实现个性化的人在循环训练:使用双速率模型实时估计个体运动学习动态
Arash Salemi1, Amirhossein Afkhami Ardekani1, Albert H Vette2
1Department of Mechanical Engineering, University of Alberta, Donadeo Innovation Centre for Engineering, Edmonton, Alberta, T6G 1H9, Canada.
Computers in biology and medicine
|October 10, 2025
概括
我们开发了新的在线方法,联合扩展卡尔曼波器 (JEKF) 和移动地平线估计 (MHE),以实时模拟个人运动学习. 这些方法准确地捕捉了个性化的学习和忘记率,超过了传统的离线方法.
科学领域:
- 运动控制和学习.
- 计算神经科学是一种计算神经科学.
- 系统识别 系统识别
背景情况:
- 双速模型描述了运动学习的快速和慢速过程.
- 现有的双率模型估计方法是离线的,忽视了个体差异.
- 准确的,实时建模个体运动学习仍然是一个挑战.
研究的目的:
- 开发和验证实时双率模型参数估计的在线系统识别方法.
- 为了解释运动学习和忘记率的个体差异.
- 为了比较联合扩展卡尔曼波器 (JEKF) 和移动地平线估计 (MHE) 在不同条件下的性能.
主要方法:
- 在线开发联合扩展卡尔曼波器 (JEKF) 和移动地平线估计 (MHE) 方法.
- 引入了JEKF和MHE的自适应版本,以适应电机输出噪声.
- 使用蒙特卡洛模拟和视觉运动适应实验验验证的方法,使用块,交替和随机时间表.
主要成果:
- JEKF和MHE准确地实时估计了双利率模型参数,平均误差低于11%.
- 这两种方法都捕获了任务转换和时间表变化期间学习和忘记率的动态变化.
- 在不可预测的环境中,MHE提供了比JEKF更可靠的估计,参数估计误差高达26%低.
结论:
- JEKF和MHE提供了有效的实时,个性化的运动学习建模.
- 这些框架可以跟踪不断发展的学习和忘记动态,优于线下方法.
- 提出的方法有可能用于机器学习应用中的在线决策.
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