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一个具有混合深度学习的新型织物应变传感器阵列,用于准确识别膝盖运动
Tao Chen1, Xiaobin Chen2,3, Fei Wang4
1School of Future Technology, South China University of Technology, Guangzhou 511422, China.
Micromachines
|January 28, 2026
概括
一个新的织物应变传感器阵列使用混合深度学习模型准确监测膝关节关节运动. 这种可穿戴技术在康复和体育分析方面表现有前途.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 机器学习 机器学习
背景情况:
- 全面的膝关节关节监测对于康复和体育科学至关重要.
- 现有的传感器系统往往缺乏捕获复杂的多轴动力学和局部组织变形的能力.
- 需要准确,非侵入性和轻量级的解决方案来分析膝盖运动.
研究的目的:
- 开发和评估一种新的轻量级织物应变传感器阵列,用于全面的膝关节关节监测.
- 评估系统捕捉多轴膝关节动力学和应变分布的能力.
- 调查混合深度学习模型对分析传感器数据和分类膝盖运动的有效性.
主要方法:
- 设计了一种两层织物应变传感器阵列,配有八个传感元件.
- 十个受试者在佩戴传感器阵列时执行了三种活动 (坐着抬起腿,站着,走路).
- 混合深度学习模型,结合卷积神经网络 (CNN),双向长期短期记忆 (BiLSTM) 和注意力机制,用于数据分析.
主要成果:
- 织物应变传感器阵列有效地绘制了运动期间膝盖的应变分布.
- 混合深度学习模型在识别基本膝盖动作方面实现了95%的准确性.
- 道注意力分析确定了关键传感器 (2,4和6),这些传感器对分类性能做出了重大贡献.
结论:
- 拟议的织物传感器阵列是监测膝关节运动的可行和准确的系统.
- 混合深度学习模型在提取运动分类的时空特征方面表现出强大的能力.
- 这项技术在医疗康复,体育科学和个性化医疗保健领域具有很大的应用潜力.
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