一个快速和低影响的嵌入式定向校正算法用于手势识别手
Andrea Mongardi1, Fabio Rossi1, Andrea Prestia1
1Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究引入了一种新的算法,用于纠正手势识别的表面电微图 (sEMG) 腕带系统中的传感器位移. 该方法确保了高精度,无论腕带的方向如何,改进了可穿戴的人机界面 (HMI).
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:
- 表面电微图 (sEMG) 对于可穿戴的人机接口 (HMI) 在手势识别方面至关重要.
- 传感器的放置显著影响基于sEMG的系统的性能,对可靠的HMI应用构成挑战.
研究的目的:
- 为基于sEMG的HMI腕带开发一种快速,低冲击的定向校正算法,以解决传感器位移问题.
- 为了提高手势识别系统的稳定性和准确性,独立于腕带方向.
主要方法:
- 引入了一种新的算法,其中包括一个校准阶段,用于腕带方向估计和实时数据校正.
- 该算法只需要两个不同的手势来激活sEMG,确保硬件和数据库的独立性.
- 在使用人工神经网络 (ANN) 实现九个手势识别的七通道sEMG腕带系统中实现了算法.
主要成果:
- 实现了平均预测准确率为93.36%,即使随意的腕带穿着方向.
- 证明对功耗 (额外500μW) 和延迟 (增加408μs) 的影响最小.
- 验证了算法的有效性,一般适用性和纠正电极转移问题的效率.
结论:
- 开发的定向校正算法为提高基于sEMG的HMI的可靠性提供了一个有希望的解决方案.
- 该算法的效率和与再培训模型的独立性使其非常适合于实际的HMI应用.
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