基于神经驱动肌肉骨模型的手腕和手部运动的同时和比例控制
概括
使用高密度电肌图 (HD EMG) 分解的新神经驱动模型提高了人机界面 (HMI) 控制精度. 与传统的EMG驱动模型相比,这种先进的方法提供了更强大的关节运动预测.
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:
- 基于电肌图 (EMG) 的人机接口 (HMI) 允许同时和比例控制 (SPC) 多个自由度 (DoF).
- 传统的EMG驱动的肌肉骨模型 (MMs) 用于假肢和机器人控制,由于表面EMG的信号扭曲而面临限制.
- 高密度 (HD) EMG分解提供了一种方法,可以从表面EMG中提取更准确的神经驱动信号.
研究的目的:
- 提出和评估一种新的神经驱动的肌肉骨模型 (MM),用于预测人类关节运动.
- 将神经驱动MM的预测准确度与传统的EMG驱动MM进行比较.
- 评估神经驱动MM在医疗康复和机器人控制中增强HMI的潜力.
主要方法:
- 通过使用64通道电极网,记录了来自十名非残疾人前臂肌肉的高密度 (HD) EMG信号.
- 同步记录的手腕和手腕关节角.
- 分解高清EMG信号来提取动力单元 (MU) 放电,估计神经驱动,并输入到MM进行运动预测.
主要成果:
- 与EMG驱动的MM相比,神经驱动的MM表现优越,具有更高的皮尔森相关系数 (r) 和更低的正常化根平均平方误差 (NRMSE).
- 该模型准确地预测了MCP和手腕关节曲/延伸运动.
- 离线分析表明,神经驱动方法的预测准确性和稳定性得到了提高.
结论:
- 拟议的神经驱动MM在预测关节运动方面明显优于传统的EMG驱动MM.
- 通过高清EMG分解精确的神经命令提取可以提高HMI性能.
- 这种方法在推进假肢控制,机器人应用和医疗康复方面具有相当大的潜力.
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