通过一种自适应的增量分类方法克服电肌图信号中的行为变异性
Hiba Hellara1, Oumayma Kahouli1, Sawsan Njeh1
1Professorship of Measurement and Sensor Technology, Technische Universität Chemnitz, Reichenhainer Str 70, Chemnitz, 09126, Saxony, Germany.
Computational and structural biotechnology journal
|February 2, 2026
概括
一个新的自适应算法 (ADINC-kNN) 改善了表面肌电图的手势识别,用于各种人群,包括吸烟者和酒精消费者,而不需要完全重新培训.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 基于表面电肌图 (sEMG) 的手势识别系统面临着具有生理变异的性能问题.
- 吸烟和饮酒等生活方式因素会导致分布变化,降低模型的准确性.
研究的目的:
- 引入一个适应式增量k-最近邻居 (ADINC-kNN) 算法,用于强大的sEMG手势识别.
- 为了使动态适应特定人群的生理变异,而无需完全重新训练模型.
主要方法:
- 实现了一个自适应增量k-最近邻近 (ADINC-kNN) 算法.
- 使用滑动窗口缓冲和距离加权投票来精确动态决策边界.
- 在15个手力强度练习中对14个受试者进行了评估,使用5倍交叉验证.
主要成果:
- 在准确性,精度,回忆和F1分数方面,ADINC-kNN显著超过静态kNN.
- 在吸烟和饮酒群体中获得了90%以上的分类性能.
- 与基于再培训的方法相比,在计算效率和预测准确性之间取得了更好的平衡.
结论:
- ADINC-kNN为强大的sEMG手势识别提供了一个可扩展和实用的解决方案.
- 该算法有效地适应各种用户群体和不断变化的生理条件.
- 适用于康复,辅助技术和人机交互等现实世界的应用.
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