对振动声学传感特征的集群方法作为机器人辅助干预中组织特征的潜在方法
Robin Urrutia1,2, Diego Espejo2, Natalia Evens3
1Instituto de Acústica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5111187, Chile.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
使用Cepstrum变换 (CT) 和Mel-Frequency Cepstral系数 (MFCCs) 来从振动声信号 (VA信号) 中提取特征,并减少维度,显著改善了机器人辅助干预分析. CT-UMAP组合实现了近99%的准确性.
科学领域:
- 机器人和自动化机器人与自动化
- 生物医学信号处理
- 手术技术 手术技术
背景情况:
- 振动声学 (VA) 信号提供了机器人辅助干预期间组织相互作用动态的见解.
- 有效的特征提取对于解释复杂的VA信号和理解组织行为至关重要.
- 之前的研究强调了在这个领域需要先进的信号处理技术.
研究的目的:
- 在机器人辅助干预中全面分析VA信号的特征提取方法.
- 评估不同转换和缩小维度技术的有效性.
- 通过改进VA信号分析,增强对组织行为的理解.
主要方法:
- 使用Cepstrum变换 (CT),Mel频率Cepstral系数 (MFCC) 和快速Chirplet变换 (FCT) 的特征提取.
- 通过主要组件分析 (PCA),t分布式随机邻方嵌入 (t-SNE) 和统一多重近似和投影 (UMAP) 来减少维度.
- 使用最近邻居分类器进行分类.
主要成果:
- 特征提取,特别是CT和MFCC与尺寸缩小相结合,证明了高效率.
- 分类指标 (准确性,回忆,F1分) 达到了大约99%.
- 结合单元多重近似和投影 (UMAP),Cepstrum转换 (CT) 显示出卓越的性能.
结论:
- 特征提取技术,特别是CT和MFCC,在机器人外科手术中对VA信号分析非常有效.
- 缩小尺寸的算法显著提高了VA信号分类的性能.
- CT-UMAP组合是一种有前途的方法,可以在干预过程中对组织行为进行可靠的分析.
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