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在处理电肌图信号的模式识别在波兰手语的选择表达方式的电肌图信号
Anna Filipowska1, Wojciech Filipowski2, Julia Mieszczanin1
1Department of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
卷积神经网络 (CNN) 在从电肌图 (EMG) 信号分类手势方面表现出高准确性. 这项研究表明,负担得起的EMG传感器对于有效的手势识别,包括波兰手语的潜力.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 姿态识别对于人机交互至关重要,尤其是在无法发言的情况下.
- 生物医学传感和机器学习方面的进步,如电肌图 (EMG) 和卷积神经网络 (CNN),使手语解释成为可能.
- 基于EMG的手势识别为开发直观控制系统提供了一条途径.
研究的目的:
- 为了比较各种机器学习算法的性能,特别是CNN,用于分类游戏控制和波兰手语手势.
- 评估两个不同的EMG数据采集系统 (BIOPAC MP36和MyoWare 2.0) 的有效性.
- 评估使用低成本EMG传感器用于手势分类的可行性.
主要方法:
- 使用BIOPAC MP36和MyoWare 2.0系统记录了24种不同的手势的EMG信号.
- 应用并比较多个机器学习算法,专注于CNN,用于手势分类.
- 使用新创建的EMG信号数据集进行分析.
主要成果:
- 与其他分类器 (≤7.8571%和≤10.2697%,分别) 相比,CNN的准确性明显更高 (BIOPAC为98.324%,MyoWare为95.5307%).
- 证明CNN在解释EMG数据中的复杂手势模式方面非常有效.
- 在使用CNN时,显示了高端BIOPAC系统和更实惠的MyoWare传感器之间的可比性能.
结论:
- CNNs为基于EMG的手势识别提供了强大而准确的方法.
- 价格实惠的EMG传感器,如MyoWare,可用于有效的手势分类,使技术更容易获得.
- 这些发现支持将具有成本效益的EMG技术集成到现实世界的手势识别应用程序中,包括波兰手语口译.
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