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Assessment and Communication for People with Disorders of Consciousness
Published on: August 1, 2017
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随机通道消去用于强大的手势分类与多模式生物信号.
概括
随机通道切除 (RChA) 提高了使用生物信号的手势分类. 这种方法提高了对多式联络传感中缺失数据通道的稳定性,优于基线和归算技术.
科学领域:
- 生物医学工程 生物医学工程
- 人与计算机的交互
- 机器学习 机器学习
背景情况:
- 基于生物信号的手势分类对于人机交互至关重要.
- 多式联络生物信号传感通常会因为缺失的通道而丢失数据,从而影响分类性能.
- 为不完整的生物信号数据开发强大的分类器是一个重大挑战.
研究的目的:
- 提出和评估一种新的方法,即随机通道切除 (RChA),以提高手势分类器对缺失数据通道的稳定性.
- 评估RChA在使用超声波和力肌图 (FMG) 的多模式生物信号分类中的有效性.
主要方法:
- 从前臂获取的多模式生物信号数据 (超声波和FMG) 来自2个受试者的12个手势.
- 在训练卷积神经网络架构时实施随机通道消去 (RChA).
- 使用5倍交叉验证对基线,归算和预言方法进行RChA比较.
主要成果:
- 与基线方法相比,RChA在手势分类准确度方面表现出显著的改进.
- 观察到平均改善12.2%和24.5%,最多缺少4个和8个通道,分别.
- 与其他评估方法相比,拟议的RChA方法在增加缺失通道数量方面表现出优越的稳定性.
结论:
- 随机通道消去 (RChA) 是一种有效的策略,用于提高基于多模式,多通道生物信号的手势分类分类器的稳定性.
- RChA提供了一种有希望的方法来减轻现实世界生物信号采集系统中数据丢失的不利影响.
- 这些发现突出了RChA在提高依赖生物信号解释的人机界面可靠性的潜力.
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