人类运动深度神经网络中的模糊同步概率图 时间序列分析
IEEE journal of biomedical and health informatics
|September 25, 2024
概括
本研究引入了一种新的模糊同步概率 (FSL) 图形方法来分析生物时间序列,通过捕捉复杂的变量相互作用来提高康复炼评估的可解释性.
科学领域:
- 多变量时间序列分析.
- 生物数据分析 生物数据分析
- 基于图形的机器学习
背景情况:
- 变量交互性是生物时间序列的关键,但图形构造方法通常是昂贵的或忽略数据复杂性.
- 现有的方法在计算费用,培训需求以及处理非线性和非静止性方面扎.
研究的目的:
- 提出一种用于构建图形的新方法,以表示生物多变量时间序列中的可变相互作用.
- 将这种方法应用于使用人类关节运动数据的自动化康复运动评估.
- 提高复杂生物系统中决策过程的可解释性.
主要方法:
- 使用模糊同步概率 (FSL) 标准构建图形,重点关注定性相似性和变量依赖性.
- 将FSL构建的图表应用于从康复练习中获得的人体关节运动数据.
- 扩展了一个深度混合密度神经网络 (DMDN) 具有卷积层来处理FSL图,创建基于FSL图的深度神经网络 (FSLGDN).
主要成果:
- FSLGDN模型的性能优于使用线性相关性和人类解剖学进行图形构建的方法.
- 对关节运动相互作用的基于任务的分析比基于解剖学的图表更有益.
- 与线性相关联方法相比,FSLGDN模型从非静止运动数据中获取了更多信息.
结论:
- 拟议的FSL图形构造为分析多变量时间序列中的变量依赖提供了更直观和更易于解释的方法.
- FSLGDN方法为自动化康复运动评估提供了一个强大的工具,为关节动力学提供了更深入的见解.
- 这种方法通过提供更清晰的特征依赖关系表示来增强决策过程.
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