估计下肢扭矩:一种基于连续波形变换和深度学习方法的新型混合方法
Shu Xu1,2, Tao Wang2, Zenghui Ding2
1Science Island Branch, Graduate School of USTC, University of Science and Technology of China, Hefei, Anhui, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究引入了一种具有成本效益的深度学习方法,使用惯性测量单元 (IMU) 进行准确的实时下肢关节扭矩估计,改进生物机械分析和伤害预防.
科学领域:
- 生物力学 生物力学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 对人类下肢的生物机械分析对于运动评估,伤害预防和康复至关重要.
- 传统的方法,如光学运动捕捉是昂贵和复杂的.
- 需要易于获取和易于使用的生物机械分析工具.
研究的目的:
- 开发一个具有成本效益和用户友好的实时下肢关节扭矩估计系统.
- 将惯性测量单元 (IMU) 与一个新的深度学习框架集成.
- 提高生物机械分析的准确性和效率.
主要方法:
- 使用惯性测量单位 (IMU) 来获取数据.
- 采用深度学习框架,将连续波形变换 (CWT) 结合起来进行时间频率分析.
- 集成多头自我注意 (MHSA),双向长期短期记忆 (Bi-LSTM) 和1D卷积残余网络 (1D Conv ResNet).
主要成果:
- 在联合扭矩估计中实现了高精度,RMSE为0.16N·m/kg,R2为0.91,Pearson相关性为0.95.
- 与现有模型相比,证明了更高的计算效率.
- 实现了152.6毫秒的单周期推断时间,使实时应用成为可能.
结论:
- 拟议的基于IMU的深度学习框架为下肢关节扭矩估计提供了准确,高效和具有成本效益的解决方案.
- 这种方法为各种应用提供了便携式生物机械监控系统.
- 该方法解决了传统步态分析技术的局限性.
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