在线肌肉激活发作检测使用条件异性电肌图信号的概率
IEEE transactions on bio-medical engineering
|December 29, 2023
概括
这项研究引入了一种新方法,利用从通用自回归条件异种性异种性 (GARCH) 模型中获得条件异种性异种性 (LCH) 的概率,用于在表面电肌图 (sEMG) 信号中准确检测肌肉激活开始.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 表面电肌图 (sEMG) 信号对于人机接口至关重要,它包含有关神经肌肉活动的详细信息.
- 准确的,实时检测肌肉激活开始 (MAO) 对于在假肢和外骨中EMG触发的控制至关重要.
研究的目的:
- 为了研究使用通用自回归条件异构度 (GARCH) 模型来分析sEMG信号.
- 提出一种新的特征,即有条件异种性 (LCH) 的可能性,用于有效检测MAO.
主要方法:
- 利用GARCH模型专注于sEMG信号的变量.
- 从GARCH参数估计开发了LCH功能,以区分信号和噪声.
- 简化在线参数估计用于直接LCH计算.
- 采用基本值分类器与LCH功能用于MAO检测.
主要成果:
- 在两个开源数据集上验证了拟议的方法,与现有技术相比显示出更高的性能.
- 在在线条件下,MAO检测的平均绝对误差约为65ms.
- 证明了基于LCH的方法的高精度,普遍性和噪声不敏感性.
结论:
- 根据GARCH模型推出的LCH特征在sEMG信号中对MAO实时检测非常有效.
- 由于其性能特征,这种新的方法对现实世界的应用具有显著的潜力.
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