用流式学习检测肌电信号域偏移，提升假肢控制稳定性
Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning
- 基于时序片段定义域，用余弦核KPCA提取特征
- 多方法对比显示现有技术实时检测性能不足
- 适合神经假肢、生物信号流处理研究者参考
肌电信号的非平稳性使得检测其域偏移成为重大挑战。本文利用数据流学习技术，以Ninapro数据库中的DB6数据集为例，将不同受试者和记录会话定义为不同域，采用余弦核核主成分分析（KPCA）进行预处理以凸显域偏移。通过评估CUSUM、Page-Hinckley和ADWIN等漂移检测方法，发现现有技术在实时检测肌电信号域偏移方面性能有限。结果表明，流式学习方法具有维持稳定肌电解码模型的潜力，但需进一步研究以提升实际应用中的鲁棒性和准确性。
原文摘要 · Abstract (English)
Detecting domain shifts in myoelectric activations poses a significant challenge due to the inherent non-stationarity of electromyography (EMG) signals. This paper explores the detection of domain shifts using data stream (DS) learning techniques, focusing on the DB6 dataset from the Ninapro database. We define domains as distinct time-series segments based on different subjects and recording sessions, applying Kernel Principal Component Analysis (KPCA) with a cosine kernel to pre-process and highlight these shifts. By evaluating multiple drift detection methods such as CUSUM, Page-Hinckley, and ADWIN, we reveal the limitations of current techniques in achieving high performance for real-time domain shift detection in EMG signals. Our results underscore the potential of streaming-based approaches for maintaining stable EMG decoding models, while highlighting areas for further research to enhance robustness and accuracy in real-world scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。