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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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卷积特征工程用于跨日个人识别,使用手腕肌电图.

Ashirbad Pradhan, Ning Jiang, Seoyeon Woo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    表面电肌图 (EMG) 提供生物识别,但与日常变化作斗争. 一种新的深度学习方法,MyoBM-Net,使用卷积层进行强大的特征提取,在跨日识别中达到98.5%的准确性.

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    科学领域:

    • 生物识别信息 生物识别信息
    • 人工智能的人工智能
    • 信号处理 信号处理

    背景情况:

    • 表面电肌图 (EMG) 是一种可行的生物识别工具,用于识别个人.
    • 多天的性能退化是EMG生物识别的一个关键挑战.
    • 深度学习为生物信号分析提供先进的特征提取.

    研究的目的:

    • 推出MyoBM-Net,这是一个新的卷积神经网络,用于基于EMG的个人识别.
    • 通过使用深度特征提取来评估EMG生物识别的跨日稳定性.
    • 将MyoBM-Net与传统的特征提取方法进行比较.

    主要方法:

    • 利用1D和2D卷积层从EMG信号中提取空间和通道特定的特征.
    • 在一个月的三个单独日子里,从43名参与者的手腕EMG数据中获取数据.
    • 在不同的日子进行跨日识别分析,培训和测试.

    主要成果:

    • 在跨日识别中,MyoBM-Net实现了98.5%的中位数排名-1准确度.
    • 拟议的方法优于传统的特征提取技术.
    • 较低的基于特色的指数 (DBI) 记录为1.73,表明强大的歧视力.

    结论:

    • MyoBM-Net显示了基于EMG的强大和准确的个人身份识别的巨大潜力,即使在不同的日子里也是如此.
    • 深度学习方法有效地解决了在多天生物识别系统中性能变化的挑战.
    • 该方法能够提取空间和通道特定信息,从而提高其适用于实际生物识别应用的适用性.