VitalHub: Robust, Non-Touch Multi-User Vital Signs Monitoring using Depth Camera-Aided UWB
VitalHub: Robust, Non-Touch Multi-User Vital Signs Monitoring using Depth Camera-Aided UWB
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DOI：
10.1109/ichi52183.2021.00056
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发表时间：
2021-08
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
基本的生命体征，如心率和呼吸率（HR和RR）是必不可少的生物指标。他们在家中的纵向收集能够预测和检测疾病的发作和变化，从而提供早期的健康干预。这种类型的数据收集，解释和评估对于面临无数健康挑战的老年人特别有价值。然而，呼吸谐波和互调对弱得多的心跳信号造成强干扰，因此鲁棒的生命体征监测仍然难以实现。在本文中，我们提出了VitalHub，一个强大的，非接触的生命体征监测系统，使用一对共处的超宽带（UWB）和深度传感器。通过大量的人工检查，我们确定了四个典型的时间和频谱信号模式和他们合适的生命体征估计。我们设计了一个概率加权框架（PWF），量化这些模式的证据，以更新估计器输出的加权组合，以稳健地跟踪生命体征。我们还设计了一个基于“热图”的信号质量检测器，该检测器实现了接近人类的性能，将信号损坏与大运动区分开来。为了在家中监测多个同居受试者，我们利用来自深度数据的连续骨骼姿势来区分个体及其活动，提供重要的活动背景以消除关键的正常生命体征变异性。大量的实验表明，VitalHub在RR/HR的80百分位数处实现了1.5/3.2“每分钟呼吸/心跳”（用“bpm”表示）的误差，接近理想但不切实际的预言的1.2/1.5 bpm误差“天花板”。我们还揭示了现有的技术谐波和互调依赖于假定的信号模式，因此可能会失败，在现实世界的动态变化。
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. This type of data collection, interpretation and evaluation is especially valuable for older adults facing myriads of health challenges. However, respiration harmonics and intermodulation cause strong disturbances to much weaker heartbeat signals, thus robust vital signs monitoring remains elusive. In this paper, we propose VitalHub, 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 signs 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 that achieves near-human performance differentiating signal corruptions from large motion. To monitor multiple cohabiting subjects in-home, we leverage consecutive skeletal poses from the depth data to distinguish between individuals and their activities, providing activity context important to disambiguating critical from normal vital sign variability. Extensive experiments show that VitalHub achieves 1.5/3.2 “breaths/beats per minute” (denoted by “bpm”) errors at 80-percentile for RR/HR, approaching the 1.2/1.5 bpm error “ceiling” of an idealistic but impractical oracle. We also reveal how existing techniques for harmonics and intermodulation rely on presumed signal patterns thus may fail under real-world dynamic changes.