多模式最佳匹配和增强方法用于小样本的手势识别
Wenli Zhang1, Bo Liu1, Tingsong Zhao1
1Faculty of Information Science and Technology, Beijing University of Technology, Beijing, China.
Bioscience trends
|January 26, 2025
概括
这项研究引入了一种使用表面电肌图 (sEMG) 和运动数据进行手势识别的新方法. 它显著减少了对特定用户数据收集的需求,提高了所有用户的效率,特别是那些有健康状况的用户.
科学领域:
- 人与计算机的交互
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 使用生理信号的手势识别提供了自然的互动,但需要广泛的用户特定数据.
- 目前的转移学习等方法面临负转移和有限的数据多样性挑战.
- 对于基于sEMG的模型来说,从不健康的用户收集数据是特别繁的.
研究的目的:
- 开发一种高效的多式联络方法,用于使用表面电肌图 (sEMG) 和运动信息进行小样本的手势识别.
- 减少数据采集对用户的负担,特别是那些有身体限制的用户.
- 提高手势识别深度学习模型培训样本的准确性和多样性.
主要方法:
- 提出了一种多模式的最佳匹配和增强方法,将运动信息与sEMG信号集成在一起.
- 利用一个最佳匹配信号选择模块来最大限度地减少新用户的域间差异.
- 实施了相似性计算增强模块,以增加训练集多样性和模式类型嵌入,以增强信息交互.
主要成果:
- 在自我收集的中风患者数据集上,达到93.69%的高精度,Ninapro DB1的91.65%和Ninapro DB5.5的98.56%.
- 证明有效的手势识别,每个手势只需要一个数据采集.
- 展示了与传统模型可比的性能,同时显著降低了数据收集要求.
结论:
- 拟议的多式联络方法显著提高了使用sEMG和运动数据的小样本手势识别.
- 这种方法为高效准确的手势识别提供了一种实用解决方案,特别有利于非健康用户.
- 这些发现为更容易访问和不那么数据密集的人与计算机交互系统铺平了道路.
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