通过双视角不一致性学习实现开放式的肌电手势识别
Chen Liu1, Can Han1, Chengfeng Zhou1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Computer methods and programs in biomedicine
|January 23, 2026
概括
这项研究介绍了PredIN,一种新的方法,用于使用表面电肌图 (sEMG) 进行开放式的手势识别. PredIN有效地区分了已知的手势和未知的手势,改进了人机交互系统.
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:
- 表面电肌图 (sEMG) 对于人机交互 (HMI) 至关重要,特别是在假肢和康复方面.
- 当前的sEMG手势识别与开放式场景作斗争,无法拒绝未知的手势.
- 强大的开放式识别对于实际的HMI应用至关重要.
研究的目的:
- 开发一个有效的开放式的手势识别系统用于sEMG.
- 解决区分已知的手势和未知的手势的挑战.
- 为了提高肌电手势识别的性能.
主要方法:
- 发现未知类的预测不一致性是开放集识别的关键因素.
- 提出PredIN,一种双视角不一致性学习方法.
- 通过增强跨视角的特征分布差异,同时优化视角内可分离性,扩大了预测不一致性.
主要成果:
- 在四个公共sEMG数据集上评估了PredIN.
- 证明PredIN显著优于现有的最先进的方法.
- 在开放式的手势识别任务中取得了卓越的性能.
结论:
- 拟议的PredIN方法使得准确的封闭式手势分类成为可能.
- PredIN有效地拒绝未知的手势,在开放场景中展示其优越性.
- 这种方法提高了基于sEMG的HMI的可靠性和适用性.
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