XceptionTime: Independent Time-Window Xceptiontime Architecture for Hand Gesture Classification

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2021

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Section: B Pre-processing Stepmentioning

confidence: 94%

FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2021

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“…For utilizing raw sEMG as the input, we follow the pre-processes approach in the existing literature [9,16,25,26] and use a 1 st order low-pass Butterworth filter to smooth the sEMG signals. Moreover, for scaling the sEMG signals, the µ-law transformation is applied to sensors with small values, amplifying their output in a logarithmic fashion [1]. This transformation keeps the scale of sensors with larger magnitudes over time.…”

Section: Preprocessing Stepmentioning

confidence: 99%

“…Recent evolution in Machine Learning (ML) and Deep Neural Networks (DNNs) coupled with advancements of neuro-rehabilitation technologies, have paved the way for the development of new control systems for myoelectric prostheses. In this regard, surface Electromyogram (sEMG) signals [1][2][3][4][5] is, typically, considered as the input for the control system of prostheses. In particular, the use of machine intelligence for sEMG-based Hand Gesture Recognition (HGR) has been the focus of literature due to its unique potentials to improve the functionality of control and consequently increase the quality of life of individuals with the lack of a biological limb.…”

Section: Introductionmentioning

confidence: 99%

Few-Shot Learning for Decoding Surface Electromyography for Hand Gesture Recognition

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

et al. 2021

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This work is motivated by the recent advancements of Deep Neural Networks (DNNs) for myoelectric prosthesis control. In this regard, hand gesture recognition via surface Electromyogram (sEMG) signals has shown a high potential for improving the performance of myoelectric control prostheses. Although the recent researches in hand gesture recognition with DNNs have achieved promising results, they are still in their infancy. The recent literature uses traditional supervised learning methods that usually have poor performance if a small amount of data is available or requires adaptation to a changing task. Therefore, in this work, we develop a novel hand gesture recognition framework based on the formulation of Few-Shot Learning (FSL) to infer the required output given only one or a few numbers of training examples. Thus in this paper, we proposed a new architecture (named as FHGR which refers to "Few-shot Hand Gesture Recognition") that learns the mapping using a small number of data and quickly adapts to a new user/gesture by combing its prior experience. The proposed approach led to 83.99% classification accuracy on new repetitions with few-shot observations, 76.39% accuracy on new subjects with few-shot observations, and 72.19% accuracy on new gestures with few-shot observations.

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“…Ever since the introduction of the AlexNet, there has been several attempts [17][18][19][20] to reduce computational cost associated with CNNs while maintaining high acceptable accuracy. Motivated by these pioneer works especially recent advances in depthwise separable convolution [18,[21][22][23], we propose the SepUnet, which incorporates depthwise separable convolution within the U-Net architecture [4] for single-coil reconstruction task. A key contribution of the proposed architecture, referred to as the SepUnet trained based on a large-scale dataset (fastMRI), is significant reduction in the required number of parameters while retaining high accuracy.…”

Section: Contributionsmentioning

confidence: 99%

SepUnet: Depthwise Separable Convolution Integrated U-Net For MRI Reconstruction

2021 IEEE International Conference on Image Processing (ICIP)

et al. 2021

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