弥合惯性传感器和光学运动捕捉之间的方法差距:深度学习作为使用惯性传感器准确联合动力学建模的途径
Vaibhav R Shah1,2, Philippe C Dixon1,2,3
1Institute of Biomedical Engineering, Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本研究引入了一种深度学习方法,用于使用惯性测量单元 (IMU) 数据预测光学运动捕捉标记位置. 这使得传统的生物力学分析能够在实验室外进行准确的运动跟踪.
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:
- 光学运动捕捉 (OMC) 拥有数十年的研究经验,但需要控制的环境.
- 惯性测量单元 (IMU) 提供便携式运动分析,但缺乏直接应用OMC方法.
- 弥合这一差距对于将已建立的生物力学模型应用于IMU数据至关重要.
研究的目的:
- 开发一种深度学习方法,从IMU数据中预测标记物位置.
- 为了使传统的基于OMC的联合动力学计算能够使用IMU数据.
- 在外部数据集上验证拟议方法的通用性.
主要方法:
- 使用了一个自动编码器网络,具有自定义的生物机械损失功能.
- 从7个IMU传感器数据中预测了16个标记位置.
- 通过离开一个主体的交叉验证进行验证,并对外部数据集进行了测试.
主要成果:
- 标记器位置预测实现了2-4厘米的根平均平方误差 (RMSE).
- 射手平面关节角度预测产生了4-7°RMSE,没有动态时间扭曲 (DTW) 调整.
- 通过DTW对齐实现了2-4° RMSE,在多个数据集中保持一致.
结论:
- 深度学习方法成功地弥合了IMU和OMC之间的差距.
- 能够使用IMU进行准确的关节动力学估计和运动分析.
- 为便于将已确定的生物力学方法应用于便携式运动分析系统.
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