导向NN:一个以物理为基础的轻量级神经网络,用于从IMU数据中实时进行联合动力学估计
Qingyao Bian1, Hongbo Wang2, Khalid Alsayed3
1School of Engineering, University of Birmingham, Birmingham, United Kingdom.
Frontiers in bioengineering and biotechnology
|January 28, 2026
概括
一个新的基于物理学的神经网络,OrientationNN,准确地估计了来自惯性测量单位 (IMU) 的关节动力学. 这种轻量级模型为实时人类运动分析提供了计算效率高的解决方案.
科学领域:
- 生物力学 生物力学
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 精确的关节动力学对于人类运动分析至关重要,但传统的光学运动捕捉是昂贵和复杂的.
- 惯性测量单位 (IMU) 提供了一个便携式的替代方案,但准确的动力学估计仍然是一个挑战.
研究的目的:
- 开发一个轻量级的,基于物理的神经网络,用于使用IMU实时联合动力学估计.
- 通过集成的物理约束来确保生物力学一致的估计.
主要方法:
- 开发了OrientationNN,一个紧的多层感知器,集成基于方向的物理约束.
- 在公共数据集上评估了OrientationNN,将其与OpenSense,MLP,LSTM,CNN和Transformer模型进行比较.
主要成果:
- 导向NN在走路运动中实现了平均关节角度估计误差低于5°.
- 该模型在所有动力学变量中表现优于基于物理的OpenSense框架.
- 导向NN表现出高计算效率,每只有4.9 × 103 FLOP和10.8 KB的参数.
结论:
- 导向NN提供了从IMU数据中准确和计算高效的关节动力学估计.
- 该模型为可穿戴生物机械和运动分析应用提供了具有成本效益和可扩展的解决方案.
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