脚时刻估计基于一种新型分布式脚部压力传感系统
IEEE journal of biomedical and health informatics
|August 16, 2024
概括
这项研究引入了一种新的脚下压力系统,用于估计脚时刻,这对于步态分析和外骨控制至关重要. 基因算法关闭循环单元 (GA-GRU) 模型实现了高精度,为实时监控提供了便携式解决方案.
科学领域:
- 生物力学 生物力学
- 机器人技术 机器人技术 机器人技术
- 可穿戴技术可穿戴技术
背景情况:
- 脚时刻估计对于步态分析,康复和外机器人控制至关重要.
- 目前使用光学运动捕捉和力板的方法是实验室限制的,对于现实应用来说是不切实际的.
- 存在需要便携式和准确的脚时刻估计系统.
研究的目的:
- 开发一种新型分布式脚下压力系统,用于估计脚动力.
- 提出和评估机器学习模型,以使用脚下压力数据来估计脚的动量.
- 为了验证系统的性能与地面行走的黄金标准方法相比.
主要方法:
- 集成八个压力传感器到每个内,以捕捉脚压力分布.
- 训练四个神经网络模型,包括一个基因算法-Gated循环单元 (GA-GRU),使用脚部压力数据.
- 实验验证与八个实验对象在地面上行走,使用光学运动捕捉和力量板作为参考.
主要成果:
- 该GA-GRU模型在通用脚时刻估计中表现出卓越的性能.
- 该GA-GRU模型实现了7.23%的正常化根平均平方误差 (NRMSE) 和0.85.85的交叉相关系数 (ρ).
- 开发的系统和方法在可穿戴机器人控制和人类运动监控方面被证明是有效的.
结论:
- 新的分布式脚部压力系统为脚时刻估计提供了一个可行的替代方案.
- GA-GRU模型为实时脚时刻计算提供了一个高度准确和通用的方法.
- 这项技术在可穿戴机器人和人类运动分析领域有很大的应用潜力.
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