可穿戴式基于传感器的关节动力学估计的个性化使用计算机视觉用于关节外骨应用程序
概括
本研究介绍了一种计算机视觉框架,用于使用深度学习 (DL) 准确地估计下肢关节动力学. 该方法需要最小的数据,并适应模型用于临床群体的实时步态分析.
科学领域:
- 生物力学 生物力学
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
背景情况:
- 准确的下肢关节动力学对于患者监测,康复和外骨控制至关重要.
- 当前的深度学习 (DL) 模型通常需要广泛的数据集来适应新的步态模式.
- 计算机视觉人类姿势估计模型可以部署,但不适合摄像机限制的环境.
研究的目的:
- 开发基于计算机视觉的DL适应框架,用于实时联合动力学估计.
- 为了使准确的步态分析与最小的数据要求和没有专业的运动捕捉.
- 为临床群体和新用户适应现有模型.
主要方法:
- 提出了一个基于计算机视觉的DL适应框架,利用转移学习.
- 通过使用小数据集 (1-2 步态周期) 调整了一个时间卷积网络 (TCN).
- 验证了对刚性膝盖步态数据的框架.
主要成果:
- 调整后的TCN模型将根平均平方误差降低了9.7%,相比之下,该模型仅使用身体健康的数据进行训练.
- 该模型实现了根平均平方误差的19.9%降低,与仅在硬膝盖数据上训练的模型相比.
- 证明了智能手机摄像头训练DL模型的可行性,用于实时动力学估计.
结论:
- 拟议的框架允许实时联合动力学估计,数据要求最小.
- 这种方法有助于适应新用户和临床群体,例如那些膝盖硬的人.
- 该技术在可穿戴机器人和远程患者监控方面有潜在的应用.
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