AI 中文总结
研究基于表面肌电信号的肌肉疲劳检测问题，提出基于脉冲神经网络的节能框架及量化兼容训练方案，所提方法在多噪声条件下评估，匹配或超越基线，更稳定且大幅降低能耗，有在低功耗可穿戴系统实时部署的潜力。
AI 中文摘要
通过表面肌电信号（sEMG）检测肌肉疲劳对体育、康复和可穿戴健康监测等应用至关重要。准确及时检测疲劳对预防损伤、优化身体表现和确保长时间活动中的用户安全至关重要。然而，现有深度学习模型因计算成本高和依赖大规模数据而不适用于此任务。本文提出基于脉冲神经网络（SNNs）的肌肉疲劳检测节能框架，利用稀疏、事件驱动计算和时间建模。还引入量化兼容训练方案（SDH），结合多个正则化项以提高噪声条件下的鲁棒性。在两个公共sEMG数据集上针对多种基线和七种噪声条件评估，量化SNNs匹配或超越强基线，在不同噪声下更稳定，能耗降低达201.77倍。结果证明该框架在低功耗可穿戴系统中实时部署的强大潜力。
英文摘要
Detecting muscle fatigue via surface electromyography (sEMG) is essential for applications in sports, rehabilitation, and wearable health monitoring. Accurate and timely detection of fatigue is crucial for preventing injuries, optimizing physical performance, and ensuring user safety during prolonged activity. However, existing deep learning models are often unsuitable for this task due to their high computational cost and dependence on large-scale data. In this work, we propose an energy-efficient framework for muscle fatigue detection based on Spiking Neural Networks (SNNs), which exploit sparse, event-driven computation and temporal modeling. We further introduce a quantization-compatible training scheme (SDH) that combines multiple regularization terms to improve robustness under noisy conditions. Evaluated on two public sEMG datasets against a broad set of baselines and under seven noise conditions including physically motivated perturbations, our quantized SNNs match or exceed strong baselines while remaining more stable under diverse noise and reducing estimated energy consumption by up to 201.77x. These results demonstrate the framework's strong potential for real-time deployment in low-power wearable systems.
Comments9 pages, 5 figures
