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PerSiVal:深度神经网络用于激活驱动的连续机械上肢模型的普遍模拟
David Rosin1,2, Johannes Kässinger3,4, Xingyao Yu5,4
1Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Pfaffenwaldring 5a, 70569, Stuttgart, Germany. rosin@imsb.uni-stuttgart.de.
Medical & biological engineering & computing
|February 3, 2026
概括
这项研究提出了一个新的深度学习模型,用于实时可视化肌肉骨模拟. 这种方法使复杂的生物机械模型能够在资源有限的设备上运行,在AR,手术和物理治疗中推进应用.
科学领域:
- 生物力学 生物力学
- 计算机科学 计算机科学
- 医疗模拟 医疗模拟
背景情况:
- 连续机械肌肉骨模型提供了详细的模拟,但需要大量的计算资源.
- 在资源较差的系统 (AR,移动) 上实时可视化对于人机交互,手术支持和物理治疗至关重要.
研究的目的:
- 开发一种新的深度神经网络架构,用于实时可视化肌肉骨系统模拟.
- 在资源有限的平台上部署复杂的生物机械模型.
主要方法:
- 一个密集连接的神经网络被设计用于可视化连续机械肌肉骨模拟.
- 使用激活驱动的五个肌肉上肢模型来获得肌肉变形.
- 一个稀疏的网格替代品捕获了表面变形,并训练了一个深度学习模型进行实时可视化.
主要成果:
- 深度神经网络在双肩臂表面网上实现了0.97±0.16毫米 (0.57±0.10%) 的平均位置误差.
- 评估时间为9.88ms (CPU) 和3.48ms (GPU),使理论速达到101fps和287fps.
- 该方法在复杂的网状模型的实时可视化方面被证明是有效的.
结论:
- 替代体和深度神经网络的组合使复杂的肌肉骨模型的实时可视化成为可能.
- 这种方法适用于超越肌肉骨系统的复杂网状模型的其他实时可视化.
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