通过1D域转换从智能内分布式vGRF进行3D脚动力学估计
IEEE journal of biomedical and health informatics
|September 5, 2025
概括
这项研究使用深度学习将智能内垂直地面反应力 (vGRF) 数据转换为高质量的3D地面反应力和动量 (3D-GRF&M) 和压力中心 (CoP) 数据,改进运动分析.
科学领域:
- 生物力学
- 人类运动分析
- 可穿戴传感器技术
背景情况:
- 了解脚动力学对于分析人类运动和机械负荷至关重要.
- 虽然垂直地面反应力 (vGRF) 是常见的,但全面的3D动力数据 (3D-GRF&M,CoP) 提供了更深入的见解.
- 智能内提供便携式vGRF测量,但与实验室设备相比,数据质量往往较差.
研究的目的:
- 开发一个深度学习模型,从智能内vGRF数据中生成仪器化跑步机级3D-GRF&M-CoP.
- 为了提高智能内捕获的vGRF数据的质量.
- 确定最佳的脚部传感器位置,以准确地估计3D动力学.
主要方法:
- 利用基于深度学习的域转换使用1D序列对序列 (1D-s2s) 模型.
- 应用多段分析来确定动力参数估计的关键脚部区域.
- 开发和评估一个新的深度学习架构Ke2KeNet.
主要成果:
- 成功地将分布式vGRF信号转化为全面的3D-GRF&M-CoP数据.
- 证明了可与仪器跑步机相比的增强vGRF数据质量.
- 新的Ke2KeNet模型在准确性方面超过了现有的1D-s2s基准.
结论:
- 深度学习可以通过便携式智能内进行高准确度的3D脚动力估计.
- 这种方法显著提高了可穿戴传感器在详细生物机械分析中的潜力.
- 优化传感器布局和先进的深度学习模型是释放智能内全部潜力的关键.
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