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相关实验视频

Updated: Jul 23, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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高性能表面电肌图学腕带设计用于手势识别

Ruihao Zhang1, Yingping Hong1, Huixin Zhang1

  • 1School of Instrument and Electronics, North University of China, Taiyuan 030051, China.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

本研究介绍了用于表面电肌图 (sEMG) 信号采集的高性能α腕带. 这款先进的可穿戴设备在识别10种手势方面达到98.6%的准确性,展示了其在医疗应用中的实际潜力.

科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习 机器学习
  • 可穿戴技术可穿戴技术

背景情况:

  • 可穿戴表面电肌图 (sEMG) 设备为医疗应用提供了潜力,包括通过机器学习识别意图.
  • 当前的商业sEMG腕带往往表现出有限的性能和识别能力,阻碍了它们的广泛采用.
  • 需要先进的sEMG采集设备,提供高可靠性数据和强大的性能,以准确解释人类意图.

研究的目的:

  • 设计和推出一个无线,高性能的sEMG腕带 (α腕带),具有增强的信号采集功能.
  • 评估开发的α腕带在捕获机器学习应用程序的详细sEMG数据方面的性能.
  • 为了证明α腕带对准确的手势识别的实用性和稳定性.

主要方法:

  • 开发了一种16通道无线sEMG腕带 (α腕带),具有16位ADC,可调节的采样速率高达2000Hz/通道,可调节的带宽 (0.1-20kHz).
  • 使用低功耗蓝牙进行参数配置和sEMG数据交互.
  • 从30名受试者的前臂收集了sEMG数据,将时间频域特征处理成图像样本,并训练了卷积神经网络 (CNN).

主要成果:

  • α腕带成功获取了具有可配置参数的高分辨率sEMG数据.
  • 在时间频域图像样本上训练的卷积神经网络在10种不同的手势中获得了98.6%的平均识别精度.
关键词:
收购系统 收购系统卷积神经网络 (CNN) 是一种神经网络.这是手势识别,是手势识别.表面电肌图 (sEMG) 信号信号穿戴式设备是一种可穿戴的设备.

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  • 高精度表明α腕带在捕捉手势解释至关重要的微妙sEMG变化的有效性.

    • 结论:

    • 开发的α Armband是一种实用且强大的高性能sEMG采集设备.
    • 在医学和辅助技术中,α腕带显示了推进基于机器学习的人类意图识别的巨大潜力.
    • 进一步开发α腕带可能会带来改进的人机界面和个性化医疗保健解决方案.