基于肌肉解剖学和序列对序列翻译的变压器框架,用于使用sEMG进行连续关节动力学预测.
IEEE journal of biomedical and health informatics
|July 21, 2025
概括
这项研究引入了一种新的序列对序列模型,用于使用表面电肌图 (sEMG) 信号预测患者的运动意图. 3DCNN-TF模型准确地将神经信号转化为康复机器人的关节角度.
科学领域:
- 生物医学工程 生物医学工程
- 机器人技术 机器人技术 机器人技术
- 神经科学是一个神经科学.
背景情况:
- 准确预测患者的运动意图对于在康复机器人中开发有效的需要辅助 (AAN) 控制至关重要.
- 现有的方法经常在实时预测准确性和通用性方面扎.
研究的目的:
- 将运动意图预测重新定义为一个序列对序列的翻译任务.
- 开发和验证一种新的3DCNN-TF模型,用于将sEMG信号转化为动态表示.
主要方法:
- 拟议的3DCNN-TF模型使用了变压器架构进行序列对序列的翻译.
- 它包含一个3D卷积神经网络 (3DCNN) 模块,从sEMG数据中提取肌肉协同功能.
- sEMG 滑动窗口被编译成输入的"句子",并以自动回归方式生成连接角度.
主要成果:
- 与八个基线模型相比,3DCNN-TF模型在预测手腕和膝关节的角度方面表现优异.
- 实现了高精度,平均nRMSE为6.2% (手腕) 和5.5% (膝盖),R2为95.5% (手腕) 和96.2% (膝盖).
- 该模型提供了强大的概括性,计算效率 (<2分钟的训练),并且可以提前预测高达300ms.
结论:
- 3DCNN-TF模型在预测患者动作意图进行AAN控制方面取得了重大进展.
- 它能够准确地将神经信号转化为动态表现的能力提高了康复机器人的实时适应性.
- 该模型的效率,稳定性和预测性进步对于实际的临床应用至关重要.
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