Multilevel attention mechanism for motion fatigue recognition based on sEMG and ACC signal fusion.

ABSTRACT: This study aims to develop a cost-effective and reliable motion monitoring device capable of comprehensive fatigue analysis. It achieves this objective by integrating surface electromyography (sEMG) and accelerometer (ACC) signals through a feature fusion strategy. The study introduces a multi-level attention mechanism for classification, leveraging convolutional neural networks (CNNs). The preprocessing phase involves a local feature attention mechanism that enhances local waveform features using the amplitude envelope. A dual-scale attention mechanism, operating at both channel and neuron levels, is employed to enhance the model's learning from high-dimensional fused data, improving feature extraction and generalization. The local feature attention mechanism significantly improves the mo

SUBMITTER: Mu D 

PROVIDER: S-EPMC11534257 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature