关于肌肉信号的分布:人类手势的基于距离的分类方法
Jonas Große Sundrup1, Katja Mombaur1,2
1Canada Excellence Research Chair Human-Centred Robotics and Machine Intelligence, Systems Design Engineering & Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究引入了一个k-Nearest-Neighbors分类器,使用动态时间扭曲用于人类手势识别. 它评估了手势的分离性,并为实时应用提出了计算效率高的方法.
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:
- 精确识别人类手势对于先进的界面至关重要.
- 动态时间扭曲 (DTW) 对时间序列数据有效,但计算密集.
- 了解肌肉特征分布是强大的手势分类的关键.
研究的目的:
- 用DTW研究肌肉特征的分布,用于手势识别.
- 开发和评估一个k-Nearest-Neighbors分类器与DTW用于手势分类.
- 评估手势分离性,并为实时应用提出计算效率高的方法.
主要方法:
- 利用动态时间变形 (DTW) 在k-Nearest-Neighbors分类器中进行距离估计.
- 分析了记录的肌肉特征样本的分布.
- 开发了一种方法来评估不同手势的分离性.
- 介绍并评估了两种用于降低实时计算成本的新方法.
主要成果:
- 建立了一个分析手势肌肉特征分布的框架.
- 基于DTW距离的量化手势分离能力.
- 证明了拟议的分类器及其参数调整对可用性和背景拒绝的有效性.
- 验证了两个计算优化的实时手势识别方法.
结论:
- 带有DTW的k-Nearest-Neighbors分类器有效地根据肌肉签名识别人类手势.
- 可以系统地评估手势分离性,从而实现分类器优化.
- 降低计算成本的方法对实际的实时应用有希望.
- 参数调整对于提高手势分类系统中的可用性和背景拒绝至关重要.
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