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

Fan, Xinchen; Zou, Lancheng; Liu, Ziwu; He, Yanru; Zou, Lian; Chi, Ruan

doi:10.3390/s22103661

Cited by 6 publications

(3 citation statements)

References 40 publications

(44 reference statements)

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“…In meta-learning, the algorithms learn the strategy to learn from a variety of tasks, thereby allowing them to quickly adapt to new tasks with minimal examples [ 132 ]. An example of a meta-learning algorithm is model-agnostic meta-learning, which learns a model initialization that can be rapidly fine-tuned to a new task [ 133 , 134 ]. As depicted in Fig.…”

Section: Machine-learning Algorithms For Gesture Recognitionmentioning

confidence: 99%

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Pyun,

Kwon,

Yoo

et al. 2023

National Science Review

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Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered precise recognition and practical applications of this technology. Recent advancements in artificial intelligence technology have made significant strides in extracting features from massive and intricate datasets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques to classify simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training datasets. Thereby, machine-learning techniques have endowed the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures, and machine-learning algorithms for hand gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

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Section: Machine-learning Algorithms For Gesture Recognitionmentioning

confidence: 99%

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Pyun,

Kwon,

Yoo

et al. 2023

National Science Review

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“…Wang et al [25] enhanced the LSTM-CNN network by introducing the attention mechanism CBAM, leading to a notable 5.3% increase in recognition accuracy. Fan et al [26] proposed the CSAC-Net network model, leveraging attention mechanisms to focus on crucial information in the channel space, achieving a gesture recognition accuracy of 82.50%. Rahimian et al [27] employed the attention mechanism and temporal convolution in the TC-HGR architecture, achieving a gesture recognition accuracy of 81.65%.…”

Section: Introductionmentioning

confidence: 99%

InRes-ACNet: Gesture Recognition Model of Multi-Scale Attention Mechanisms Based on Surface Electromyography Signals

Luo,

Huang,

Wang

et al. 2024

Applied Sciences

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Surface electromyography (sEMG) signals are the sum of action potentials emitted by many motor units; they contain the information of muscle contraction patterns and intensity, so they can be used as a simple and reliable source for grasping mode recognition. This paper introduces the InRes-ACNet (inception–attention–ACmix-ResNet50) model, a novel deep-learning approach based on ResNet50, incorporating multi-scale modules and self-attention mechanisms. The proposed model aims to improve gesture recognition performance by enhancing its ability to extract channel feature information within sparse sEMG signals. The InRes-ACNet model is evaluated on the NinaPro DB1 and NinaPro DB5 datasets; the recognition accuracy for these datasets can reach 87.94% and 87.04%, respectively, and recognition accuracy can reach 88.37% in the grasping mode prediction of an electromyography manipulator. The results show that the fusion of multi-scale modules and self-attention mechanisms endows a strong ability for the task of gesture recognition based on sparse sEMG signals.

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“…Recently, meta-learning has attracted attention as a way to rapidly adapt to new tasks using small samples by learning internal representations from multiple classification tasks ( 25 - 28 ). Li et al achieved an area under the curve (AUC) of 83.3% with only five samples per category using meta-learning on the International Skin Imaging Collaboration (ISIC) 2018 skin lesion classification data set ( 29 ).…”

Section: Introductionmentioning

confidence: 99%

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification

Liu,

Dong,

et al. 2024

Quant Imaging Med Surg

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Background Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images. Methods The difficulty-aware (Da) method operates by dynamically modifying the cross-entropy loss function applied to learning tasks. This methodology has the ability to intelligently down-weight simpler tasks, while simultaneously prioritizing more challenging tasks. These adjustments occur automatically and aim to optimize the learning process. Additionally, the task-augmentation (Ta) method is used to enhance the meta-training process by augmenting the number of tasks through image rotation and improving the feature-extraction capability. To implement the expansion of the meta-training tasks, various task instances can be sampled during the meta-training stage. Ultimately, the proposed Ta method was introduced to optimize the initialization parameters and enhance the meta-generalization performance of the model. The DaTa-ML model showed promising results by effectively addressing the challenges associated with few-shot DR classification. Results The Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 blindness detection data set was used to evaluate the DaTa-ML model. The results showed that with only 1% of the training data (5-way, 20-shot) and a single update step (training time reduced by 90%), the DaTa-ML model had an accuracy rate of 89.6% on the test data, which is a 1.7% improvement over the transfer-learning method [i.e., residual neural network (ResNet)50 pre-trained on ImageNet], and a 16.8% improvement over scratch-built models (i.e., ResNet50 without pre-trained weights), despite having fewer trainable parameters (the parameters used by the DaTa-ML model are only 0.47% of the ResNet50 parameters). Conclusions The DaTa-ML model provides a more efficient DR classification solution with little annotated data and has significant advantages over state-of-the-art methods. Thus, it could be used to guide and assist ophthalmologists to determine the severity of DR.

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

Cited by 6 publications

References 40 publications

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

InRes-ACNet: Gesture Recognition Model of Multi-Scale Attention Mechanisms Based on Surface Electromyography Signals

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification

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