强大的神经解码,用于精巧控制机器人手动力学
Jiahao Fan1, Luis Vargas2, Derek G Kamper2
1Department of Mechanical Engineering, Pennsylvania State University, University Park, USA.
Computers in biology and medicine
|June 10, 2023
概括
研究人员开发了一种新的神经解码方法,以精确控制假手. 这种方法从高密度电肌图 (HD-EMG) 信号中解码预期的手指运动,以实时,敏捷的机器人手控制.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 由于神经肌肉损伤而失去手的灵敏度,限制了日常任务.
- 目前的辅助机器人手缺乏灵巧的,多个自由度的实时控制.
- 先进的假肢控制仍然是一个重大挑战.
研究的目的:
- 开发一种高效,强大的神经解码方法,用于实时控制假肢手.
- 为了使预期的手指动态运动的连续解码.
- 为了提高辅助机器人手的灵活性和功能.
主要方法:
- 在指部动作过程中获得高密度电肌图 (HD-EMG) 信号.
- 实现了深度学习神经网络,将高清电磁图特征映射到神经驱动信号中.
- 使用预测的神经驱动信号来实时控制假肢手指动力学.
主要成果:
- 与其他方法相比,神经驱动器解码器实现了与其他方法相比,联合角度的预测误差明显较低.
- 解码器性能保持稳定,并对EMG信号变化保持强大.
- 在无意指预测中显示出较高的指分离,误差最小.
结论:
- 开发的神经解码技术提供了一种新且高效的神经机器接口.
- 机器人手指动力学的高精度预测可以实现灵巧的控制.
- 这种方法显著提高了辅助机器人手的能力.
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