在基于神经驱动的肌肉骨模型的关节约束下改善运动解码性能
Lizhi Pan1,2, Xingyu Yan1,2, Shizhuo Yue1,2
1The Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
Medical & biological engineering & computing
|February 11, 2025
概括
一个新的神经驱动的肌肉骨模型 (N-DMM) 通过使用高密度的EMG信号,改善了对假肢控制的运动解码. 与传统的电肌图驱动模型相比,这种模型可以更准确地估计关节位置.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 康复工程 康复工程 康复工程
背景情况:
- 电肌图驱动的肌肉骨模型 (E-DMMs) 将用户命令与关节位置联系起来,但面临诸如表面EMG信号交叉声等局限性.
- 高密度 (HD) EMG信号分解通过提取神经驱动器来增强人机接口提供了一个解决方案.
研究的目的:
- 提出和验证一种新的神经驱动的肌肉骨模型 (N-DMM),用于在约束条件下改进手腕和手指甲关节 (MCP) 关节位置的解码.
- 为了比较N-DMM与传统的E-DMM在估计关节运动中的性能.
主要方法:
- 从八名受试者中记录了高强度EMG信号,这些受试者在双边镜像训练中用受约束和不受约束的四肢进行了双边训练.
- 快速独立组件分析 (fICA) 用于从EMG信号中提取动力单元放电和估计神经驱动器.
- 神经驱动器作为N-DMM的输入来预测关节运动,而E-DMM用于比较.
主要成果:
- 与E-DMM相比,N-DMM在估计联合立场方面表现优越.
- 拟议的模型显示了连续运动的更准确和更强大的解码的潜力,即使在联合约束下.
结论:
- N-DMM为人机接口提供了有前途的进步,特别是在假肢应用中.
- 进一步开发N-DMM可以显著提高对截肢者的控制精度和稳定性.
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