基于自编码器的肌电控制器用于假肢手
Alexandra A Portnova-Fahreeva1, Fabio Rizzoglio2, Ferdinando A Mussa-Ivaldi1,2
1Department of Mechanical Engineering, Northwestern University, Evanston, IL, United States.
Frontiers in bioengineering and biotechnology
|July 12, 2023
概括
这项研究引入了一种新型的自动编码控制器,用于先进的假肢手控制. 该系统允许用户使用简化的2D空间操纵高维虚拟手,提高手势匹配速度和效率.
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
- 神经科学是一个神经科学.
背景情况:
- 像主要组件分析 (PCA) 这样的线性维度减小技术在简化高维假肢手的复杂肌电控制方面存在局限性.
- 非线性方法,特别是自动编码器,在压缩和重建复杂的手动力学数据方面表现出卓越的性能.
研究的目的:
- 介绍和评估一种基于Autoencoder的新控制器,用于直观地控制高维虚拟手的肌电控制.
- 评估控制器在使用户能够通过缩小的二维空间操纵17维虚拟手的有效性.
主要方法:
- 开发一种基于Autoencoder的新型控制器,用于将高维的手数据映射到低维的控制空间.
- 验证实验涉及四名无障碍参与者使用开发的肌电界面控制虚拟手.
主要成果:
- 参与者显著减少了与虚拟手匹配目标手势所需的时间.
- 在四分之三的参与者中观察到改善的路径效率,这表明控制精度提高.
- 与传统的PCA方法相比,Autoencoder控制器在假肢手操纵中显示出更高准确度的潜力.
结论:
- 基于Autoencoder的控制器显示出显著的希望,可以增强对高维度假肢手的肌电控制.
- 需要进一步的研究来优化现实世界假肢应用中Autoencoder控制器的学习策略.
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