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Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
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基于sEMG的下肢活动识别使用堆叠加加权随机森林.

Cheng Shen, Zhongcai Pei, Weihai Chen

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |December 25, 2023
    PubMed
    概括

    一个新的堆叠加加权随机森林 (SWRF) 算法改进了表面肌电图样式识别系统 (sEMG-PRS). 这提高了长期的可用性和用户适应实际应用的适应性.

    科学领域:

    • 生物医学工程 生物医学工程
    • 机器学习 机器学习
    • 信号处理 信号处理

    背景情况:

    • 基于表面肌电图的模式识别系统 (sEMG-PRS) 面临着在现实世界使用中的普遍性挑战.
    • 现有的系统在长期的可用性和适应个人用户方面扎.

    研究的目的:

    • 引入一个新的堆叠加加权随机森林 (SWRF) 算法.
    • 提高sEMG-PRS. 的可通用性,长期可用性和用户适应性.

    主要方法:

    • 开发了一个加权随机森林 (WRF),以解决标准随机森林 (RF) 的性能不平衡.
    • 采用堆叠,使用RF作为基础学习者和WRF作为meta-learner,创建SWRF算法.
    • 使用离线数据集和在线实验对RF,WRF,LSTM和SVM进行SWRF评估.

    主要成果:

    • 在线分类的平均准确率为89.06%,超过了RF,WRF,LSTM和SVM.
    • 在线实验表明,与其他算法相比,SWRF在长期可用性和用户适应性方面的表现优越.

    结论:

    • 拟议的SWRF算法显著提高了sEMG-PRS的性能.

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  • 在实际应用中,SWRF显示出强大的潜力,需要强大且可适应的人机接口.