Al Hossain Forsad, Lover Andrew A, Corey George A, Reich Nicholas G, Rahman Tauhidur
University of Massachusetts Amherst, Amherst, MA, 01002, USA.
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2020 Mar;4(1). doi: 10.1145/3381014. Epub 2020 Mar 18.
We developed a contactless syndromic surveillance platform that aims to expand the current paradigm of influenza-like illness (ILI) surveillance by capturing crowd-level bio-clinical signals directly related to physical symptoms of ILI from hospital waiting areas in an unobtrusive and privacy-sensitive manner. FluSense consists of a novel edge-computing sensor system, models and data processing pipelines to track crowd behaviors and influenza-related indicators, such as coughs, and to predict daily ILI and laboratory-confirmed influenza caseloads. uses a microphone array and a thermal camera along with a neural computing engine to passively and continuously characterize speech and cough sounds along with changes in crowd density on the edge in a real-time manner. We conducted an IRB-approved 7 month-long study from December 10, 2018 to July 12, 2019 where we deployed in four public waiting areas within the hospital of a large university. During this period, the platform collected and analyzed more than 350,000 waiting room thermal images and 21 million non-speech audio samples from the hospital waiting areas. can accurately predict daily patient counts with a Pearson correlation coefficient of 0.95. We also compared signals from with the gold standard laboratory-confirmed influenza case data obtained in the same facility and found that our sensor-based features are strongly correlated with laboratory-confirmed influenza trends.
我们开发了一个非接触式症状监测平台，旨在通过以不显眼且注重隐私的方式从医院候诊区捕捉与流感样疾病（ILI）身体症状直接相关的人群层面生物临床信号，来扩展当前ILI监测模式。FluSense由一个新型边缘计算传感器系统、模型和数据处理管道组成，用于跟踪人群行为和流感相关指标，如咳嗽，并预测每日ILI和实验室确诊流感病例数。它使用麦克风阵列、热成像摄像头以及神经计算引擎，以实时、被动且连续的方式在边缘端对语音、咳嗽声音以及人群密度变化进行特征提取。我们进行了一项经机构审查委员会（IRB）批准的为期7个月的研究，从2018年12月10日至2019年7月12日，在一所大型大学医院的四个公共候诊区进行了部署。在此期间，该平台收集并分析了来自医院候诊区的超过350,000张候诊室热成像图像和2100万份非语音音频样本。它能够以0.95的皮尔逊相关系数准确预测每日患者数量。我们还将该平台的信号与在同一机构获得的实验室确诊流感病例数的金标准数据进行了比较，发现基于传感器的特征与实验室确诊流感趋势密切相关。
