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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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改变决策的知情拒绝提高了基于模式识别的肌电控制的稳定性.

Shriram Tallam Puranam Raghu, Dawn MacIsaac, Erik Scheme

    IEEE journal of biomedical and health informatics
    |September 18, 2023
    PubMed
    概括

    这项研究比较了肌电控制后加工技术. 一种新的方法,决策变更知情拒绝 (DCIR),在动态转换过程中提高了准确性和稳定性,优于现有的方案.

    科学领域:

    • 生物医学工程 生物医学工程
    • 康复工程 康复工程
    • 机器学习用于假肢.

    背景情况:

    • 后处理技术增强了基于模式识别的肌电控制决策流.
    • 现有的方法通常在静态数据上单独评估,限制了对动态性能权衡的理解.
    • 在动态场景中评估平滑与延迟对于现实应用至关重要.

    研究的目的:

    • 调查和比较八个已建立的肌电控制后处理方案.
    • 引入和评估两种新的,有时意识的后处理方法.
    • 评估这些技术在连续和动态类过渡期间的性能.

    主要方法:

    • 八种后处理技术的比较:多数投票,贝叶斯融合,发病锁定,异常值检测,基于信任的拒绝,信任缩放,先行调整和自适应窗口.
    • 开发和实施两个新的临时意识计划:决策变化知情拒绝 (DCIR) 和相关方法.
    • 使用常规和深度分类器对具有连续类过渡的动态数据集进行评估.

    主要成果:

    • 拟议的决策变更知情拒绝 (DCIR) 方法在与现有方案相比,表现优越.
    • 在稳定状态条件和动态过渡期间,DCIR减少了错误率和决策流波动性.
    • 在传统和深度分类器类型中,DCIR的有效性是一致的.

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    结论:

    • 在后处理中利用时间上下文显著提高了肌电控制系统的稳定性.
    • 时间意识的方法,如DCIR,通过在动态使用期间更好地拒绝不确定的决策,提供更好的性能.
    • 这些发现表明了开发更可靠,更响应的肌电控制接口的新方向.