteristics of biomedical signals collected over different periods, inevitably lead to a reduction in model recognition accuracy. To investigate the adverse effects on the recognition results of hand actions, a 12-day cross-time MMG data collection experiment with eight subjects was conducted by an armband, then a novel MMG-based hand action recognition framework with densely connected convolutional networks (DenseNet) was proposed. In this study, data from 10 days were selected as a training subset, and the remaining data from another 2 days were used as a test set to evaluate the model’s performance. As the number of days in the training set increases, the recognition accuracy increases and becomes more stable, peaking when the training set includes 10 days and achieving an average recognition rate of 99.57% (± 0.37%). In addition, part of the training subset is extracted and recombined into a new dataset and the better classification performances of models can be achieved from the test set. The method proposed effectively mitigates the adverse effects of sensor donning and doffing on recognition results.

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Decoding of lower limb continuous movement intention from multi-channel sEMG and design of adaptive exoskeleton controller

Wang,

Zhang,

et al. 2024

Biomedical Signal Processing and Control

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An end-to-end lower limb activity recognition framework based on sEMG data augmentation and enhanced CapsNet

Cited by 10 publications

References 40 publications

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

An end-to-end hand action recognition framework based on cross-time mechanomyography signals

Decoding of lower limb continuous movement intention from multi-channel sEMG and design of adaptive exoskeleton controller

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