一个分析模型的基于节能的在线调整,用于准确估计多关节刚度,并配有联合模块软执行器
Fuko Matsunaga1, Taichi Kurayama2, Ming-Ta Ke3
1Graduate School of Engineering, Chiba University, Chiba, Japan.
Wearable technologies
|September 19, 2025
概括
这项研究引入了一种使用人工神经网络的节能方法,以准确估计手指关节硬度. 这种方法通过克服当前分析和数据驱动方法的局限性,改善了中风患者的个性化康复.
科学领域:
- 生物医学工程 生物医学工程
- 康复技术 康复技术 康复技术
- 机器人技术 机器人技术 机器人技术
背景情况:
- 准确的指关节硬度估计对于中风患者的评估和康复至关重要.
- 软执行器提供个性化的刚度估计,但面临分析和数据限制.
- 现有的方法在执行器-手指交互和数据概括方面存在困难.
研究的目的:
- 开发一种改进的方法,用于在线调整用于指关节硬度估计的分析模型.
- 解决现有的分析和数据驱动方法的局限性.
- 为了提高个人化康复的刚性估计的准确性和概括性.
主要方法:
- 提出了一种基于节能的在线调整方法,用于分析模型.
- 利用人工神经网络 (ANN) 将分析模型适应测量数据.
- 引入了基于执行器指复合体内的能量平衡的校正参数.
主要成果:
- 与传统的纠正方法相比,拟议的方法显示出更高的性能.
- 实现了比纯数据驱动方法更好的概括潜力.
- 成功估计了人类受试者的性,即使在较大的实验错误的情况下也显示出有效性.
结论:
- 开发的方法提高了分析模型的适应性和指关节硬度的估计准确性.
- 这种方法克服了现有方法的局限性,在康复中提供了更好的个性化.
- 代表了实现针对中风患者个性化康复策略的重大进展.
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