对上肢假肢的sEMG分类器进行再培训的方法
Tom Donnelly1,2, Elena Seminati1,2,3, Benjamin Metcalfe1,2,4
1Centre for Accountable, Responsible, and Transparent AI (ART-AI), Department of Computer Science, University of Bath, Bath, United Kingdom.
Frontiers in neurorobotics
|October 17, 2025
概括
机器学习用于假肢控制,随着时间的推移而退化. 本研究引入了三种改造训练方法以提高准确性,信号噪声比和最近邻近方法显示了肌电假肢的最佳性能.
科学领域:
- 生物医学工程 生物医学工程
- 康复技术 康复技术 康复技术
- 机器学习在假肢方面
背景情况:
- 肌电上肢假肢的高放弃率 (44%) 对生活质量产生负面影响,并增加受伤风险.
- 在假肢中,传统的信号处理不如机器学习模式识别对运动意图的强度.
- 表面电肌图 (sEMG) 信号的非静止性和每日变化降低了机器学习分类的准确性,需要重新培训或调整.
研究的目的:
- 评估用于肌电假肢的机器学习分类器再培训的三个不同的范式.
- 为了比较信心得分的有效性,最近邻居窗口评估,以及一种新的信号噪声比 (SNR) 方法来减轻准确性损失.
主要方法:
- 该研究评估了基于机器学习的肌电假肢控制的三种再培训范式.
- 评估的范式包括信心评分,最近邻窗评估和基于SNR的新方法.
- 通过使用NinaPro 6数据集在5天内10次会话间的间歇准确度来衡量有效性.
主要成果:
- 与没有再培训相比,所有评估的再培训模式都显示出更好的准确性.
- 最近邻窗口评估和基于SNR的方法比信心评分方法的平均准确性提高了5%.
- 这些发现突出了适应性再培训策略的潜力,以提高假肢控制可靠性.
结论:
- 重新培训对于保持机器学习分类器在肌电假肢中的性能至关重要.
- 基于SNR和近邻方法的准确性比信心得分更高.
- 这些先进的再培训技术可以帮助减少假肢放弃和改善用户的结果.
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