一个基于会员功能的广泛学习系统,用于在绘制任务下进行人机交互力估计
Biwei Tang1, Ruiqing Li1, Jing Luo2
1School of Automation, Wuhan University of Technology, Luoshi Road, Wuhan, 430070, Hubei, China.
Medical & biological engineering & computing
|June 3, 2023
概括
这项研究引入了一种新方法,用于使用广义学习系统 (BLS) 和表面电肌图 (sEMG) 信号来估计人机交互力. 结合时间和频率域的sEMG特征,可显著提高相互作用力估计的准确性和安全性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 准确估计相互作用力对于确保人机交互 (HRI) 的安全至关重要.
- 以前的方法往往忽略了历史的表面电肌图 (sEMG) 数据,导致不完整的力估计和精度降低.
- 表面电肌图 (sEMG) 信号包含有关人体肌肉力量的宝贵信息.
研究的目的:
- 提出一种新的方法来估计HRI中的相互作用力.
- 为了利用广泛的学习系统 (BLS) 和人类sEMG信号来改进力估计.
- 通过结合历史的sEMG数据来提高相互作用力估计的准确性和完整性.
主要方法:
- 开发了一个新的线性会员函数来计算sEMG信号在不同时间点的贡献.
- 集成的sEMG功能及其时间依赖的贡献到广泛的学习系统 (BLS) 的输入层.
- 探索了五种不同的sEMG特征,包括时间域 (TD) 和频率域 (FD),以及它们的组合.
主要成果:
- 拟议的方法有效地利用历史的sEMG数据通过一个新的会员功能.
- 结合sEMG信号的时间域 (TD) 和频域 (FD) 特性,可以明显提高相互作用力估计质量.
- 绘图任务的实验结果表明,拟议的基于BLS的方法在估计准确性方面优于现有技术.
结论:
- 这种新的方法将BLS与全面的sEMG特征分析集成在一起,在相互作用力估计方面取得了重大进展.
- 开发的线性成员函数成功地捕捉了sEMG信号的时间动态,以便更准确地预测力.
- 这种方法通过提供更精确的相互作用力估计来提高HRI的安全性,特别是在使用结合的TD和FDsEMG功能时.
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