一次性随机森林模型校准,用于解码手势手势
Xinyu Jiang1, Chenfei Ma1, Kianoush Nazarpour1
1School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom.
Journal of neural engineering
|January 16, 2024
概括
本研究引入了一种新的,无源方法,用于校准预先训练的随机森林模型,以使用最小的用户数据进行肌电控制. 该方法显著提高了电肌图信号模式识别的准确性和稳定性.
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:
- 现有的机器学习模型对肌电控制要求广泛的用户特定的电肌图 (EMG) 数据进行有效的校准.
- 这种数据要求对肌电设备的新用户构成重大负担.
- 对于肌电控制系统,需要高效,低数据的校准方法.
研究的目的:
- 开发一种新的方法来校准预训练的机器学习模型,使用来自新myoelectric用户的最小数据.
- 为了使肌电控制模型能够高效准确地适应特定用户的需求.
主要方法:
- 一个随机森林 (RF) 模型最初被训练在来自20个人执行各种手握的EMG数据上.
- 对于新用户,预先训练的决策树使用有限的验证数据进行了修剪.
- 仅在新用户的数据上训练的新决策树被添加到削减模型中.
主要成果:
- 在两天内与18名参与者进行的实时实验表明,拟议的方法在准确性方面明显优于基准用户特定的射频和线性差异分析模型.
- 在第一天校准的射频模型在第二天与基准相比保持了显著更高的准确性,证明了稳定性.
- 校准程序是无源的,在初始模型预训练后不需要访问原始训练数据.
结论:
- 拟议的无源模型校准方法有效地降低了新的myoelectric用户的数据负担.
- 这种方法提高了肌电控制系统的准确性和稳定性.
- 该研究倡导在肌电控制应用中使用高效,可解释和简单的模型.
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