使用sEMG和机器人动力学进行定量肘部运动功能的评估的行动意识多式波纹融合网络
Zilong Song1, Pei Zhu2, Cuiwei Yang1
1Department of Biomedical Engineering, College of Biomedical Engineering, Fudan University, Shanghai 200433, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了一个新的AI网络 (AMWFNet),用于在中风后客观地评估上肢运动,使用肌肉信号和机器人数据. 它准确量化了Fugl-Meyer评估得分,改善了康复.
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
- 人工智能在医学中的应用
背景情况:
- 精确的上肢运动评估对于中风康复至关重要.
- 目前用于Fugl-Meyer评估 (FMA-UE) 的临床尺度是主观的.
- 需要客观的量化方法来改善康复结果.
研究的目的:
- 开发一个与FMA-UE一致的客观上肢运动评估的自动化系统.
- 整合表面电肌图 (sEMG) 和机器人动力学数据以进行增强的评估.
- 为实时康复应用程序创建一个计算高效的模型.
主要方法:
- 提出了行动意识的多模式波形融合网络 (AMWFNet).
- 使用连续波形变换 (CWT) 进行信号处理.
- 实施了一种模式关门机制和特征融合的行动嵌入.
- 在40名参与者身上验证了模型 (20名中风后,20名健康人) 在18个达到任务中验证了模型.
主要成果:
- 在六个类别的分类中,AMWFNet的准确性达到了94.68%,超过了基线模型 (随机森林,SVM,1D-CNN) 的9%.
- 与现有方法相比,该模型表现出优越的性能.
- 轻量级架构允许实时集成到康复机器人中.
结论:
- 在中风康复中,AMWFNet为上肢运动评估提供了一个客观有效的解决方案.
- 多式联运方法提高了量化准确性,并提供了可操作的见解.
- 实时能力有助于在机器人系统中无整合评估和培训.
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