Multimodal Sensor Fusion and Bio-Signal Processing for Vital Sign Estimation (UNOSECO)

用于生命体征估计的多模态传感器融合和生物信号处理（UNOSECO）

• 批准号: 313380423
• 负责人: Professor Dr.-Ing. Steffen Leonhardt
• 金额: --
• 依托单位: Lehrstuhl für Medizinische Informationstechnik (MedIT)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/313380423?language=en
• 关键词: Multimodal; Sensor; Fusion; Bio; Signal

The goal of this project is the investigation of methods of multimodal bio-signal processing for vital sign estimation. The focus lies on the development of a methodology that is independent of the sensing method and especially considers non-contact and unobtrusive modalities, which in general are very susceptible to disturbances. However, in contrast to traditional clinical instrumentation they can potentially be integrated into objects of everyday life. For this, a unified, macroscopic model of bio-signal generation is necessary. Our approach proposes the generation of cardiac signals from a virtual source. This virtual source signal is transmitted through virtual channels, at the end of which a real physical signal such as electrocardiogram, photoplethysmogram or ballistocardiogram can be measured. The first goal of this project is answering the question which assumptions have to be made about such a source-filter model for the generation of cardiac and later even respiratory signals. Based on these assumptions, existing signal processing methods will be evaluated and adapted or created from scratch. If the proposed source-filter model is adapted for each subject, two advantages arise: First, model-based sensor-fusion becomes possible. Second, diagnostic information can potentially be extracted from the adapted model parameters. To validate the proposed model and to test signal processing methods, a simulation environment for multimodal bio-signal and their distortions needs to be created. Additionally, real measurement data needs to be recorded. For this, several unobtrusive sensing modalities already available at the institute need to be integrated into a unified multimodal measurement setup. In addition to the recording of clean data, this setup will allow the generation of various, reproducible artifacts. The recorded measurements and reference data will be provided to the public via UNOVIS, the "MedIT Public Unobtrusive Vital Sign Database". Hence, the overall goal of this project is the generation of general methods and structures to advance research in the area of unobtrusive vital signs estimation. In subsequent projects, these methods and structures can be applied to different areas of applications and pathologies.

该项目的目的是研究生命体征估计的多模式生物信号处理方法。重点在于开发一种独立于感应方法的方法，尤其是考虑了非接触式和不引人注目的方式，通常这种方式非常容易受到干扰。但是，与传统的临床仪器相反，它们可能会融入日常生活的对象中。为此，必须进行生物信号产生的统一的宏观模型。我们的方法提出了从虚拟来源产生的心脏信号。该虚拟源信号是通过虚拟通道传输的，在该通道的末尾，可以测量真实的物理信号，例如心电图，光心电图或ballistarcardiogon图。该项目的第一个目标是回答一个问题，该问题必须对这种来源过滤器模型进行哪些假设，以生成心脏，甚至是呼吸信号。基于这些假设，将对现有的信号处理方法进行评估，改编或从头开始。如果为每个主题调整了提出的源滤波器模型，则会出现两个优点：首先，基于模型的传感器融合将成为可能。其次，诊断信息可以从适应的模型参数中提取。为了验证提出的模型和测试信号处理方法，需要创建用于多模式生物信号的模拟环境及其扭曲。此外，需要记录实际测量数据。为此，研究所已经可用的几种不引人注目的感应方式需要集成到统一的多模式测量设置中。除了记录清洁数据外，此设置还将允许生成各种可再现的伪影。记录的测量和参考证明将通过Unovis（Medit公共不受欢迎的生命标志数据库”提供给公众。因此，该项目的总体目标是生成一般方法和结构，以推动对无引人注目的生命体征估算领域的研究。在随后的项目中，这些方法和结构可以应用于应用和病理的不同领域。