从使用前神经网络的多个动力变量来估计行走过程中的地面反应力和时刻
Jeheon Moon1, Taewhan Kim2, Sangwoo Lee3
1Department of Physical Education, Korea National University of Education, Cheongju, Chungbuk, Republic of Korea.
概括
这项研究介绍了一种使用可穿戴传感器和人工智能的成本效益高的方法,以准确预测步行过程中的地面反应力和时刻,为昂贵的强力板提供灵活的替代方案.
科学领域:
- 生物力学 生物力学
- 可穿戴技术可穿戴技术
- 运动科学中的人工智能
背景情况:
- 在步态分析中,地面反应力 (GRF) 和地面反应矩 (GRM) 是必不可少的.
- 传统的强力板是准确的,但昂贵和空间限制.
研究的目的:
- 评估使用前神经网络 (FNN) 预测GRF和GRM的准确性和可靠性.
- 将红外摄像头的位置数据与可穿戴加速度计 (ACC) 数据集成,以提高预测.
主要方法:
- 80名参与者走过强力板,同时收集了他们的身体部分位置和ACC数据.
- 一个feedforward神经网络使用这些组合数据进行训练,以预测GRF和GRM.
- 使用RMSE和NRMSE等指标量化预测准确度.
主要成果:
- 结合位置和ACC数据,可以显著改善所有方向 (垂直,前后,中侧) 的GRF预测.
- 高相关性 (0.979) 实现了中侧侧面GRF,NRMSE为6.07%.
- 在GRM预测中,特别是在角平面 (R2=0.939) 中,ACC集成显示出更高的性能.
结论:
- 拟议的FNN模型整合了摄像头和ACC数据,与现有方法相比,其表现优越.
- 这种方法为临床和体育评估提供了强力板的经济有效和灵活的替代方案.
- 这些发现表明,在非侵入性步行分析技术方面取得了重大进展.
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