用转移学习将上肢力量建模泛化:一种使用EMG和IMU用于新用户和条件的多模式方法
概括
转移学习 (TL) 能够为新用户提供精确的基于EMG的力量建模,使用最小的数据. 与传统方法相比,这种方法显著提高了模型性能,提高了在辅助和机器人应用中的采用.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 康复技术 康复技术 康复技术
背景情况:
- 基于电肌图 (EMG) 的力量建模对于辅助,机器人和康复设备至关重要.
- 目前的模型主要关注主体内部的性能,对最终用户数据采集造成负担.
- 对个体进行模型的概括是广泛采用的关键,但仍然是一个重大挑战.
研究的目的:
- 调查转移学习 (TL) 的有效性,以将基于EMG的力模型推广到新用户.
- 通过适应具有最小新数据的模型,减少最终用户的数据采集负担.
- 根据LOSO和主体内部建模场景评估TL性能.
主要方法:
- 开发了一个深度多式联络卷积神经网络 (CNN),集成高密度 (HD) EMG和惯性测量单元 (IMU) 运动数据.
- 采用了TL策略:使用现有用户数据建立基线模型,然后对10%,20%和40%的新用户数据进行微调.
- 在同位素,同位素动力和动态条件下测试模型.
主要成果:
- TL显著提高了力模型的准确性,比LOSO的平均R平方值增加了60.81%,达到199.79%.
- 在不同条件下,TL的表现超过了对象内部建模的13.4%至45.51%.
- 对新用户的新实验条件进行了成功的概括.
结论:
- 转移学习使基于EMG的力量建模能够有效地适应新用户,并大幅减少数据需求.
- 拟议的多式联络TL方法与传统的主体内和LOSO方法相比,提供了更高的性能.
- TL促进了更具适应性和用户友好的EMG控制的辅助和机器人系统的开发.
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