TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement

• URL: http://arxiv.org/abs/2410.03843v2
• Date: Tue, 8 Oct 2024 16:50:15 GMT
• Title: TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement
• Authors: Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh, Sheng-Yu Peng, Yu Tsao,
• Abstract summary: This paper proposes a novel neural network (NN)-based sEMG denoising method called TrustEMG-Net. TrustEMG-Net achieves a minimum improvement of 20% compared with existing sEMG denoising methods.
• Score: 14.421826563179101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface electromyography (sEMG) is a widely employed bio-signal that captures human muscle activity via electrodes placed on the skin. Several studies have proposed methods to remove sEMG contaminants, as non-invasive measurements render sEMG susceptible to various contaminants. However, these approaches often rely on heuristic-based optimization and are sensitive to the contaminant type. A more potent, robust, and generalized sEMG denoising approach should be developed for various healthcare and human-computer interaction applications. This paper proposes a novel neural network (NN)-based sEMG denoising method called TrustEMG-Net. It leverages the potent nonlinear mapping capability and data-driven nature of NNs. TrustEMG-Net adopts a denoising autoencoder structure by combining U-Net with a Transformer encoder using a representation-masking approach. The proposed approach is evaluated using the Ninapro sEMG database with five common contamination types and signal-to-noise ratio (SNR) conditions. Compared with existing sEMG denoising methods, TrustEMG-Net achieves exceptional performance across the five evaluation metrics, exhibiting a minimum improvement of 20%. Its superiority is consistent under various conditions, including SNRs ranging from -14 to 2 dB and five contaminant types. An ablation study further proves that the design of TrustEMG-Net contributes to its optimality, providing high-quality sEMG and serving as an effective, robust, and generalized denoising solution for sEMG applications.

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