通过VICReg进行自我监督学习,可以训练使用不清楚标签的连续数据进行EMG模式识别
Shriram Tallam Puranam Raghu1, Dawn T MacIsaac1, Erik J Scheme1
1Department of Electrical and Computer Engineering and the Institute of Biomedical Engineering, University of New Brunswick, Fredericton, E3B 5A3, NB, Canada.
Computers in biology and medicine
|December 5, 2024
概括
使用预训练的长短期记忆 (LSTM) 网络进行自我监督学习,可以增强表面电肌图案识别 (sEMG-PR) 模型. 使用带有过渡的动态数据,这种方法显著优于传统方法,特别是VICReg预训练模型.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 表面电肌图案识别 (sEMG-PR) 模型对于人机接口至关重要.
- 使用动态数据训练sEMG-PR模型,包括肌肉激活模式之间的过渡,是具有挑战性的,因为在这些过渡期间缺乏基本真相标签.
- 自主监督学习为克服动态sEMG数据中的标签限制提供了一个潜在的解决方案.
研究的目的:
- 用预训练的长期短期记忆 (LSTM) 网络进行sEMG-PR模型训练,研究自我监督学习的有效性.
- 将LSTM模型 (完全监督与自我监督) 的性能与传统的线性差异分析 (LDA) 模型进行比较.
- 为了评估模型的性能,对分段的道数据和连续的动态数据与类过渡进行评估.
主要方法:
- 雇员预训练的长期短期记忆 (LSTM) 网络使用自主监督学习 (VICReg) 进行sEMG-PR.
- 与线性差异分析 (LDA) 模型相比,LSTM模型 (经过自我监督和完全监督损失的训练) 进行了比较.
- 使用了两个数据集:细分的坡道数据 (没有过渡) 和连续的动态数据 (有过渡).
主要成果:
- 在连续动态数据上训练时,时间模型 (LSTM) 的表现优于非时间模型 (LDA).
- 使用连续动态数据进行VICReg预训练的LSTM模型,与所有其他测试模型相比,表现优越.
- 仅在道数据上训练的LSTM的表现比LDA差,表明没有足够的动态信息的潜在超装.
结论:
- 培训数据中的代表性动态对于有效的sEMG-PR模型开发至关重要.
- 自主监督学习方法,特别是对动态数据进行预训练的LSTM,可以显著提高sEMG-PR模型的性能.
- 模型架构和数据类型的选择显著影响sEMG-PR的结果.
关键词:
深度时间学习 (deep temporal learning) 是一种深度时间学习.我的电动控制器是我的电动控制器自主监督学习学习表面电肌图形识别模式的识别.过渡 过渡 过渡 过渡维克雷格 (VICReg) 是一个维克雷格.更多相关视频
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