E2CNN: An Efficient Concatenated CNN for Classification of Surface EMG Extracted From Upper Limb

Qureshi, Muhammad Farrukh; Mushtaq, Zohaib; Rehman, Muhammad Zia ur; Kamavuako, Ernest Nlandu

doi:10.1109/jsen.2023.3255408

Cited by 17 publications

(10 citation statements)

References 34 publications

(30 reference statements)

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“…We conduct a comprehensive comparison of our proposed gesture classification method with existing CNN-based gesture classification models, including GengNet [42], Cheng et al [43], Wei et al [44], E2CNN [45], Yang et al [46], Zhai et al [25], Ding et al [47], Chen et al [26], Vitale et al [48], Peng et al [49], AtzoriNet [50], CNNLM [51], EVCNN [24], Hu et al [52], Pizzolato et al [53], MSCNet [54], DVMSCNN [55], and MV-CNN [41]. These models, like ours, are CNN-based classifiers that independently classify each frame of EMG data.…”

Section: Compared Methodsmentioning

confidence: 99%

“…Many of them have achieved state-of-the-art performances on the Ninapro datasets. For example, E2CNN [45] and Yang et al [46] have both achieved over 90% classification accuracy on the Ninapro DB1 dataset. Particular emphasis is given to the comparison with MV-CNN [41], as it has been extensively evaluated on diverse public datasets and demonstrated a consistently high accuracy over all datasets.…”

Section: Compared Methodsmentioning

confidence: 99%

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Unveiling EMG semantics: a prototype-learning approach to generalizable gesture classification

Lee,

Jiang,

Yang

et al. 2024

J. Neural Eng.

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Upper limb loss can profoundly impact an individual's quality of life, posing challenges to both physical capabilities and emotional well-being. To restore limb function by decoding electromyography (EMG) signals, in this paper, we present a novel deep prototype learning method for accurate and generalizable EMG-based gesture classification. Existing methods suffer from limitations in generalization across subjects due to the diverse nature of individual muscle responses, impeding seamless applicability in broader populations. By leveraging deep prototype learning, we introduce a method that goes beyond direct output prediction. Instead, it matches new EMG inputs to a set of learned prototypes and predicts the corresponding labels. This novel methodology significantly enhances the model's classification performance and generalizability by discriminating subtle differences between gestures, making it more reliable and precise in real-world applications. Our experiments on four Ninapro datasets suggest that our deep prototype learning classifier outperforms state-of-the-art methods in terms of intra-subject and inter-subject classification accuracy in gesture prediction. The results from our experiments validate the effectiveness of the proposed method and pave the way for future advancements in the field of EMG gesture classification for upper limb prosthetics.

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Section: Compared Methodsmentioning

confidence: 99%

Section: Compared Methodsmentioning

confidence: 99%

Unveiling EMG semantics: a prototype-learning approach to generalizable gesture classification

Lee,

Jiang,

Yang

et al. 2024

J. Neural Eng.

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“…An important bioelectrical indication during muscle contraction, surface electromyography (sEMG) signals are the subject of research’s classification efforts, with a focus on their potential usefulness in controlling prosthetic limbs for the upper limbs 27 . They used an E2CNN, an efficient concatenated convolutional neural network optimized for fast response and real-time performance, to achieve these goals.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence framework for heart disease classification from audio signals

Abbas,

Ojo,

Al Hejaili

et al. 2024

Sci Rep

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As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model’s performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care.

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“…Qureshi, M. F. et al [ 23 ] generated Mel spectrograms based on a 6-channel sEMG dataset from 8 intact subjects performing 11 gesture motions, repeated daily for 7 days, and proposed a CNN model for the classification of Mel Spectrograms, achieving a recognition accuracy of 99.42%. Qureshi, M. F. et al [ 24 ] generated Log-Mel Spectrograms based on a 10-channel sEMG dataset from intact subjects performing 10 gesture motions and proposed an E2CNN model for the classification of Log-Mel Spectrograms, achieving a recognition accuracy of 91.27%. Zhang et al [ 25 ] generated Hilbert graphs from 10-channel sEMG of 52 gesture actions by the Hilbert transform and proposed a dual-view multi-scale convolutional neural network for Hilbert graph classification with recognition accuracy of 86.72%.…”

Section: Introductionmentioning

confidence: 99%

Motion intention recognition of the affected hand based on the sEMG and improved DenseNet network

Niu,

Shi,

Niu

et al. 2024

Heliyon

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E2CNN: An Efficient Concatenated CNN for Classification of Surface EMG Extracted From Upper Limb

Cited by 17 publications

References 34 publications

Unveiling EMG semantics: a prototype-learning approach to generalizable gesture classification

Unveiling EMG semantics: a prototype-learning approach to generalizable gesture classification

Artificial intelligence framework for heart disease classification from audio signals

Motion intention recognition of the affected hand based on the sEMG and improved DenseNet network

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