一致的控制信息驱动的肌肉骨模型用于多天的肌电控制.
Jiamin Zhao1, Yang Yu1, Xinjun Sheng1,2
1State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Journal of neural engineering
|August 11, 2023
概括
这项研究引入了改进的非负矩阵因子化 (NMF) 算法,以增强电肌图 (EMG) 驱动的肌肉骨模型 (MMs),以实现强大的人机交互,而不需要重新培训.
科学领域:
- 生物医学工程 生物医学工程
- 人与机器的互动 人与机器的互动
- 康复机器人 康复机器人
背景情况:
- 电肌图 (EMG) 驱动的肌肉骨模型 (MMs) 对人机交互至关重要.
- 非静态的EMG信号特征降低了EMG驱动MM的长期性能.
- 为了可靠的长期控制,需要持续的肌肉刺激提取.
研究的目的:
- 为EMG驱动的MMs开发一种强大的零训练肌肉刺激提取方法.
- 为了提高MMs的估计性能,同时预测手和手腕运动.
- 在长期的肌电控制中解决EMG信号的非静止性.
主要方法:
- 通过添加约束和L2-规范规范化术语,开发了一种改进的非负矩阵因子化 (NMF) 算法.
- 增强的NMF被应用于提取稳定的肌肉协同作用及其随时间变化的配置文件.
- 提取的肌肉协同作用被用来驱动运动预测的肌肉骨模型.
主要成果:
- 与现有方法相比,拟议的方法在日间实验中显示出明显优越和强大的性能.
- 竞争方法包括机器学习,EMG信封驱动的MM和基于NMF的经典MM.
- 分析证实了该方法在不同日间实现一致的肌肉刺激方面的有效性.
结论:
- 开发的基于NMF的方法为强大的,零再训练控制肌电接口提供了有希望的途径.
- 这种方法提高了长期应用的EMG驱动MM的可靠性和一致性.
- 这些发现为更直观,更可靠的人机交互系统铺平了道路.
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