Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion
Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion
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DOI：
10.1145/3549941
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Zongxing Xie;Bing Zhou;Xi Cheng;E. Schoenfeld;Fan Ye
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文献类型：
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作者：
Zongxing Xie;Bing Zhou;Xi Cheng;E. Schoenfeld;Fan Ye
心跳和呼吸频率(HR和RR)等基本生命体征是基本的生物指标。他们的家庭纵向收集能够预测和检测疾病的发生和变化，从而提供更早的健康干预。在本文中，我们提出了一种健壮的非接触式生命体征监测系统，该系统使用一对共置的超宽带(UWB)和深度传感器。通过大量的人工检验，我们确定了四种典型的时间和频谱信号模式及其合适的生命体征估计器。我们设计了一个概率加权框架(PWF)来量化这些模式的证据，以更新估计器输出的加权组合，从而稳健地跟踪生命体征。我们还设计了一种基于热图的信号质量检测器，以排除无意运动中的干扰信号。为了在家中监控多个共同居住的受试者，我们建立了一个两分支的长期短期记忆(LSTM)神经网络来区分个人及其活动，提供至关重要的活动背景，以区分关键生命体征和正常生命体征的变异性。为了实现可靠的上下文标注，我们从深度数据中精心设计了连续骨骼姿势的特征集，并开发了一种概率跟踪模型来处理非视线(NLOS)情况。我们的实验结果证明了各个模块以及端到端系统的健壮性和优越的性能，用于被动和上下文感知的生命体征监测。
Basic vital signs such as heart and respiratory rates (HR and RR) are essential bio-indicators. Their longitudinal in-home collection enables prediction and detection of disease onset and change, providing for earlier health intervention. In this article, we propose a robust, non-touch vital signs monitoring system using a pair of co-located Ultra-Wide Band (UWB) and depth sensors. By extensive manual examination, we identify four typical temporal and spectral signal patterns and their suitable vital sign estimators. We devise a probabilistic weighted framework (PWF) that quantifies evidence of these patterns to update the weighted combination of estimator output to track the vital signs robustly. We also design a “heatmap”-based signal quality detector to exclude the disturbed signal from inadvertent motions. To monitor multiple co-habiting subjects in-home, we build a two-branch long short-term memory (LSTM) neural network to distinguish between individuals and their activities, providing activity context crucial to disambiguating critical from normal vital sign variability. To achieve reliable context annotation, we carefully devise the feature set of the consecutive skeletal poses from the depth data, and develop a probabilistic tracking model to tackle non-line-of-sight (NLOS) cases. Our experimental results demonstrate the robustness and superior performance of the individual modules as well as the end-to-end system for passive and context-aware vital sign monitoring.