使用用于可穿戴超声波应用程序设计的自动细分来评估腹肌收缩情况
Hannah Strohm1,2, Sven Rothluebbers3, Luis Perotti4
1Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany. hannah.strohm@mevis.fraunhofer.de.
概括
可穿戴式超声波设备的深度学习算法可以在物理治疗练习期间自动分析肌肉活动. 这项技术在家庭使用场景中显示出实时反和改善患者结果的前景.
科学领域:
- 生物医学工程 生物医学工程
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 可穿戴式超声波设备提供持续的肌肉活动监测.
- 超声波数据的自动分析可以增强物理治疗反,减少手动评估需求.
- 实时反系统对于患者的坚持和炼效率至关重要,特别是在家庭康复中.
研究的目的:
- 在超声波视频中研究腹肌的三种基于深度学习的细分方法.
- 使用细分数据,在细分稳定炼期间自动分类肌肉收缩状态.
- 评估物理治疗应用后期和实时分析的可行性.
主要方法:
- 开发三种深度学习细分模型:一个2D网络和两个集成时间信息 (跟踪或直接架构集成).
- 基于诸如肌肉厚度和质量中心变化等指标的肌肉收缩状态的分类.
- 用于绩效评估的追溯和实时分析场景的模拟.
主要成果:
- 追溯分析在肌肉状态分类中实现了71%的准确性,而手动细分则为90%.
- 实时方法提供了大部分正确的反,当回顾性分析是准确的.
- 结合时间信息的网络显示了随着时间的推移更一致的细分.
结论:
- 追溯和实时分析方法都是可行的,用于物理治疗中的自动化肌肉活动监测.
- 集成时间数据的深度学习模型产生了更一致的细分.
- 需要进一步的研究来解决细分,分类和实时评估的局限性,重点是图像质量和硬件设计.
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