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通过机械图信号进行手势识别:手臂姿势变化适应性框架.

Panipat Wattanasiri, Samuel Wilson, Weiguang Huo

    IEEE journal of biomedical and health informatics
    |October 28, 2024
    PubMed
    概括

    这项研究引入了一种新的机械图 (MMG) 装置用于手势识别,克服了无监督域适应的手臂姿势挑战. 该系统实现了高精度,为传统电肌图 (EMG) 方法提供了有希望的替代方案.

    科学领域:

    • 生物医学工程 生物医学工程
    • 人与计算机的交互
    • 信号处理 信号处理

    背景情况:

    • 手的手势识别对于人与计算机的互动至关重要.
    • 由于动态肌肉活动,在不同的手臂姿势中对手势进行分类具有重大挑战.
    • 现有的方法通常依赖于电肌图 (EMG) 传感器,需要与皮肤接触.

    研究的目的:

    • 开发一个强大的手势识别系统,解决手臂姿势的变化.
    • 使用可穿戴机械肌图 (MMG) 装置,消除了与皮肤电气接触的需要.
    • 评估无监督域适应的有效性,以改善不同姿势的手势分类准确性.

    主要方法:

    • 使用可穿戴机械肌图 (MMG) 装置来检测肌肉活动.
    • 使用连续波形变换 (CWT) 来从MMG信号中提取特征.
    • 实现的域-对立的卷积神经网络 (DACNN),用于手势分类的无监督域适应.
    • 在多个手臂姿势中,与监督分类器进行DACNN性能比较.

    主要成果:

    • 与监督方法相比,拟议的DACNN方法在各种手臂姿势中显示了对分类准确性的持续改善.
    • 在5个手势的内部姿势中获得了87.43%的平均预测准确度,在5个手势之间的姿势分类中达到64.29%的平均预测准确度.

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  • 将MMG细分窗口扩展到600ms,将姿势内准确度提高到92.32%,姿势间准确度提高到71.75%.

    • 结论:

    • 开发的方法有效地改善了动态手臂姿势变化中的手势识别概括.
    • 机械肌图 (MMG) 显示了作为一种可行的替代感应器的潜力,用于手势识别,与电肌图 (EMG) 相比.
    • 该系统适合非实验室使用,提供用户友好的设置和高性能.