通过协同映射和重建对多指力进行并发和连续的估计:试点研究
Zhicheng Teng1, Guanghua Xu1, Xun Zhang1
1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China.
Journal of neural engineering
|November 29, 2023
概括
这项研究引入了一种新的方法,将肌肉和力量的协同作用联系起来,以实现直观的肌电控制. 该方法可以从表面电肌图 (sEMG) 信号中改进多指力估计,提高辅助设备的可用性.
科学领域:
- 神经科学是一个神经科学.
- 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
背景情况:
- 目前的肌电接口缺乏直观的控制,阻碍了高效的人机交互.
- 有效控制辅助设备需要精确估计多指力.
研究的目的:
- 通过结合肌肉和力量协同作用来开发一个直观的肌电控制系统,用于多指力力估计.
- 提高控制辅助设备的自然性和效率.
主要方法:
- 十一名受试者执行了对称抓取任务,记录了表面电肌图 (sEMG) 和指尖力数据.
- 肌肉协同效应从sEMG中提取,并从力数据中提取力协同效应.
- 一个线性回归器连接了肌肉和力量协同作用,通过手指状态识别来改进力量预测.
主要成果:
- 五个肌肉协同和四个力量协同提供了计算负载和预测准确性之间的最佳平衡.
- 拟议的SYN-II方法在力估计 (R2=0.80) 中优于传统的基于sEMG振幅的方法.
- SYN-II对未知的任务表现出卓越的概括性,表明了增强的适应性 (R2=0.74).
结论:
- 这项研究介绍了第一个将肌肉和力量协同作用联系起来的方法,用于从sEMG获得并发,连续的多指力力估计.
- 这种方法为高性能肌电接口提供了基础,使机器人能够更自然地控制手.
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