用肌电图预测肌肉厚度变化，实现便携式健康监测
Predicting Muscle Thickness Deformation from Muscle Activation Patterns: A Dual-Attention Framework
- 设计双注意力网络，融合自注意力与交叉注意力建模肌电信号
- 六名受试者实验显示平均预测精度达0.923±0.900毫米
- 适合临床诊断、运动科学与康复领域实时监测应用
理解肌肉激活与厚度变形之间的关系对诊断肌肉疾病和监测肌肉健康至关重要。尽管超声技术可测量运动中肌肉厚度的变化，但其在便携设备中的应用受限于布线和数据采集挑战。表面肌电图（sEMG）则能记录肌肉生物电信号作为激活指标。本文提出一种深度学习方法，利用sEMG信号直接预测肌肉厚度变形，无需超声测量。采用结合自注意力与交叉注意力机制的双注意力框架，从sEMG数据中预测肌肉位移。六名健康受试者的实验结果表明，该方法可准确预测肌肉位移，平均精度为0.923±0.900毫米，验证了其在实时便携式肌肉健康监测中的潜力，适用于临床诊断、运动科学与康复场景。
原文摘要 · Abstract (English)
Understanding the relationship between muscle activation and thickness deformation is critical for diagnosing muscle-related diseases and monitoring muscle health. Although ultrasound technique can measure muscle thickness change during muscle movement, its application in portable devices is limited by wiring and data collection challenges. Surface electromyography (sEMG), on the other hand, records muscle bioelectrical signals as the muscle activation. This paper introduced a deep-learning approach to leverage sEMG signals for muscle thickness deformation prediction, eliminating the need for ultrasound measurement. Using a dual-attention framework combining self-attention and cross-attention mechanisms, this method predicted muscle deformation directly from sEMG data. Experimental results with six healthy subjects showed that the approach could accurately predict muscle excursion with an average precision of 0.923$\pm$0.900mm, which shows that this method can facilitate real-time portable muscle health monitoring, showing potential for applications in clinical diagnostics, sports science, and rehabilitation.
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