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基于深度学习的人类关节定位使用毫米波雷达和序列融合

Parma Hadi Rantelinggi, Xintong Shi, Mondher Bouazizi

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

这项研究引入了一种使用频率调制连续波 (FMCW) 雷达进行精确人类关节位置检测的新框架. 该方法增强了时空特征表示,提高了监测的准确性和可靠性.

科学领域:

生物医学工程生物医学工程
信号处理信号处理
机器学习机器学习

背景情况:

毫米波 (mmWave) 频率调制连续波 (FMCW) 雷达在RGB和深度摄像头上为人类监控提供了优势,包括在低光条件下操作和增强隐私.
传统的FMCW雷达技术难以准确地捕捉人类关节点检测的时空特征.

研究的目的:

利用FMCW雷达数据开发一个用于精确估计人类关节位置的新框架.
增强时空特征的表示,以提高人类姿势估计的准确性.

主要方法:

一个新的框架,包含序列连接用于预处理,以保持时空连续性.
一个深度学习架构,将卷积神经网络 (CNN) 结合起来用于空间特征,变压器用于长期依赖,双向长期短期记忆 (Bi-LSTM) 用于时间建模.

主要成果:

拟议的方法有效地利用时空数据,从而提高了人类关节位置检测的准确性和可靠性.
实验验证表明平均绝对误差 (MAE) 为1.77厘米,根平均平方误差 (RMSE) 为2.92厘米.

结论:

新的框架显著提高了使用FMCW雷达估计人类联合位置的准确性和可靠性.
集成的深度学习架构优化了时空数据处理,加速了模型的融合,并重申了模型的有效性.

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