基于线性回归和闭环训练协议的同时三度自由度假肢控制
1Instituto de Telecomunicaciones y Aplicaciones Multimedia (ITEAM), Universitat Politècnica de València, 46022 Valencia, Spain.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究引入了一种改进的机器学习控制器,用于使用电肌图 (EMG) 信号进行假肢控制. 一种新的闭环训练方法提高了三种假肢手动的同时控制,大大提高了性能.
科学领域:
- 生物医学工程 生物医学工程
- 康复机器人 康复机器人
- 医疗保健中的机器学习
背景情况:
- 使用电肌图 (EMG) 信号的假肢机器学习控制器越来越受欢迎.
- 基于回归的控制提供了比离散分类方法更自然,更比例的运动.
- 现有的回归控制器很少同时管理超过两个自由度.
研究的目的:
- 在假肢手中应用适应性线性回归器,同时对三个自由度 (DoF) 的比例控制.
- 调查培训模式对控制器性能的影响.
- 引入一个闭环培训程序,以改善EMG信号生成和控制器学习.
主要方法:
- 采用了一个具有八个EMG传感器的自适应线性回归器,用于低维特征空间.
- 实施了一种新的闭环培训程序,用户可以积极提高EMG信号质量.
- 在10名健康和3名肢体缺陷的受试者身上测试了该系统,同时控制左右,上下和打开关闭的手动.
主要成果:
- 拟议的闭环培训程序显著提高了控制器的性能.
- 同时控制三个DoF的平均完成率从53%上升到65%.
- 多维目标和培训协议的结合是提高绩效的关键.
结论:
- 闭环训练模式对于优化基于机器学习的假肢EMG控制器至关重要.
- 通过自适应线性回归和改进的训练,可以有效地同时控制多个DoF.
- 这种方法有望实现更自然和直观的假肢控制.
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