一个框架来取代测量的地面反应力与ANN预测的,用于OpenSim中的联合负载估计
概括
这项研究将人工神经网络 (ANN) 与肌肉骨 (MSK) 建模相结合,以预测下肢关节反应力 (JRF). 虽然ANN显示出预测地面反应力 (GRF) 的前景,但准确性因方向而异,影响了JRF预测.
科学领域:
- 生物力学 生物力学
- 计算建模计算建模
- 人工智能的人工智能
背景情况:
- 肌肉骨 (MSK) 建模对于分析下肢关节负荷至关重要.
- 准确的地面反应力 (GRF) 是MSK模型的重要输入.
- 使用人工神经网络 (ANN) 预测GRF为实验测量提供了一个潜在的替代方案.
研究的目的:
- 将ANN与OpenSim MSK建模集成在一起,以预测下肢关节反应力 (JRF).
- 评估ANN预测的GRF的准确性及其对JRF估计的影响.
- 为了确定影响预测准确性的因素,为未来的模型改进.
主要方法:
- 从脚位置数据中训练了一个ANN来预测GRF.
- 预测了20个新对象的GRF,并将它们与测量的GRF (前后,中侧,垂直) 进行了比较.
- 使用OpenSim计算JRF,使用测量和ANN预测的GRF.
主要成果:
- 在中侧和垂直的GRF中,ANN实现了中度的相关性,但在前后的GRF中,准确性较低.
- 预测GRF中的错误传播到JRF估计中,观察到更高的可变性.
- 对于JRFs来说,规范化的根平均平方误差 (NRMSE) 从0.16 ± 0.07到0.33 ± 0.32.
结论:
- ANN预测的GRF显示了MSK分析的潜力,但需要提高准确性,特别是垂直GRF.
- 像坐标系统和数据重新采样等因素影响了预测准确度.
- 进一步精细化可以使ANN预测的GRFs在临床和研究环境中取代测量的GRFs.
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