强大的车载呼吸速率检测使用多式联接信号融合
Joana M Warnecke1,2, Joan Lasenby3, Thomas M Deserno4
1Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 38106, Braunschweig, Germany. joana.warnecke@plri.de.
这项研究引入了一种多传感器系统,用于在汽车中持续监测呼吸率. 传感器数据的混合融合可以在60%的驾驶时间内可靠地检测健康状况.
科学领域:
- 生物医学工程 生物医学工程
- 汽车健康监测 汽车健康监测
- 信号处理 信号处理
背景情况:
- 车辆中持续的健康监测对于早期疾病检测是不发达的.
- 呼吸速率是健康评估的关键重要标志.
研究的目的:
- 开发冗余传感器系统和信号融合方法,用于驾驶期间监测呼吸速率.
- 评估基于卷积神经网络 (CNN) 的不同融合方法的有效性.
主要方法:
- 使用压电传感器,两个加速度计,以及用于呼吸运动检测的摄像头.
- 从城市,高速公路和乡村驾驶场景中收集了15名受试者的数据.
- 我们比较了四种基于CNN的融合策略:早期,基于传感器的晚期,基于信号的晚期和混合融合.
主要成果:
- 结合所有四个信号的混合融合算法在所有驾驶条件下都表现出最高的准确性.
- 连续的呼吸率测量在总驾驶时间的60%是可行的.
- 在多信号融合中增加的信号数量提高了可靠性.
结论:
- 开发的系统和混合融合方法使车辆的有效持续健康监测成为可能.
- 这项技术有可能在日常通勤期间早期检测疾病.
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