East China University of Technology, Nanchang, China.
East China Jiaotong University, Nanchang, China.
PLoS One. 2024 Nov 4;19(11):e0310035. doi: 10.1371/journal.pone.0310035. eCollection 2024.
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 model's classification accuracy and convergence, as demonstrated in ablation experiments. The model, optimized with multi-level attention mechanisms, excels in accuracy and generalization, particularly in handling data with pseudo-artifacts. Computational analysis indicates that the proposed optimization algorithm has minimal impact on CNN's training and testing times. The study achieves recognition accuracies of 92.52%, 92.38%, and 92.30%, as well as F1-scores of 91.92%, 92.13%, and 92.29% for the three fatigue states, affirming its reliability. This research provides technical support for the development of affordable and dependable wearable motion monitoring devices.
本研究旨在开发一种具有成本效益和可靠性的运动监测设备，能够进行全面的疲劳分析。它通过特征融合策略将表面肌电图（sEMG）和加速度计（ACC）信号集成在一起，实现了这一目标。本研究引入了一种多级注意力机制进行分类，利用卷积神经网络（CNN）。预处理阶段涉及局部特征注意力机制，使用幅度包络增强局部波形特征。采用双通道和神经元级别的双尺度注意力机制，从高维融合数据中增强模型的学习能力，提高特征提取和泛化能力。局部特征注意力机制在消融实验中显著提高了模型的分类准确性和收敛性。经过多级注意力机制优化的模型在准确性和泛化能力方面表现出色，尤其在处理具有伪伪像的数据时表现出色。计算分析表明，所提出的优化算法对 CNN 的训练和测试时间的影响很小。该研究实现了 92.52%、92.38%和 92.30%的识别准确率，以及 91.92%、92.13%和 92.29%的三个疲劳状态的 F1 分数，证明了其可靠性。本研究为开发具有成本效益和可靠性的可穿戴运动监测设备提供了技术支持。
