Car as Diagnostic Space (CarDS)

早期发现症状是及早发现疾病的关键。因此，集成到车辆等私人空间的持续健康监测有可能更早地发现疾病。这可以提供更好的治疗，降低死亡率，并降低医疗保健系统的成本。平均而言，德国人每天花在汽车上的时间为43分钟。因此，我们的目标是建立一种车载传感器系统，将医疗检查集成到我们的日常移动性中。我们想要集成一个具有多个传感器的健康监测系统来测量心电(ECG)、光容积图(PPG)、远程PPG(RPPG)和心音(PCG)。冗余的传感器数据增加了数据分析的可靠性。我们将传感器系统集成到CAN-Bus系统中，并考虑使用内置传感器，如加速度计、电容式传感器和外部摄像头来检测文物。我们将对20名测试者在不同的驾驶场景下进行研究：休息、城市、高速公路和农村地区。在记录数据之后，我们将开发一种基于卷积神经网络(CNN)结构的传感器融合方法。我们将比较参考心率和计算的心率，并对算法进行评估。根据我们的数据分析，我们将改进传感器系统，并重复数据记录。作为最后一步，我们将评估伪影检测和传感器融合算法。我们将回答一些未解决的研究问题，例如，“多大百分比的驾驶时间可用于可靠的心率分析？”该项目的结果是回答其他特定学科和特定疾病研究问题的基础。

Early detection of symptoms is critical to detect diseases at an early stage. Therefore, continuous health monitoring integrated into private spaces such as the vehicle has the potential to detect diseases earlier. This enables a better treatment, decreases the mortality rate, and reduces costs in the healthcare system. On average, a German spends 43 min per day in a vehicle. Hence, we aim at building an in-vehicle sensor system that integrates a medical check-up into our daily mobility. We want to integrate a health monitoring system with multiple sensors to measure the electrocardiogram (ECG), Photoplethysmogram (PPG), remote PPG (rPPG), and phonocardiogram (PCG). Redundant sensor data increases the reliability of the data analysis. We will integrate the sensor system into the CAN-BUS system and consider the in-build sensors such as the accelerometer, capacitive sensor, and an external camera for the artifact’s detection. We will conduct a study with 20 test persons in different driving scenarios: rest, city, highway, and rural areas. After recording the data, we will develop a sensor fusion approach based on a convolutional neural network (CNN) structure. We will compare the reference heart rate with the calculated heart rate and evaluate the algorithms. Based on our data analytics, we will improve the sensor system and repeat the data recording. As a final step, we will evaluate the artifact detection and sensor fusion algorithm. We will answer open research questions such as, for instance, “What percentage of the driving time is usable for a reliable heart rate analysis?”. Results from this project are the basis to answer other, subject- as well as disease-specific research questions.