由上肢动力学驱动的肌肉活动生成的概念
Marie D Schmidt1,2, Tobias Glasmachers3, Ioannis Iossifidis4
1Faculty of Electrical Engineering and Information Technology, Ruhr-University Bochum, Bochum, Germany. Marie.Schmidt@ruhr-uni-bochum.de.
Biomedical engineering online
|June 24, 2023
概括
这项研究使用循环神经网络从运动参数预测上肢肌肉活动,实现已知和新运动的高精度. 这种方法对了解运动控制和通过功能电刺激帮助康复有希望.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 灵感来自于中枢神经系统的运动规划,这项工作基于关节参数模型人造肌肉活动.
- 假设以前的经验和感官输入被代表为大脑中预先学习的反向动态.
研究的目的:
- 用运动参数来演示人工肌肉活动的产生.
- 开发一个模型,预测复杂的3D人手臂运动的肌肉活动.
- 调查这种模型在不同个体中的通用性.
主要方法:
- 经常性神经网络 (RNN) 被用来预测上肢肌肉活动.
- 输入特征包括关节角度,速度,加速度和手的位置/方向.
- 模型在多个主题上接受了培训,评估了一般,专题和转移学习方法.
主要成果:
- RNN方法在预测新型运动的肌肉活动方面取得了高精度.
- 与其他架构相比,该模型表现出卓越的性能.
- 转移学习有效地解决了肌肉活动中各个主体之间的差异.
结论:
- 预测模型增强了对人类运动控制的理解.
- 潜在的应用包括用于康复的指导功能电刺激.
- 这种方法可以为治疗策略和辅助设备的开发提供信息.
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