通过模块化测试方法在外骨架模型上评估关节角度估计算法的性能
Ryan S Pollard1, Sarah M Bass1, Mark C Schall2
1Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究测试了两种关节角度估计模型,用于只使用一个传感器的脚外骨架. 与动力模型相比,随机森林模型显示出较低的误差和更快的启动时间.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物力学 生物力学
- 机器学习 机器学习
背景情况:
- 外骨控制需要准确的操作者意图检测,以实现无执行.
- 关节角度估计算法通常使用多个传感器,但单传感器方法的探索较少.
- 操作人员的意图对于外部骨中有效的人机系统集成至关重要.
研究的目的:
- 为了评估动力推断算法和随机森林机器学习算法的性能,仅使用单个传感器数据进行关节角度估计.
- 评估模块化测试方法对外骨模型评估的可行性.
- 为了比较两个不同的联合角度估计模型的精度和执行时间.
主要方法:
- 实施了模块化测试方法来评估外骨模型.
- 测试了两种联合角度估计模型,一种动力抽象算法和一种随机森林算法.
- 每个模型的信息完全来自脚外骨架模型上的单个电位计的动态步态数据.
主要成果:
- 与动态模型相比,随机森林算法在估计的关节角度中展示了较低的实现错误.
- 随机森林算法导致执行时间缩短.
- 模块化测试方法证明可以用于评估外骨模型.
结论:
- 一个单一的传感器可以提供足够的数据,用于在外骨控制中有效地估计关节角度.
- 随机森林机器学习算法是基于单个传感器的外骨控制的一个有希望的方法.
- 模块化测试有助于对外骨开发中的人机系统进行可靠的评估.
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