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相关实验视频

Updated: May 9, 2025

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
05:23

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients

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侧向行走步态识别和部角度预测使用双任务学习框架

Mingxiang Luo1, Meng Yin1, Jinke Li1

  • 1Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Cyborg and bionic systems (Washington, D.C.)
|May 2, 2025
PubMed
概括
此摘要是机器生成的。

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这项研究引入了"双胞胎兄弟"模型,用于准确的横行步态识别和使用电肌图 (EMG) 信号预测部角度. 该模型通过在分类步态阶段和估计关节角度方面实现高精度,显著改善了外骨的控制.

科学领域:

  • 生物力学和生物医学工程
  • 医疗保健中的机器学习
  • 康复机器人 康复机器人

背景情况:

  • 侧向行走增强了部绑架器的力量,这对移动性至关重要.
  • 准确的步态识别和关节角度预测对于控制外骨架等辅助设备至关重要.
  • 电肌图 (EMG) 信号为实时生物机械分析提供了一个有希望的途径.

研究的目的:

  • 开发和验证一种新的双任务学习框架,即"双胞胎兄弟"模型.
  • 为了准确地分类横行走的步态阶段,并从EMG信号中估计连续的关节角度.
  • 加强外骨架的控制能力,以改善康复和援助.

主要方法:

  • 采用了一种融合卷积神经网络 (CNN),长期短期记忆 (LSTM),神经网络 (NN) 和注意力机制的双重任务学习框架.
  • 在横行走过程中,从十个受试者的六个肌肉中收集并分析了电肌图 (EMG) 信号.
  • 该模型与传统的机器学习方法进行了评估,包括CNN-LSTM,CNN,LSTM,支持矢量机 (SVM),NN和K-最近邻居 (KNN).

主要成果:

  • "双胞胎兄弟"模型实现了98.81%±0.14%的高步态识别精度.
  • 优秀的部角度预测准确度被证明,根平均平方误差 (RMSE) 为0.9183° ± 0.024° (左) 和1.0511° ± 0.027° (右),R2值分别为0.9853 ± 0.006和0.9808 ± 0.008.

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  • 该模型在步态识别和部角度估计任务中超过了所有比较方法.

    • 结论:

    • 拟议的"双胞胎兄弟"模型为实时横向步行步态识别和关节角度预测提供了强大而准确的解决方案.
    • 这一进步具有显著的潜力,可以提高外骨控制系统的精度和有效性.
    • 这些发现为更复杂的人机交互在康复和辅助技术领域铺平了道路.