用跨人差异的肌电信号方差，少标数据也能准识动作
Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference
- 基于贝叶斯框架，迁移多人的肌电方差信息
- 仅需少量目标个体数据，识别准确率显著提升
- 适合标注成本高的实时运动识别场景
在基于肌电（EMG）的动作识别中，通常需为每个受试者收集大量标注数据训练专属分类器，但此过程耗时耗力。本文提出一种基于贝叶斯推断的跨人方差迁移学习方法。该方法假设：尽管不同受试者的肌电特征均值差异大，但其方差模式可能相似。通过在多个源受试者上预训练获取方差信息，并在贝叶斯更新框架中迁移到目标受试者，实现仅用少量目标校准数据即可获得高精度分类。研究引入调节系数控制迁移信息量，优化学习效率。在两个公开肌电数据集上的实验验证了该方法的有效性，性能优于现有主流方法。
原文摘要 · Abstract (English)
In electromyogram (EMG)-based motion recognition, a subject-specific classifier is typically trained with sufficient labeled data. However, this process demands extensive data collection over extended periods, burdening the subject. To address this, utilizing information from pre-training on multiple subjects for the training of the target subject could be beneficial. This paper proposes an inter-subject variance transfer learning method based on a Bayesian approach. This method is founded on the simple hypothesis that while the means of EMG features vary greatly across subjects, their variances may exhibit similar patterns. Our approach transfers variance information, acquired through pre-training on multiple source subjects, to a target subject within a Bayesian updating framework, thereby allowing accurate classification using limited target calibration data. A coefficient was also introduced to adjust the amount of information transferred for efficient transfer learning. Experimental evaluations using two EMG datasets demonstrated the effectiveness of our variance transfer strategy and its superiority compared to existing methods.
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