Car as Diagnostic Space (CarDS)

汽车作为诊断空间 (CarDS)

• 批准号: 513991330
• 负责人: Professor Dr. Thomas Martin Deserno
• 金额: --
• 依托单位: Institut für Fahrzeugtechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/513991330?language=en
• 关键词: Car; Diagnostic; Space; CarDS

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.

早期发现症状对于早期发现疾病至关重要。因此，集成到车辆等私人空间中的持续健康监测有可能更早地发现疾病。这可以实现更好的治疗，降低死亡率，并降低医疗保健系统的成本。平均而言，德国人每天在汽车上花费43分钟。因此，我们的目标是建立一个车载传感器系统，将医疗检查集成到我们的日常移动中。我们希望将健康监测系统与多个传感器集成，以测量心电图（ECG）、光电容积描记图（PPG）、远程PPG（rPPG）和心音图（PCG）。冗余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总线系统中，并考虑内置传感器，如加速度计，电容传感器和外部摄像头的文物的检测。我们将在不同的驾驶场景中对20名测试人员进行研究：休息，城市，高速公路和农村地区。记录数据后，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与计算心率并评估算法。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。我们将回答开放性的研究问题，例如，“驾驶时间的百分比有多大可用于可靠的心率分析？”。该项目的结果是回答其他主题以及疾病特定研究问题的基础。