Portable AI device predicts outbreaks based on coughing

羽毛装置容纳这些部件。

Source: University of Massachusetts

Flusense Creators表示，新的优势计算平台，设想用于医院，医疗保健候诊室和更大的公共空间，可能会扩大用于预测季节性流感和其他病毒呼吸的健康监控工具的阿森纳outbreaks, such as the COVID-19 pandemic or SARS.

这样的模型可以通过直接通知流感期间的公共卫生反应来成为救生者epidemic. These data sources can help determine the timing for flu vaccine campaigns, potential travel restrictions, the allocation of medical supplies and more. “This may allow us to predict flu trends in a much more accurate manner,” says co-author Tauhidur Rahman, assistant professor of computer and information sciences, who advises Ph.D. student and lead author Forsad Al Hossain.

为了使他们的发明是一个真实世界的试验，美容发明者与大学卫生服务执行董事乔治库迪博士合作;生物统计学家Nicholas Reich，基于Umass的CDC流感预测卓越中心的主任;和流行病学家安德鲁·洛夫，传染媒介传统的疾病专家和公共卫生和健康科学学院的助理教授。

Flusense平台使用覆盆子PI和神经计算发动机来处理低成本的麦克风阵列和热成像数据。它不存储任何个人可识别的信息，例如语音数据或区分图像。在拉赫曼的马赛克实验室，计算机科学家发展sensorsto observe human health and behavior, the researchers first developed a lab-based cough model. Then they trained the deep neural network classifier to draw bounding boxes on thermal images representing people, and then to count them. “Our main goal was to build predictive models at the population level, not the individual level,” Rahman says.

They placed the FluSense devices, encased in a rectangular box about the size of a large dictionary, in four healthcare waiting rooms at UMass’s University Health Services clinic.

From December 2018 to July 2019, the FluSense platform collected and analyzed more than 350,000 thermal images and 21 million non-speech audio samples from the public waiting areas. The researchers found that FluSense was able to accurately predict daily illness rates at the university clinic. Multiple and complementary sets of FluSense signals “strongly correlated” withlaboratory-based testing for flu-like illnesses and influenza itself.

Tauhidur Ra​​hman，左手和Forsad Al Hossain显示了他们发明的卷义装置。

Source: University of Massachusetts

根据这项研究,“symptom-related早期information captured by FluSense could provide valuable additional and complementary information to current influenza prediction efforts,” such as the FluSight Network, which is a multidisciplinary consortium of flu forecasting teams, including the Reich Lab at UMass Amherst. “I’ve been interested in non-speech body sounds for a long time,” Rahman says. “I thought if we could capture coughing or sneezing sounds from public spaces where a lot of people naturally congregate, we could utilize this information as a new source of data for predicting epidemiologic trends.”

Al Hossain表示，Flusense是组合力量的一个例子人工智能使用Edge Computing，前端推送趋势使数据能​​够在数据的源上收集和分析。“我们正试图带来machine-learningsystems to the edge,” Al Hossain says, pointing to the compact components inside the FluSense device. “All of the processing happens right here. These systems are becoming cheaper and more powerful.”

The next step is to test FluSense in other public areas and geographic locations. “We have the initial validation that the coughing indeed has a correlation with influenza-related illness,” Lover says. “Now we want to validate it beyond this specific hospital setting and show that we can generalize across locations.”