增强的光谱-时间特征提取用于假肢控制,使用变化模式分解
Uzma Shafiq1, Asim Waris2, Faisal Amin1
1Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Scientific reports
|November 22, 2025
概括
这项研究引入了一种新的光谱-时间特征,旨在通过降低噪音敏感性来改善截肢者的康复系统. 这种新方法在区分手势方面取得了很高的准确性,提高了假肢的控制.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 康复技术 康复技术 康复技术
背景情况:
- 目前的康复系统正在与噪音敏感性和不良的手势识别斗争.
- 现有的模式识别技术对截肢者应用有限.
- 在假肢控制中,特征提取对于区分类似手势至关重要.
研究的目的:
- 为加强康复系统的实施提出一个新的光谱-时间特征集.
- 为了提高假肢控制系统的准确性和稳定性.
- 解决现有的噪声敏感技术的局限性.
主要方法:
- 利用变化模式分解 (VMD) 将电肌学 (EMG) 信号分解为变化模式函数 (VMF).
- 应用单数值分解 (SVD) 来从每个VMF中获得单数值表示.
- 将拟议的光谱-时间特征与时间域和其他光谱-时间特征集进行比较.
主要成果:
- 拟议的特征集在数据集I (健康受试者,4次手/手腕运动) 上实现了97%的准确性.
- 在数据集 (Ninapro DB2,DB3,DB9) 中表现出高概括性,准确率分别为91.43%,95.66%和98.16%.
- 确定了6个VMF作为平衡精度和执行时间的最佳数量.
结论:
- 新的光谱-时间特征集显著提高了康复系统的性能.
- 拟议的技术为健康和截肢的用户提供了更好的噪音弹性和通用性.
- 这一进步有望实现更直观,更有效的假肢控制.
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