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A Multi-Modal Sensor for a Bed-Integrated Unobtrusive Vital Signs Sensing Array

机译:用于床上集成不引声重要标志传感阵列的多模态传感器

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In this paper, we present a novel unobtrusive multi-modal sensor for monitoring of physiological parameters featuring capacitive electrocardiogram (cECG), reflective photoplethysmogram (rPPG), and magnetic induction monitoring (MI) in a single sensor. The sensor system comprises sensor nodes designed and optimized for integration into a grid-like array of multiple sensors in a bed and a central controller box for data collection and processing. Hence, it is highly versatile in application and suitable for unobtrusive monitoring of vital signs, both in a professional setting and a home-care environment. The presented hardware design takes both inter-modal interference between cECG and MI into account as well as intra-modal interference due to cross talk between two MI sensors in close vicinity. In a lab study, we evaluated a prototype of our new multi-modal sensor with two sensor nodes on four healthy subjects. The subjects were lying on the sensors and exercising with a hand grip in order to increase heart rate and thus evaluate our sensor both during changing physiological parameters as well as a wider range of those. Heart beat intervals and heart rate variability were derived from both cECG and rPPG. Breathing intervals were derived from the MI sensor. For heart beat intervals, we achieved an RMSE of 2.3 ms and a correlation of 0.99 using cECG. Similarly, using rPPG, an RMSE of 18.9 ms with a correlation of 0.99 was achieved. With regard to breathing intervals derived from MI, we achieved an RMSE of 1.12 s and a correlation of 0.90.
机译:在本文中,我们提出了一种用于监测用于监测单个传感器中的电容心电图(CECG),反射光学读数(RPPG)和磁感应监测(MI)的生理学参数的不引声多模态传感器。传感器系统包括设计和优化的传感器节点,用于集成到床和中央控制器盒中的多个传感器的网格状阵列中,用于数据收集和处理。因此,在申请中具有高度通用性,适用于专业环境和家庭护理环境的不引人注目的生命体征监测。由于近距离附近的两个MI传感器之间的串扰,所呈现的硬件设计考虑到CECG和MI之间的跨跨性干扰以及跨谈话的跨谈话。在实验室研究中,我们评估了我们新的多模态传感器的原型,其中四个健康的两个传感器节点。受试者躺在传感器上,用手抓地握住,以增加心率,从而在改变生理参数和更广泛的那些中评估我们的传感器。心跳间隔和心率变异源自CECG和RPPG。呼吸间隔来自MI传感器。对于心跳间隔,我们使用CECG实现了2.3毫秒的RMSE和0.99的相关性。类似地,使用RPPG,实现了0.99的相关18.9ms的RMSE。关于源自MI的呼吸间隔,我们达到了1.12秒的RMSE和0.90的相关性。

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