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.

本计画的目标是研究多模态生物信号处理的方法，以进行生命体征的估测。重点在于开发一种独立于传感方法的方法，特别是考虑非接触和不引人注目的方式，这些方式通常很容易受到干扰。然而，与传统的临床仪器相比，它们可以潜在地集成到日常生活的对象中。为此，一个统一的，宏观的生物信号生成模型是必要的。我们的方法提出了从虚拟源生成心脏信号。该虚拟源信号通过虚拟通道传输，在虚拟通道的末端，可以测量诸如心电图、光电体积描记图或心冲击图的真实的物理信号。该项目的第一个目标是回答这样一个源滤波器模型的心脏和后来甚至呼吸信号的生成的假设必须作出的问题。基于这些假设，现有的信号处理方法将被评估和调整或从头开始创建。如果所提出的源滤波器模型适用于每个主题，有两个优点：第一，基于模型的传感器融合成为可能。其次，诊断信息可以潜在地从适应的模型参数中提取。为了验证所提出的模型和测试信号处理方法，需要创建多模态生物信号及其失真的仿真环境。此外，需要记录真实的测量数据。为此，该研究所已经提供的几种不显眼的传感模式需要集成到统一的多模式测量设置中。除了记录干净的数据外，该设置还允许生成各种可再现的伪影。记录的测量结果和参考数据将通过UNOVIS（MedIT公共非侵入性生命体征数据库）向公众提供。因此，该项目的总体目标是生成通用方法和结构，以推进非侵入性生命体征估计领域的研究。在随后的项目中，这些方法和结构可以应用于不同的应用领域和病理学。