一个多模式手来获取肌肉活动和陀螺数据,以研究传感器融合,以检测意图
Daniel Andreas1, Zhongshi Hou1, Mohamad Obada Tabak1
1Chair of Autonomous Systems and Mechatronics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究介绍了一种多模式手,它结合了力肌图和表面电肌图,以更好地控制机器人的手. 传感器融合显著提高了手势分类的准确性,提高了假肢和机器人手的功能.
科学领域:
- 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
- 人与计算机的交互
背景情况:
- 通过生物信号控制机器人手和假肢仍然是一个挑战,表面电肌扫描 (sEMG) 是主要但有限的方法.
- 现有的基于sEMG的系统难以满足实际用于手动解码的可靠性要求.
- 结合多种传感器类型可以提高手势分类的准确性.
研究的目的:
- 开发和评估一种新的多式联动手,以改善手势分类.
- 为了研究传感器融合在先进的假肢和机器人手控制中用于意图检测的有效性.
- 评估使用惯性测量单位 (IMU) 的联合力肌图 (fMG) 和sEMG系统的性能.
主要方法:
- 设计了一个多式联动手,集成一个24通道的fMG系统与六个商用sEMG传感器,每一个配备一个六轴IMU.
- 五名参与者执行了五种不同的手势,并使用拟议的设备记录了肌肉活动.
- 采用随机森林模型,根据获得的多式联络传感器数据对手势进行分类.
主要成果:
- 多式联络设备展示了获得肌肉活动的功能,用于手势分类.
- 将所有传感器模式 (fMG,sEMG,IMU) 结合起来,实现了最高的平均分类准确率92.3±2.6%.
- 与单独使用sEMG相比,传感器融合减少了37%的错误分类,与单独使用fMG相比,减少了22%.
结论:
- 开发的多式联络手适合在意图检测中研究传感器融合.
- 传感器融合为强大而准确的手势分类提供了显著的好处.
- 这种方法对推进机器人和假肢手的控制有希望.
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