基于雷达的生命信号检测系统的心脏活动的胸壁运动模型
1School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
研究人员开发了一种新模型来模拟心跳诱导的胸壁运动 (CWM) 数据. 这种方法解决了雷达数据集的稀缺性,使得深度学习在非接触式生命体征检测方面的进步成为可能.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 基于雷达的非接触式生命体征检测越来越多地使用深度学习.
- 雷达数据集的有限可用性阻碍了深度学习模型的开发.
- 用于雷达检测生命体征的数据采集和标记是复杂的,需要专门的资源.
研究的目的:
- 为模拟心跳诱导的胸壁运动 (CWM) 提出一种新型模型.
- 创建一个大数据集,用于训练基于雷达的生命体征检测的深度学习模型.
- 为了克服稀缺的局限性,现实世界的雷达数据.
主要方法:
- 分析了VICON红外 (IR) 运动捕捉和连续波 (CW) 雷达数据的CWM在呼吸保持期间.
- 在心脏循环阶段中识别和数学建模运动特征.
- 利用心跳的生理特性来告知数学函数选择.
主要成果:
- 开发的模型产生了与测量CWM数据密切匹配的模拟数据.
- 使用动态时间扭曲 (DTW) 和根平均平方误差 (RMSE) 的验证证实了数据的真实性.
- 模型参数可以调整,以模拟不同个体的心跳信号.
结论:
- 该CWM模拟模型有效地为深度学习生成现实的数据.
- 这便于在基于雷达的非接触式生命体征检测中应用数据驱动的方法.
- 预计这种方法将加速该领域的研发.
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