使用基于CNN的深度学习进行连续关节速度估计,用于日常生活活动的多DoF假肢手腕
Zixia Meng1,2, Jiyeon Kang1,3,4
1Mechanical and Aerospace Engineering, School of Engineering and Applied Sciences, University at Buffalo, Buffalo, NY, United States.
Frontiers in neurorobotics
|September 25, 2023
概括
这项研究引入了一种新的肌电控制系统,用于使用表面电肌图 (sEMG) 和快速训练策略的假肢手腕. 该系统提高了日常活动的控制精度,提高了假肢的功能.
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
- 康复技术 康复技术 康复技术
背景情况:
- 假肢的肌电控制使用表面电肌图 (sEMG) 进行意图检测.
- 当前的模型往往因为训练孤立的运动而失败,而不是反映现实生活活动.
- 假肢控制受到手臂姿势,活动持续时间和个人习惯的挑战.
研究的目的:
- 为多度自由度 (DoF) 假肢手臂开发一种新的手腕控制系统.
- 直接将sEMG信号映射到联合速度以进行直观的控制.
- 引入"快速培训"战略,以适应新用户和现实环境.
主要方法:
- 专注于旋转-俯卧和射箭投运动 (DTM).
- 开发了一个系统,将sEMG映射到多DoF手腕关节速度.
- 在24名参与者身上使用根平均平方误差 (RMSE) 和皮尔森相关性来评估表现.
主要成果:
- 射箭投动作任务显示RMSE较低 (例如,子:6.68度/秒),而不是俯仰-俯仰 (例如,灯泡:43.98度/秒).
- "快速培训"战略将平均RMSE降低了35%.
- "快速培训"战略使得皮尔森的平均相关性在任务之间增加了40%.
结论:
- 拟议的控制系统有效地将sEMG映射到假肢手腕运动中.
- "快速培训"战略提高了对现实世界假肢使用的适应性和稳定性.
- 这种方法提高了多DoF假肢手腕的性能和可用性.
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