极端学习机器/有限冲动响应过器和视觉数据辅助惯性导航系统基于人类运动捕捉
Yuan Xu1, Rui Gao1, Ahong Yang2
1School of Electrical Engineering, University of Jinan, Jinan 250022, China.
Micromachines
|November 25, 2023
概括
这项研究引入了一种新的人类运动捕获方法,将极端学习机器 (ELM) 和有限冲动响应 (FIR) 过器与惯性导航系统 (INS) 和视觉数据融合在一起. 该方法显著提高了位置准确性,证明了其在精确的人类运动跟踪方面的有效性.
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
- 计算机视觉 计算机视觉
背景情况:
- 准确的人类运动捕捉对于康复和虚拟现实等应用至关重要.
- 惯性导航系统 (INS) 提供了机动性,但却遭受漂移.
- 基于视觉的系统提供绝对定位,但可以被封闭.
研究的目的:
- 开发一种结合INS和视觉数据的强大的人类运动捕捉方法.
- 通过使用先进的机器学习和信号处理技术融合数据来提高位置准确性.
- 为了解决个人INS和基于视觉的跟踪方法的局限性.
主要方法:
- 一种新的融合方法,集成极端学习机器 (ELM) 和有限冲动响应 (FIR) 过器.
- 使用FIR过器处理视觉和INS数据,以估计人类的位置和姿势.
- 使用ELM将FIR过器输出映射到相应的估计错误以进行精细化.
主要成果:
- 拟议的方法在右臂肘部的位置错误的累积分布函数 (CDF) 中得到了大约12.71%的改善.
- 证明有效地估计人类的位置,即使视觉数据是不可用的.
- 成功集成INS和视觉数据,以实现更准确的人类运动跟踪.
结论:
- ELM,FIR过器,INS和视觉数据的融合为准确的人类运动捕捉提供了高度有效的解决方案.
- 与传统方法相比,该方法在位置准确性方面取得了显著的改进.
- 这种技术为实时人类运动跟踪应用提供了强大而可靠的方法.
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