Warnecke Joana M, Boeker Nicolai, Spicher Nicolai, Wang Ju, Flormann Maximilian, Deserno Thomas M
Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:447-450. doi: 10.1109/EMBC46164.2021.9630935.
Private spaces like apartments and vehicles are not yet fully exploited for health monitoring, which includes continuous measurement of biosignals. This work proposes sensor fusion for robust heartbeat detection in the noisy and dynamic driving environment. We use four sensors: electrocardiography (ECG), ballistocardiography (BCG), photoplethysmography (PPG), and image-based PPG (iPPG). As ground truth, we record a 3-lead ECG with wet electrodes attached to the chest. Twelve healthy volunteers are monitored in rest and during driving, each for 11 min. We propose sensor fusion using convolutional neural networks to detect the sensor combination delivering the most accurate heart rate measurement. For rest, we achieve scores of 95.16% (BCG + iPPG), 96.08% (ECG + iPPG), 96.35% (ECG + BCG), 96.53% (ECG + PPG), 96.58% (PPG + iPPG), and 97.15% (BCG + PPG). In motion, the highest scores are 92.46% (BCG + iPPG, PPG + iPPG, ECG + iPPG), 92.83% (ECG + PPG), 93.03% (BCG + PPG), and 93.08% (ECG + BCG). Fusing all four signals with the best fusion approach results in scores of 97.24% (rest) and 94.38% (motion). We conclude that sensor fusion allows robust heartbeat measurement of car drivers to support continuous and unobtrusive health monitoring for early disease detection.
像公寓和车辆这样的私人空间在健康监测方面尚未得到充分利用，健康监测包括对生物信号的持续测量。这项工作提出了传感器融合技术，用于在嘈杂且动态的驾驶环境中进行可靠的心跳检测。我们使用四种传感器：心电图（ECG）、心冲击图（BCG）、光电容积脉搏波描记法（PPG）和基于图像的PPG（iPPG）。作为基准真值，我们使用附着在胸部的湿电极记录三导联心电图。对12名健康志愿者在休息和驾驶期间进行监测，每人监测11分钟。我们提出使用卷积神经网络进行传感器融合，以检测能提供最准确心率测量值的传感器组合。在休息状态下，我们得到的准确率分别为：95.16%（BCG + iPPG）、96.08%（ECG + iPPG）、96.35%（ECG + BCG）、96.53%（ECG + PPG）、96.58%（PPG + iPPG）和97.15%（BCG + PPG）。在驾驶状态下，最高准确率为：92.46%（BCG + iPPG、PPG + iPPG、ECG + iPPG）、92.83%（ECG + PPG）、93.03%（BCG + PPG）和93.08%（ECG + BCG）。采用最佳融合方法融合所有四种信号，在休息状态下准确率为97.24%，在驾驶状态下准确率为94.38%。我们得出结论，传感器融合能够对汽车驾驶员进行可靠的心跳测量，以支持持续且不引人注意的健康监测，用于早期疾病检测。
